Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

PubMed Central

Toews, Matthew; Wells, William M.

2013-01-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. PMID:23265799

Cluster compression algorithm: A joint clustering/data compression concept

NASA Technical Reports Server (NTRS)

Hilbert, E. E.

1977-01-01

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.

Deep features for efficient multi-biometric recognition with face and ear images

NASA Astrophysics Data System (ADS)

Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

2017-07-01

Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

A harmonic linear dynamical system for prominent ECG feature extraction.

PubMed

Thi, Ngoc Anh Nguyen; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc

2014-01-01

Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

Diabetic Rethinopathy Screening by Bright Lesions Extraction from Fundus Images

NASA Astrophysics Data System (ADS)

Hanđsková, Veronika; Pavlovičova, Jarmila; Oravec, Miloš; Blaško, Radoslav

2013-09-01

Retinal images are nowadays widely used to diagnose many diseases, for example diabetic retinopathy. In our work, we propose the algorithm for the screening application, which identifies the patients with such severe diabetic complication as diabetic retinopathy is, in early phase. In the application we use the patient's fundus photography without any additional examination by an ophtalmologist. After this screening identification, other examination methods should be considered and the patient's follow-up by a doctor is necessary. Our application is composed of three principal modules including fundus image preprocessing, feature extraction and feature classification. Image preprocessing module has the role of luminance normalization, contrast enhancement and optical disk masking. Feature extraction module includes two stages: bright lesions candidates localization and candidates feature extraction. We selected 16 statistical and structural features. For feature classification, we use multilayer perceptron (MLP) with one hidden layer. We classify images into two classes. Feature classification efficiency is about 93 percent.

A method for real-time implementation of HOG feature extraction

NASA Astrophysics Data System (ADS)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.

PubMed

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-09-13

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

On-Demand Single Photons with High Extraction Efficiency and Near-Unity Indistinguishability from a Resonantly Driven Quantum Dot in a Micropillar.

PubMed

Ding, Xing; He, Yu; Duan, Z-C; Gregersen, Niels; Chen, M-C; Unsleber, S; Maier, S; Schneider, Christian; Kamp, Martin; Höfling, Sven; Lu, Chao-Yang; Pan, Jian-Wei

2016-01-15

Scalable photonic quantum technologies require on-demand single-photon sources with simultaneously high levels of purity, indistinguishability, and efficiency. These key features, however, have only been demonstrated separately in previous experiments. Here, by s-shell pulsed resonant excitation of a Purcell-enhanced quantum dot-micropillar system, we deterministically generate resonance fluorescence single photons which, at π pulse excitation, have an extraction efficiency of 66%, single-photon purity of 99.1%, and photon indistinguishability of 98.5%. Such a single-photon source for the first time combines the features of high efficiency and near-perfect levels of purity and indistinguishabilty, and thus opens the way to multiphoton experiments with semiconductor quantum dots.

System and method for automated object detection in an image

DOEpatents

Kenyon, Garrett T.; Brumby, Steven P.; George, John S.; Paiton, Dylan M.; Schultz, Peter F.

2015-10-06

A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition.

PubMed

Zhang, Yifan; Gao, Xunzhang; Peng, Xuan; Ye, Jiaqi; Li, Xiang

2018-05-16

The High Resolution Range Profile (HRRP) recognition has attracted great concern in the field of Radar Automatic Target Recognition (RATR). However, traditional HRRP recognition methods failed to model high dimensional sequential data efficiently and have a poor anti-noise ability. To deal with these problems, a novel stochastic neural network model named Attention-based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed in this paper. RTRBM is utilized to extract discriminative features and the attention mechanism is adopted to select major features. RTRBM is efficient to model high dimensional HRRP sequences because it can extract the information of temporal and spatial correlation between adjacent HRRPs. The attention mechanism is used in sequential data recognition tasks including machine translation and relation classification, which makes the model pay more attention to the major features of recognition. Therefore, the combination of RTRBM and the attention mechanism makes our model effective for extracting more internal related features and choose the important parts of the extracted features. Additionally, the model performs well with the noise corrupted HRRP data. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model outperforms other traditional methods, which indicates that ARTRBM extracts, selects, and utilizes the correlation information between adjacent HRRPs effectively and is suitable for high dimensional data or noise corrupted data.

Finger vein recognition based on the hyperinformation feature

NASA Astrophysics Data System (ADS)

Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Yang, Lu

2014-01-01

The finger vein is a promising biometric pattern for personal identification due to its advantages over other existing biometrics. In finger vein recognition, feature extraction is a critical step, and many feature extraction methods have been proposed to extract the gray, texture, or shape of the finger vein. We treat them as low-level features and present a high-level feature extraction framework. Under this framework, base attribute is first defined to represent the characteristics of a certain subcategory of a subject. Then, for an image, the correlation coefficient is used for constructing the high-level feature, which reflects the correlation between this image and all base attributes. Since the high-level feature can reveal characteristics of more subcategories and contain more discriminative information, we call it hyperinformation feature (HIF). Compared with low-level features, which only represent the characteristics of one subcategory, HIF is more powerful and robust. In order to demonstrate the potential of the proposed framework, we provide a case study to extract HIF. We conduct comprehensive experiments to show the generality of the proposed framework and the efficiency of HIF on our databases, respectively. Experimental results show that HIF significantly outperforms the low-level features.

Building Facade Reconstruction by Fusing Terrestrial Laser Points and Images

PubMed Central

Pu, Shi; Vosselman, George

2009-01-01

Laser data and optical data have a complementary nature for three dimensional feature extraction. Efficient integration of the two data sources will lead to a more reliable and automated extraction of three dimensional features. This paper presents a semiautomatic building facade reconstruction approach, which efficiently combines information from terrestrial laser point clouds and close range images. A building facade's general structure is discovered and established using the planar features from laser data. Then strong lines in images are extracted using Canny extractor and Hough transformation, and compared with current model edges for necessary improvement. Finally, textures with optimal visibility are selected and applied according to accurate image orientations. Solutions to several challenge problems throughout the collaborated reconstruction, such as referencing between laser points and multiple images and automated texturing, are described. The limitations and remaining works of this approach are also discussed. PMID:22408539

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

2017-11-01

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

PubMed

Mohammed, Ameer; Zamani, Majid; Bayford, Richard; Demosthenous, Andreas

2017-12-01

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

PubMed Central

Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

2012-01-01

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yinggang; Zhang, Libao; Yu, Xianchuan

2015-01-01

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

PubMed Central

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-01-01

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features. PMID:27649171

Coding visual features extracted from video sequences.

PubMed

Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2014-05-01

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

Combined rule extraction and feature elimination in supervised classification.

PubMed

Liu, Sheng; Patel, Ronak Y; Daga, Pankaj R; Liu, Haining; Fu, Gang; Doerksen, Robert J; Chen, Yixin; Wilkins, Dawn E

2012-09-01

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.

Joint classification and contour extraction of large 3D point clouds

NASA Astrophysics Data System (ADS)

Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

2017-08-01

We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours. Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with >109 points.

Ensemble methods with simple features for document zone classification

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Xie, Bingqing

2012-01-01

Document layout analysis is of fundamental importance for document image understanding and information retrieval. It requires the identification of blocks extracted from a document image via features extraction and block classification. In this paper, we focus on the classification of the extracted blocks into five classes: text (machine printed), handwriting, graphics, images, and noise. We propose a new set of features for efficient classifications of these blocks. We present a comparative evaluation of three ensemble based classification algorithms (boosting, bagging, and combined model trees) in addition to other known learning algorithms. Experimental results are demonstrated for a set of 36503 zones extracted from 416 document images which were randomly selected from the tobacco legacy document collection. The results obtained verify the robustness and effectiveness of the proposed set of features in comparison to the commonly used Ocropus recognition features. When used in conjunction with the Ocropus feature set, we further improve the performance of the block classification system to obtain a classification accuracy of 99.21%.

A multiple maximum scatter difference discriminant criterion for facial feature extraction.

PubMed

Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei

2007-12-01

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.

Synthetic aperture radar target detection, feature extraction, and image formation techniques

NASA Technical Reports Server (NTRS)

Li, Jian

1994-01-01

This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

Age and gender estimation using Region-SIFT and multi-layered SVM

NASA Astrophysics Data System (ADS)

Kim, Hyunduk; Lee, Sang-Heon; Sohn, Myoung-Kyu; Hwang, Byunghun

2018-04-01

In this paper, we propose an age and gender estimation framework using the region-SIFT feature and multi-layered SVM classifier. The suggested framework entails three processes. The first step is landmark based face alignment. The second step is the feature extraction step. In this step, we introduce the region-SIFT feature extraction method based on facial landmarks. First, we define sub-regions of the face. We then extract SIFT features from each sub-region. In order to reduce the dimensions of features we employ a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). Finally, we classify age and gender using a multi-layered Support Vector Machines (SVM) for efficient classification. Rather than performing gender estimation and age estimation independently, the use of the multi-layered SVM can improve the classification rate by constructing a classifier that estimate the age according to gender. Moreover, we collect a dataset of face images, called by DGIST_C, from the internet. A performance evaluation of proposed method was performed with the FERET database, CACD database, and DGIST_C database. The experimental results demonstrate that the proposed approach classifies age and performs gender estimation very efficiently and accurately.

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

PubMed

Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

2017-09-22

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection.

PubMed

Park, Juyoung; Kang, Mingon; Gao, Jean; Kim, Younghoon; Kang, Kyungtae

2017-01-01

Detecting arrhythmia from ECG data is now feasible on mobile devices, but in this environment it is necessary to trade computational efficiency against accuracy. We propose an adaptive strategy for feature extraction that only considers normalized beat morphology features when running in a resource-constrained environment; but in a high-performance environment it takes account of a wider range of ECG features. This process is augmented by a cascaded random forest classifier. Experiments on data from the MIT-BIH Arrhythmia Database showed classification accuracies from 96.59% to 98.51%, which are comparable to state-of-the art methods.

Study on the traditional pattern retrieval method of minorities in Gansu province

NASA Astrophysics Data System (ADS)

Zheng, Gang; Wang, Beizhan; Sun, Yuchun; Xu, Jin

2018-03-01

The traditional patterns of ethnic minorities in gansu province are ethnic arts with strong ethnic characteristics. It is the crystallization of the hard work and wisdom of minority nationalities in gansu province. Unique traditional patterns of ethnic minorities in Gansu province with rich ethnic folk arts, is the crystallization of geographical environment in Gansu minority diligence and wisdom. By using the Surf feature point identification algorithm, the feature point extractor in OpenCV is used to extract the feature points. And the feature points are applied to compare the pattern features to find patterns similar to the artistic features. The application of this method can quickly or efficiently extract pattern information in a database.

Research and implementation of finger-vein recognition algorithm

NASA Astrophysics Data System (ADS)

Pang, Zengyao; Yang, Jie; Chen, Yilei; Liu, Yin

2017-06-01

In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.

Analysis of light extraction efficiency enhancement for thin-film-flip-chip InGaN quantum wells light-emitting diodes with GaN micro-domes.

PubMed

Zhao, Peng; Zhao, Hongping

2012-09-10

The enhancement of light extraction efficiency for thin-film flip-chip (TFFC) InGaN quantum wells (QWs) light-emitting diodes (LEDs) with GaN micro-domes on n-GaN layer was studied. The light extraction efficiency of TFFC InGaN QWs LEDs with GaN micro-domes were calculated and compared to that of the conventional TFFC InGaN QWs LEDs with flat surface. The three dimensional finite difference time domain (3D-FDTD) method was used to calculate the light extraction efficiency for the InGaN QWs LEDs emitting at 460nm and 550 nm, respectively. The effects of the GaN micro-dome feature size and the p-GaN layer thickness on the light extraction efficiency were studied systematically. Studies indicate that the p-GaN layer thickness is critical for optimizing the TFFC LED light extraction efficiency. Significant enhancement of the light extraction efficiency (2.5-2.7 times for λ(peak) = 460nm and 2.7-2.8 times for λ(peak) = 550nm) is achievable from TFFC InGaN QWs LEDs with optimized GaN micro-dome diameter and height.

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

Evaluation of a web based informatics system with data mining tools for predicting outcomes with quantitative imaging features in stroke rehabilitation clinical trials

NASA Astrophysics Data System (ADS)

Wang, Ximing; Kim, Bokkyu; Park, Ji Hoon; Wang, Erik; Forsyth, Sydney; Lim, Cody; Ravi, Ragini; Karibyan, Sarkis; Sanchez, Alexander; Liu, Brent

2017-03-01

Quantitative imaging biomarkers are used widely in clinical trials for tracking and evaluation of medical interventions. Previously, we have presented a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials. The system integrates imaging features extraction tools and a web-based statistical analysis tool. The tools include a generalized linear mixed model(GLMM) that can investigate potential significance and correlation based on features extracted from clinical data and quantitative biomarkers. The imaging features extraction tools allow the user to collect imaging features and the GLMM module allows the user to select clinical data and imaging features such as stroke lesion characteristics from the database as regressors and regressands. This paper discusses the application scenario and evaluation results of the system in a stroke rehabilitation clinical trial. The system was utilized to manage clinical data and extract imaging biomarkers including stroke lesion volume, location and ventricle/brain ratio. The GLMM module was validated and the efficiency of data analysis was also evaluated.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Wei, E-mail: wguo2@ncsu.edu; Kirste, Ronny; Bryan, Zachary

Enhanced light extraction efficiency was demonstrated on nanostructure patterned GaN and AlGaN/AlN Multiple-Quantum-Well (MQW) structures using mass production techniques including natural lithography and interference lithography with feature size as small as 100 nm. Periodic nanostructures showed higher light extraction efficiency and modified emission profile compared to non-periodic structures based on integral reflection and angular-resolved transmission measurement. Light extraction mechanism of macroscopic and microscopic nanopatterning is discussed, and the advantage of using periodic nanostructure patterning is provided. An enhanced photoluminescence emission intensity was observed on nanostructure patterned AlGaN/AlN MQW compared to as-grown structure, demonstrating a large-scale and mass-producible pathway to higher lightmore » extraction efficiency in deep-ultra-violet light-emitting diodes.« less

An integrated condition-monitoring method for a milling process using reduced decomposition features

NASA Astrophysics Data System (ADS)

Liu, Jie; Wu, Bo; Wang, Yan; Hu, Youmin

2017-08-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

Research on Optimal Observation Scale for Damaged Buildings after Earthquake Based on Optimal Feature Space

NASA Astrophysics Data System (ADS)

Chen, J.; Chen, W.; Dou, A.; Li, W.; Sun, Y.

2018-04-01

A new information extraction method of damaged buildings rooted in optimal feature space is put forward on the basis of the traditional object-oriented method. In this new method, ESP (estimate of scale parameter) tool is used to optimize the segmentation of image. Then the distance matrix and minimum separation distance of all kinds of surface features are calculated through sample selection to find the optimal feature space, which is finally applied to extract the image of damaged buildings after earthquake. The overall extraction accuracy reaches 83.1 %, the kappa coefficient 0.813. The new information extraction method greatly improves the extraction accuracy and efficiency, compared with the traditional object-oriented method, and owns a good promotional value in the information extraction of damaged buildings. In addition, the new method can be used for the information extraction of different-resolution images of damaged buildings after earthquake, then to seek the optimal observation scale of damaged buildings through accuracy evaluation. It is supposed that the optimal observation scale of damaged buildings is between 1 m and 1.2 m, which provides a reference for future information extraction of damaged buildings.

FATS: Feature Analysis for Time Series

NASA Astrophysics Data System (ADS)

Nun, Isadora; Protopapas, Pavlos; Sim, Brandon; Zhu, Ming; Dave, Rahul; Castro, Nicolas; Pichara, Karim

2017-11-01

FATS facilitates and standardizes feature extraction for time series data; it quickly and efficiently calculates a compilation of many existing light curve features. Users can characterize or analyze an astronomical photometric database, though this library is not necessarily restricted to the astronomical domain and can also be applied to any kind of time series data.

Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

PubMed

Ming, Yue; Wang, Guangchao; Fan, Chunxiao

2015-01-01

With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

An efficient scheme for automatic web pages categorization using the support vector machine

NASA Astrophysics Data System (ADS)

Bhalla, Vinod Kumar; Kumar, Neeraj

2016-07-01

In the past few years, with an evolution of the Internet and related technologies, the number of the Internet users grows exponentially. These users demand access to relevant web pages from the Internet within fraction of seconds. To achieve this goal, there is a requirement of an efficient categorization of web page contents. Manual categorization of these billions of web pages to achieve high accuracy is a challenging task. Most of the existing techniques reported in the literature are semi-automatic. Using these techniques, higher level of accuracy cannot be achieved. To achieve these goals, this paper proposes an automatic web pages categorization into the domain category. The proposed scheme is based on the identification of specific and relevant features of the web pages. In the proposed scheme, first extraction and evaluation of features are done followed by filtering the feature set for categorization of domain web pages. A feature extraction tool based on the HTML document object model of the web page is developed in the proposed scheme. Feature extraction and weight assignment are based on the collection of domain-specific keyword list developed by considering various domain pages. Moreover, the keyword list is reduced on the basis of ids of keywords in keyword list. Also, stemming of keywords and tag text is done to achieve a higher accuracy. An extensive feature set is generated to develop a robust classification technique. The proposed scheme was evaluated using a machine learning method in combination with feature extraction and statistical analysis using support vector machine kernel as the classification tool. The results obtained confirm the effectiveness of the proposed scheme in terms of its accuracy in different categories of web pages.

Comparison of Single and Multi-Scale Method for Leaf and Wood Points Classification from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Wei, Hongqiang; Zhou, Guiyun; Zhou, Junjie

2018-04-01

The classification of leaf and wood points is an essential preprocessing step for extracting inventory measurements and canopy characterization of trees from the terrestrial laser scanning (TLS) data. The geometry-based approach is one of the widely used classification method. In the geometry-based method, it is common practice to extract salient features at one single scale before the features are used for classification. It remains unclear how different scale(s) used affect the classification accuracy and efficiency. To assess the scale effect on the classification accuracy and efficiency, we extracted the single-scale and multi-scale salient features from the point clouds of two oak trees of different sizes and conducted the classification on leaf and wood. Our experimental results show that the balanced accuracy of the multi-scale method is higher than the average balanced accuracy of the single-scale method by about 10 % for both trees. The average speed-up ratio of single scale classifiers over multi-scale classifier for each tree is higher than 30.

Emotion recognition from EEG using higher order crossings.

PubMed

Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J

2010-03-01

Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion experiences, recurring affective states, time-dependent emotional trends).

Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences

ERIC Educational Resources Information Center

Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

2015-01-01

This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…

Objective grading of facial paralysis using Local Binary Patterns in video processing.

PubMed

He, Shu; Soraghan, John J; O'Reilly, Brian F

2008-01-01

This paper presents a novel framework for objective measurement of facial paralysis in biomedial videos. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the Local Binary Patterns (LBP) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of block schemes. A multi-resolution extension of uniform LBP is proposed to efficiently combine the micro-patterns and large-scale patterns into a feature vector, which increases the algorithmic robustness and reduces noise effects while still retaining computational simplicity. The symmetry of facial movements is measured by the Resistor-Average Distance (RAD) between LBP features extracted from the two sides of the face. Support Vector Machine (SVM) is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) Scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Prominent feature extraction for review analysis: an empirical study

NASA Astrophysics Data System (ADS)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Thermal feature extraction of servers in a datacenter using thermal image registration

NASA Astrophysics Data System (ADS)

Liu, Hang; Ran, Jian; Xie, Ting; Gao, Shan

2017-09-01

Thermal cameras provide fine-grained thermal information that enhances monitoring and enables automatic thermal management in large datacenters. Recent approaches employing mobile robots or thermal camera networks can already identify the physical locations of hot spots. Other distribution information used to optimize datacenter management can also be obtained automatically using pattern recognition technology. However, most of the features extracted from thermal images, such as shape and gradient, may be affected by changes in the position and direction of the thermal camera. This paper presents a method for extracting the thermal features of a hot spot or a server in a container datacenter. First, thermal and visual images are registered based on textural characteristics extracted from images acquired in datacenters. Then, the thermal distribution of each server is standardized. The features of a hot spot or server extracted from the standard distribution can reduce the impact of camera position and direction. The results of experiments show that image registration is efficient for aligning the corresponding visual and thermal images in the datacenter, and the standardization procedure reduces the impacts of camera position and direction on hot spot or server features.

Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

PubMed

Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

2018-06-02

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

Yarn-dyed fabric defect classification based on convolutional neural network

NASA Astrophysics Data System (ADS)

Jing, Junfeng; Dong, Amei; Li, Pengfei

2017-07-01

Considering that the manual inspection of the yarn-dyed fabric can be time consuming and less efficient, a convolutional neural network (CNN) solution based on the modified AlexNet structure for the classification of the yarn-dyed fabric defect is proposed. CNN has powerful ability of feature extraction and feature fusion which can simulate the learning mechanism of the human brain. In order to enhance computational efficiency and detection accuracy, the local response normalization (LRN) layers in AlexNet are replaced by the batch normalization (BN) layers. In the process of the network training, through several convolution operations, the characteristics of the image are extracted step by step, and the essential features of the image can be obtained from the edge features. And the max pooling layers, the dropout layers, the fully connected layers are also employed in the classification model to reduce the computation cost and acquire more precise features of fabric defect. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show the capability of defect classification via the modified Alexnet model and indicate its robustness.

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2011-11-24

Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Numerical analysis of light extraction enhancement of GaN-based thin-film flip-chip light-emitting diodes with high-refractive-index buckling nanostructures

NASA Astrophysics Data System (ADS)

Yue, Qing-Yang; Yang, Yang; Cheng, Zhen-Jia; Guo, Cheng-Shan

2018-06-01

In this work, the light extraction efficiency enhancement of GaN-based thin-film flip-chip (TFFC) light-emitting diodes (LEDs) with high-refractive-index (TiO2) buckling nanostructures was studied using the three-dimensional finite difference time domain method. Compared with 2-D photonic crystals, the buckling structures have the advantages of a random directionality and a broad distribution in periodicity, which can effectively extract the guided light propagating in all azimuthal directions over a wide spectrum. Numerical studies revealed that the light extraction efficiency of buckling-structured LEDs reaches 1.1 times that of triangular lattice photonic crystals. The effects of the buckling structure feature sizes and the thickness of the N-GaN layer on the light extraction efficiency for TFFC LEDs were also investigated systematically. With optimized structural parameters, a significant light extraction enhancement of about 2.6 times was achieved for TiO2 buckling-structured TFFC LEDs compared with planar LEDs.

Extracting and identifying concrete structural defects in GPR images

NASA Astrophysics Data System (ADS)

Ye, Qiling; Jiao, Liangbao; Liu, Chuanxin; Cao, Xuehong; Huston, Dryver; Xia, Tian

2018-03-01

Traditionally most GPR data interpretations are performed manually. With the advancement of computing technologies, how to automate GPR data interpretation to achieve high efficiency and accuracy has become an active research subject. In this paper, analytical characterizations of major defects in concrete structures, including delamination, air void and moisture in GPR images, are performed. In the study, the image features of different defects are compared. Algorithms are developed for defect feature extraction and identification. For validations, both simulation results and field test data are utilized.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs

PubMed Central

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2017-01-01

Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC) descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches. PMID:28771497

Efficient Spatio-Temporal Local Binary Patterns for Spontaneous Facial Micro-Expression Recognition

PubMed Central

Wang, Yandan; See, John; Phan, Raphael C.-W.; Oh, Yee-Hui

2015-01-01

Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datasets—SMIC and CASME II, both with baseline results released using the widely used dynamic texture descriptor LBP-TOP for feature extraction. Although LBP-TOP is popular and widely used, it is still not compact enough. In this paper, we draw further inspiration from the concept of LBP-TOP that considers three orthogonal planes by proposing two efficient approaches for feature extraction. The compact robust form described by the proposed LBP-Six Intersection Points (SIP) and a super-compact LBP-Three Mean Orthogonal Planes (MOP) not only preserves the essential patterns, but also reduces the redundancy that affects the discriminality of the encoded features. Through a comprehensive set of experiments, we demonstrate the strengths of our approaches in terms of recognition accuracy and efficiency. PMID:25993498

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

PubMed

Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng

2018-01-01

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Efficient moving target analysis for inverse synthetic aperture radar images via joint speeded-up robust features and regular moment

NASA Astrophysics Data System (ADS)

Yang, Hongxin; Su, Fulin

2018-01-01

We propose a moving target analysis algorithm using speeded-up robust features (SURF) and regular moment in inverse synthetic aperture radar (ISAR) image sequences. In our study, we first extract interest points from ISAR image sequences by SURF. Different from traditional feature point extraction methods, SURF-based feature points are invariant to scattering intensity, target rotation, and image size. Then, we employ a bilateral feature registering model to match these feature points. The feature registering scheme can not only search the isotropic feature points to link the image sequences but also reduce the error matching pairs. After that, the target centroid is detected by regular moment. Consequently, a cost function based on correlation coefficient is adopted to analyze the motion information. Experimental results based on simulated and real data validate the effectiveness and practicability of the proposed method.

Efficient iris texture analysis method based on Gabor ordinal measures

NASA Astrophysics Data System (ADS)

Tajouri, Imen; Aydi, Walid; Ghorbel, Ahmed; Masmoudi, Nouri

2017-07-01

With the remarkably increasing interest directed to the security dimension, the iris recognition process is considered to stand as one of the most versatile technique critically useful for the biometric identification and authentication process. This is mainly due to every individual's unique iris texture. A modestly conceived efficient approach relevant to the feature extraction process is proposed. In the first place, iris zigzag "collarette" is extracted from the rest of the image by means of the circular Hough transform, as it includes the most significant regions lying in the iris texture. In the second place, the linear Hough transform is used for the eyelids' detection purpose while the median filter is applied for the eyelashes' removal. Then, a special technique combining the richness of Gabor features and the compactness of ordinal measures is implemented for the feature extraction process, so that a discriminative feature representation for every individual can be achieved. Subsequently, the modified Hamming distance is used for the matching process. Indeed, the advanced procedure turns out to be reliable, as compared to some of the state-of-the-art approaches, with a recognition rate of 99.98%, 98.12%, and 95.02% on CASIAV1.0, CASIAV3.0, and IIT Delhi V1 iris databases, respectively.

Instantaneous Coastline Extraction from LIDAR Point Cloud and High Resolution Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Li, Y.; Zhoing, L.; Lai, Z.; Gan, Z.

2018-04-01

A new method was proposed for instantaneous waterline extraction in this paper, which combines point cloud geometry features and image spectral characteristics of the coastal zone. The proposed method consists of follow steps: Mean Shift algorithm is used to segment the coastal zone of high resolution remote sensing images into small regions containing semantic information;Region features are extracted by integrating LiDAR data and the surface area of the image; initial waterlines are extracted by α-shape algorithm; a region growing algorithm with is taking into coastline refinement, with a growth rule integrating the intensity and topography of LiDAR data; moothing the coastline. Experiments are conducted to demonstrate the efficiency of the proposed method.

Feature generation using genetic programming with application to fault classification.

PubMed

Guo, Hong; Jack, Lindsay B; Nandi, Asoke K

2005-02-01

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.

Linguistic feature analysis for protein interaction extraction

PubMed Central

2009-01-01

Background The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels. Results Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared. Conclusion Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches. PMID:19909518

Efficient simple sealed-off CO laser at room temperature

NASA Astrophysics Data System (ADS)

Peters, P. J. M.; Witteman, W. J.; Zuidema, R. J.

1980-07-01

The paper reports a simple sealed-off CW CO laser with gold electrodes. A constant long-life output power of more than 29 W/m and a maximum efficiency of 15% at room temperature are reported. No auxiliary features, such as a palladium hydrogen extraction tube, are necessary.

Mathematical morphology-based shape feature analysis for Chinese character recognition systems

NASA Astrophysics Data System (ADS)

Pai, Tun-Wen; Shyu, Keh-Hwa; Chen, Ling-Fan; Tai, Gwo-Chin

1995-04-01

This paper proposes an efficient technique of shape feature extraction based on the application of mathematical morphology theory. A new shape complexity index for preclassification of machine printed Chinese Character Recognition (CCR) is also proposed. For characters represented in different fonts/sizes or in a low resolution environment, a more stable local feature such as shape structure is preferred for character recognition. Morphological valley extraction filters are applied to extract the protrusive strokes from four sides of an input Chinese character. The number of extracted local strokes reflects the shape complexity of each side. These shape features of characters are encoded as corresponding shape complexity indices. Based on the shape complexity index, data base is able to be classified into 16 groups prior to recognition procedures. The performance of associating with shape feature analysis reclaims several characters from misrecognized character sets and results in an average of 3.3% improvement of recognition rate from an existing recognition system. In addition to enhance the recognition performance, the extracted stroke information can be further analyzed and classified its own stroke type. Therefore, the combination of extracted strokes from each side provides a means for data base clustering based on radical or subword components. It is one of the best solutions for recognizing high complexity characters such as Chinese characters which are divided into more than 200 different categories and consist more than 13,000 characters.

Research on improving image recognition robustness by combining multiple features with associative memory

NASA Astrophysics Data System (ADS)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Quantitative analysis of facial paralysis using local binary patterns in biomedical videos.

PubMed

He, Shu; Soraghan, John J; O'Reilly, Brian F; Xing, Dongshan

2009-07-01

Facial paralysis is the loss of voluntary muscle movement of one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents a novel framework for objective measurement of facial paralysis. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBPs) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of novel block processing schemes. A multiresolution extension of uniform LBP is proposed to efficiently combine the micropatterns and large-scale patterns into a feature vector. The symmetry of facial movements is measured by the resistor-average distance (RAD) between LBP features extracted from the two sides of the face. Support vector machine is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

PubMed

Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

2017-01-01

The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

Distant supervision for neural relation extraction integrated with word attention and property features.

PubMed

Qu, Jianfeng; Ouyang, Dantong; Hua, Wen; Ye, Yuxin; Li, Ximing

2018-04-01

Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model. First, we develop a word-level attention mechanism to distinguish the importance of each individual word in a sentence, increasing the attention weights for those critical words. Second, we investigate the semantic information from word embeddings of target entities, which can be developed as a supplementary feature for the extractor. Experimental results show that our model outperforms previous state-of-the-art baselines. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparing the role of shape and texture on staging hepatic fibrosis from medical imaging

NASA Astrophysics Data System (ADS)

Zhang, Xuejun; Louie, Ryan; Liu, Brent J.; Gao, Xin; Tan, Xiaomin; Qu, Xianghe; Long, Liling

2016-03-01

The purpose of this study is to investigate the role of shape and texture in the classification of hepatic fibrosis by selecting the optimal parameters for a better Computer-aided diagnosis (CAD) system. 10 surface shape features are extracted from a standardized profile of liver; while15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: normal or abnormal. The accurate rate value of all 10/15 types number of features is 66.83% by texture, while 85.74% by shape features, respectively. The irregularity of liver shape can demonstrate fibrotic grade efficiently and texture feature of CT image is not recommended to use with shape feature for interpretation of cirrhosis.

Fast Compressive Tracking.

PubMed

Zhang, Kaihua; Zhang, Lei; Yang, Ming-Hsuan

2014-10-01

It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from a multiscale image feature space with data-independent basis. The proposed appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is constructed to efficiently extract the features for the appearance model. We compress sample images of the foreground target and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. A coarse-to-fine search strategy is adopted to further reduce the computational complexity in the detection procedure. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art methods on challenging sequences in terms of efficiency, accuracy and robustness.

Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier

NASA Astrophysics Data System (ADS)

Rama Krishna, K.; Ramachandran, K. I.

2018-02-01

Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.

Efficient architecture for spike sorting in reconfigurable hardware.

PubMed

Hwang, Wen-Jyi; Lee, Wei-Hao; Lin, Shiow-Jyu; Lai, Sheng-Ying

2013-11-01

This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.

Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)

NASA Astrophysics Data System (ADS)

Iqtait, M.; Mohamad, F. S.; Mamat, M.

2018-03-01

Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture.

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

PubMed

Feng, Lichen; Li, Zunchao; Wang, Yuanfa

2018-02-01

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

PubMed

Ali, Aziah; Hussain, Aini; Wan Zaki, Wan Mimi Diyana

2017-07-01

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

A rapid extraction of landslide disaster information research based on GF-1 image

NASA Astrophysics Data System (ADS)

Wang, Sai; Xu, Suning; Peng, Ling; Wang, Zhiyi; Wang, Na

2015-08-01

In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 .Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.

PubMed

Chen, Huan-Yuan; Chen, Chih-Chang; Hwang, Wen-Jyi

2017-09-28

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

PubMed Central

Chen, Huan-Yuan; Chen, Chih-Chang

2017-01-01

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Wismüller, Axel

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.

Role of Gist and PHOG Features in Computer-Aided Diagnosis of Tuberculosis without Segmentation

PubMed Central

Chauhan, Arun; Chauhan, Devesh; Rout, Chittaranjan

2014-01-01

Purpose Effective diagnosis of tuberculosis (TB) relies on accurate interpretation of radiological patterns found in a chest radiograph (CXR). Lack of skilled radiologists and other resources, especially in developing countries, hinders its efficient diagnosis. Computer-aided diagnosis (CAD) methods provide second opinion to the radiologists for their findings and thereby assist in better diagnosis of cancer and other diseases including TB. However, existing CAD methods for TB are based on the extraction of textural features from manually or semi-automatically segmented CXRs. These methods are prone to errors and cannot be implemented in X-ray machines for automated classification. Methods Gabor, Gist, histogram of oriented gradients (HOG), and pyramid histogram of oriented gradients (PHOG) features extracted from the whole image can be implemented into existing X-ray machines to discriminate between TB and non-TB CXRs in an automated manner. Localized features were extracted for the above methods using various parameters, such as frequency range, blocks and region of interest. The performance of these features was evaluated against textural features. Two digital CXR image datasets (8-bit DA and 14-bit DB) were used for evaluating the performance of these features. Results Gist (accuracy 94.2% for DA, 86.0% for DB) and PHOG (accuracy 92.3% for DA, 92.0% for DB) features provided better results for both the datasets. These features were implemented to develop a MATLAB toolbox, TB-Xpredict, which is freely available for academic use at http://sourceforge.net/projects/tbxpredict/. This toolbox provides both automated training and prediction modules and does not require expertise in image processing for operation. Conclusion Since the features used in TB-Xpredict do not require segmentation, the toolbox can easily be implemented in X-ray machines. This toolbox can effectively be used for the mass screening of TB in high-burden areas with improved efficiency. PMID:25390291

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

PubMed

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

2018-01-25

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

NASA Astrophysics Data System (ADS)

Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Zhao, Haina; Li, Jun

2014-01-01

We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.

Unsupervised Feature Learning for Heart Sounds Classification Using Autoencoder

NASA Astrophysics Data System (ADS)

Hu, Wei; Lv, Jiancheng; Liu, Dongbo; Chen, Yao

2018-04-01

Cardiovascular disease seriously threatens the health of many people. It is usually diagnosed during cardiac auscultation, which is a fast and efficient method of cardiovascular disease diagnosis. In recent years, deep learning approach using unsupervised learning has made significant breakthroughs in many fields. However, to our knowledge, deep learning has not yet been used for heart sound classification. In this paper, we first use the average Shannon energy to extract the envelope of the heart sounds, then find the highest point of S1 to extract the cardiac cycle. We convert the time-domain signals of the cardiac cycle into spectrograms and apply principal component analysis whitening to reduce the dimensionality of the spectrogram. Finally, we apply a two-layer autoencoder to extract the features of the spectrogram. The experimental results demonstrate that the features from the autoencoder are suitable for heart sound classification.

Special object extraction from medieval books using superpixels and bag-of-features

NASA Astrophysics Data System (ADS)

Yang, Ying; Rushmeier, Holly

2017-01-01

We propose a method to extract special objects in images of medieval books, which generally represent, for example, figures and capital letters. Instead of working on the single-pixel level, we consider superpixels as the basic classification units for improved time efficiency. More specifically, we classify superpixels into different categories/objects by using a bag-of-features approach, where a superpixel category classifier is trained with the local features of the superpixels of the training images. With the trained classifier, we are able to assign the category labels to the superpixels of a historical document image under test. Finally, special objects can easily be identified and extracted after analyzing the categorization results. Experimental results demonstrate that, as compared to the state-of-the-art algorithms, our method provides comparable performance for some historical books but greatly outperforms them in terms of generality and computational time.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets.

PubMed

Li, Der-Chiang; Liu, Chiao-Wen; Hu, Susan C

2011-05-01

Medical data sets are usually small and have very high dimensionality. Too many attributes will make the analysis less efficient and will not necessarily increase accuracy, while too few data will decrease the modeling stability. Consequently, the main objective of this study is to extract the optimal subset of features to increase analytical performance when the data set is small. This paper proposes a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, this study applies principal component analysis (PCA) to extract the optimal subset of features. Finally, we use the transformed data with these optimal features as the input data for a learning tool, a support vector machine (SVM). Six medical data sets: Pima Indians' diabetes, Wisconsin diagnostic breast cancer, Parkinson disease, echocardiogram, BUPA liver disorders dataset, and bladder cancer cases in Taiwan, are employed to illustrate the approach presented in this paper. This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance than either PCA or kernel principal component analysis (KPCA) when the data set is small, and suggest creating new purpose-related information to improve the analysis performance. This paper has shown that feature extraction is important as a function of feature selection for efficient data analysis. When the data set is small, using the fuzzy-based transformation method presented in this work to increase the information available produces better results than the PCA and KPCA approaches. Copyright © 2011 Elsevier B.V. All rights reserved.

Chinese wine classification system based on micrograph using combination of shape and structure features

NASA Astrophysics Data System (ADS)

Wan, Yi

2011-06-01

Chinese wines can be classification or graded by the micrographs. Micrographs of Chinese wines show floccules, stick and granule of variant shape and size. Different wines have variant microstructure and micrographs, we study the classification of Chinese wines based on the micrographs. Shape and structure of wines' particles in microstructure is the most important feature for recognition and classification of wines. So we introduce a feature extraction method which can describe the structure and region shape of micrograph efficiently. First, the micrographs are enhanced using total variation denoising, and segmented using a modified Otsu's method based on the Rayleigh Distribution. Then features are extracted using proposed method in the paper based on area, perimeter and traditional shape feature. Eight kinds total 26 features are selected. Finally, Chinese wine classification system based on micrograph using combination of shape and structure features and BP neural network have been presented. We compare the recognition results for different choices of features (traditional shape features or proposed features). The experimental results show that the better classification rate have been achieved using the combinational features proposed in this paper.

An efficient visualization method for analyzing biometric data

NASA Astrophysics Data System (ADS)

Rahmes, Mark; McGonagle, Mike; Yates, J. Harlan; Henning, Ronda; Hackett, Jay

2013-05-01

We introduce a novel application for biometric data analysis. This technology can be used as part of a unique and systematic approach designed to augment existing processing chains. Our system provides image quality control and analysis capabilities. We show how analysis and efficient visualization are used as part of an automated process. The goal of this system is to provide a unified platform for the analysis of biometric images that reduce manual effort and increase the likelihood of a match being brought to an examiner's attention from either a manual or lights-out application. We discuss the functionality of FeatureSCOPE™ which provides an efficient tool for feature analysis and quality control of biometric extracted features. Biometric databases must be checked for accuracy for a large volume of data attributes. Our solution accelerates review of features by a factor of up to 100 times. Review of qualitative results and cost reduction is shown by using efficient parallel visual review for quality control. Our process automatically sorts and filters features for examination, and packs these into a condensed view. An analyst can then rapidly page through screens of features and flag and annotate outliers as necessary.

Group sparse multiview patch alignment framework with view consistency for image classification.

PubMed

Gui, Jie; Tao, Dacheng; Sun, Zhenan; Luo, Yong; You, Xinge; Tang, Yuan Yan

2014-07-01

No single feature can satisfactorily characterize the semantic concepts of an image. Multiview learning aims to unify different kinds of features to produce a consensual and efficient representation. This paper redefines part optimization in the patch alignment framework (PAF) and develops a group sparse multiview patch alignment framework (GSM-PAF). The new part optimization considers not only the complementary properties of different views, but also view consistency. In particular, view consistency models the correlations between all possible combinations of any two kinds of view. In contrast to conventional dimensionality reduction algorithms that perform feature extraction and feature selection independently, GSM-PAF enjoys joint feature extraction and feature selection by exploiting l(2,1)-norm on the projection matrix to achieve row sparsity, which leads to the simultaneous selection of relevant features and learning transformation, and thus makes the algorithm more discriminative. Experiments on two real-world image data sets demonstrate the effectiveness of GSM-PAF for image classification.

A novel framework for feature extraction in multi-sensor action potential sorting.

PubMed

Wu, Shun-Chi; Swindlehurst, A Lee; Nenadic, Zoran

2015-09-30

Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently. In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms. Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results. Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates. The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Improving the automated detection of refugee/IDP dwellings using the multispectral bands of the WorldView-2 satellite

NASA Astrophysics Data System (ADS)

Kemper, Thomas; Gueguen, Lionel; Soille, Pierre

2012-06-01

The enumeration of the population remains a critical task in the management of refugee/IDP camps. Analysis of very high spatial resolution satellite data proofed to be an efficient and secure approach for the estimation of dwellings and the monitoring of the camp over time. In this paper we propose a new methodology for the automated extraction of features based on differential morphological decomposition segmentation for feature extraction and interactive training sample selection from the max-tree and min-tree structures. This feature extraction methodology is tested on a WorldView-2 scene of an IDP camp in Darfur Sudan. Special emphasis is given to the additional available bands of the WorldView-2 sensor. The results obtained show that the interactive image information tool is performing very well by tuning the feature extraction to the local conditions. The analysis of different spectral subsets shows that it is possible to obtain good results already with an RGB combination, but by increasing the number of spectral bands the detection of dwellings becomes more accurate. Best results were obtained using all eight bands of WorldView-2 satellite.

Brain's tumor image processing using shearlet transform

NASA Astrophysics Data System (ADS)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

Harvesting Intelligence in Multimedia Social Tagging Systems

NASA Astrophysics Data System (ADS)

Giannakidou, Eirini; Kaklidou, Foteini; Chatzilari, Elisavet; Kompatsiaris, Ioannis; Vakali, Athena

As more people adopt tagging practices, social tagging systems tend to form rich knowledge repositories that enable the extraction of patterns reflecting the way content semantics is perceived by the web users. This is of particular importance, especially in the case of multimedia content, since the availability of such content in the web is very high and its efficient retrieval using textual annotations or content-based automatically extracted metadata still remains a challenge. It is argued that complementing multimedia analysis techniques with knowledge drawn from web social annotations may facilitate multimedia content management. This chapter focuses on analyzing tagging patterns and combining them with content feature extraction methods, generating, thus, intelligence from multimedia social tagging systems. Emphasis is placed on using all available "tracks" of knowledge, that is tag co-occurrence together with semantic relations among tags and low-level features of the content. Towards this direction, a survey on the theoretical background and the adopted practices for analysis of multimedia social content are presented. A case study from Flickr illustrates the efficiency of the proposed approach.

Optical devices featuring nonpolar textured semiconductor layers

DOEpatents

Moustakas, Theodore D; Moldawer, Adam; Bhattacharyya, Anirban; Abell, Joshua

2013-11-26

A semiconductor emitter, or precursor therefor, has a substrate and one or more textured semiconductor layers deposited onto the substrate in a nonpolar orientation. The textured layers enhance light extraction, and the use of nonpolar orientation greatly enhances internal quantum efficiency compared to conventional devices. Both the internal and external quantum efficiencies of emitters of the invention can be 70-80% or higher. The invention provides highly efficient light emitting diodes suitable for solid state lighting.

Integrating Dimension Reduction and Out-of-Sample Extension in Automated Classification of Ex Vivo Human Patellar Cartilage on Phase Contrast X-Ray Computed Tomography

PubMed Central

Nagarajan, Mahesh B.; Coan, Paola; Huber, Markus B.; Diemoz, Paul C.; Wismüller, Axel

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns. PMID:25710875

Oriented modulation for watermarking in direct binary search halftone images.

PubMed

Guo, Jing-Ming; Su, Chang-Cheng; Liu, Yun-Fu; Lee, Hua; Lee, Jiann-Der

2012-09-01

In this paper, a halftoning-based watermarking method is presented. This method enables high pixel-depth watermark embedding, while maintaining high image quality. This technique is capable of embedding watermarks with pixel depths up to 3 bits without causing prominent degradation to the image quality. To achieve high image quality, the parallel oriented high-efficient direct binary search (DBS) halftoning is selected to be integrated with the proposed orientation modulation (OM) method. The OM method utilizes different halftone texture orientations to carry different watermark data. In the decoder, the least-mean-square-trained filters are applied for feature extraction from watermarked images in the frequency domain, and the naïve Bayes classifier is used to analyze the extracted features and ultimately to decode the watermark data. Experimental results show that the DBS-based OM encoding method maintains a high degree of image quality and realizes the processing efficiency and robustness to be adapted in printing applications.

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Multiscale quantification of tissue spiculation and distortion for detection of architectural distortion and spiculated mass in mammography

NASA Astrophysics Data System (ADS)

Lao, Zhiqiang; Zheng, Xin

2011-03-01

This paper proposes a multiscale method to quantify tissue spiculation and distortion in mammography CAD systems that aims at improving the sensitivity in detecting architectural distortion and spiculated mass. This approach addresses the difficulty of predetermining the neighborhood size for feature extraction in characterizing lesions demonstrating spiculated mass/architectural distortion that may appear in different sizes. The quantification is based on the recognition of tissue spiculation and distortion pattern using multiscale first-order phase portrait model in texture orientation field generated by Gabor filter bank. A feature map is generated based on the multiscale quantification for each mammogram and two features are then extracted from the feature map. These two features will be combined with other mass features to provide enhanced discriminate ability in detecting lesions demonstrating spiculated mass and architectural distortion. The efficiency and efficacy of the proposed method are demonstrated with results obtained by applying the method to over 500 cancer cases and over 1000 normal cases.

Detection of protruding lesion in wireless capsule endoscopy videos of small intestine

NASA Astrophysics Data System (ADS)

Wang, Chengliang; Luo, Zhuo; Liu, Xiaoqi; Bai, Jianying; Liao, Guobin

2018-02-01

Wireless capsule endoscopy (WCE) is a developed revolutionary technology with important clinical benefits. But the huge image data brings a heavy burden to the doctors for locating and diagnosing the lesion images. In this paper, a novel and efficient approach is proposed to help clinicians to detect protruding lesion images in small intestine. First, since there are many possible disturbances such as air bubbles and so on in WCE video frames, which add the difficulty of efficient feature extraction, the color-saliency region detection (CSD) method is developed for extracting the potentially saliency region of interest (SROI). Second, a novel color channels modelling of local binary pattern operator (CCLBP) is proposed to describe WCE images, which combines grayscale and color angle. The CCLBP feature is more robust to variation of illumination and more discriminative for classification. Moreover, support vector machine (SVM) classifier with CCLBP feature is utilized to detect protruding lesion images. Experimental results on real WCE images demonstrate that proposed method has higher accuracy on protruding lesion detection than some art-of-state methods.

An Efficient Method for Automatic Road Extraction Based on Multiple Features from LiDAR Data

NASA Astrophysics Data System (ADS)

Li, Y.; Hu, X.; Guan, H.; Liu, P.

2016-06-01

The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for "Urban Classification and 3D Building Reconstruction" project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase.

PubMed

Asadabadi, Ebrahim Barzegari; Abdolmaleki, Parviz; Barkooie, Seyyed Mohsen Hosseini; Jahandideh, Samad; Rezaei, Mohammad Ali

2009-12-01

Regarding the great potential of dual binding site inhibitors of acetylcholinesterase as the future potent drugs of Alzheimer's disease, this study was devoted to extraction of the most effective structural features of these inhibitors from among a large number of quantitative descriptors. To do this, we adopted a unique approach in quantitative structure-activity relationships. An efficient feature selection method was emphasized in such an approach, using the confirmative results of different routine and novel feature selection methods. The proposed methods generated quite consistent results ensuring the effectiveness of the selected structural features.

Reliable Early Classification on Multivariate Time Series with Numerical and Categorical Attributes

DTIC Science & Technology

2015-05-22

design a procedure of feature extraction in REACT named MEG (Mining Equivalence classes with shapelet Generators) based on the concept of...Equivalence Classes Mining [12, 15]. MEG can efficiently and effectively generate the discriminative features. In addition, several strategies are proposed...technique of parallel computing [4] to propose a process of pa- rallel MEG for substantially reducing the computational overhead of discovering shapelet

Low-power hardware implementation of movement decoding for brain computer interface with reduced-resolution discrete cosine transform.

PubMed

Minho Won; Albalawi, Hassan; Xin Li; Thomas, Donald E

2014-01-01

This paper describes a low-power hardware implementation for movement decoding of brain computer interface. Our proposed hardware design is facilitated by two novel ideas: (i) an efficient feature extraction method based on reduced-resolution discrete cosine transform (DCT), and (ii) a new hardware architecture of dual look-up table to perform discrete cosine transform without explicit multiplication. The proposed hardware implementation has been validated for movement decoding of electrocorticography (ECoG) signal by using a Xilinx FPGA Zynq-7000 board. It achieves more than 56× energy reduction over a reference design using band-pass filters for feature extraction.

Analysis of breast thermograms using Gabor wavelet anisotropy index.

PubMed

Suganthi, S S; Ramakrishnan, S

2014-09-01

In this study, an attempt is made to distinguish the normal and abnormal tissues in breast thermal images using Gabor wavelet transform. Thermograms having normal, benign and malignant tissues are considered in this study and are obtained from public online database. Segmentation of breast tissues is performed by multiplying raw image and ground truth mask. Left and right breast regions are separated after removing the non-breast regions from the segmented image. Based on the pathological conditions, the separated breast regions are grouped as normal and abnormal tissues. Gabor features such as energy and amplitude in different scales and orientations are extracted. Anisotropy and orientation measures are calculated from the extracted features and analyzed. A distinctive variation is observed among different orientations of the extracted features. It is found that the anisotropy measure is capable of differentiating the structural changes due to varied metabolic conditions. Further, the Gabor features also showed relative variations among different pathological conditions. It appears that these features can be used efficiently to identify normal and abnormal tissues and hence, improve the relevance of breast thermography in early detection of breast cancer and content based image retrieval.

The effects of TIS and MI on the texture features in ultrasonic fatty liver images

NASA Astrophysics Data System (ADS)

Zhao, Yuan; Cheng, Xinyao; Ding, Mingyue

2017-03-01

Nonalcoholic fatty liver disease (NAFLD) is prevalent and has a worldwide distribution now. Although ultrasound imaging technology has been deemed as the common method to diagnose fatty liver, it is not able to detect NAFLD in its early stage and limited by the diagnostic instruments and some other factors. B-scan image feature extraction of fatty liver can assist doctor to analyze the patient's situation and enhance the efficiency and accuracy of clinical diagnoses. However, some uncertain factors in ultrasonic diagnoses are often been ignored during feature extraction. In this study, the nonalcoholic fatty liver rabbit model was made and its liver ultrasound images were collected by setting different Thermal index of soft tissue (TIS) and mechanical index (MI). Then, texture features were calculated based on gray level co-occurrence matrix (GLCM) and the impacts of TIS and MI on these features were analyzed and discussed. Furthermore, the receiver operating characteristic (ROC) curve was used to evaluate whether each feature was effective or not when TIS and MI were given. The results showed that TIS and MI do affect the features extracted from the healthy liver, while the texture features of fatty liver are relatively stable. In addition, TIS set to 0.3 and MI equal to 0.9 might be a better choice when using a computer aided diagnosis (CAD) method for fatty liver recognition.

Visual words for lip-reading

NASA Astrophysics Data System (ADS)

Hassanat, Ahmad B. A.; Jassim, Sabah

2010-04-01

In this paper, the automatic lip reading problem is investigated, and an innovative approach to providing solutions to this problem has been proposed. This new VSR approach is dependent on the signature of the word itself, which is obtained from a hybrid feature extraction method dependent on geometric, appearance, and image transform features. The proposed VSR approach is termed "visual words". The visual words approach consists of two main parts, 1) Feature extraction/selection, and 2) Visual speech feature recognition. After localizing face and lips, several visual features for the lips where extracted. Such as the height and width of the mouth, mutual information and the quality measurement between the DWT of the current ROI and the DWT of the previous ROI, the ratio of vertical to horizontal features taken from DWT of ROI, The ratio of vertical edges to horizontal edges of ROI, the appearance of the tongue and the appearance of teeth. Each spoken word is represented by 8 signals, one of each feature. Those signals maintain the dynamic of the spoken word, which contains a good portion of information. The system is then trained on these features using the KNN and DTW. This approach has been evaluated using a large database for different people, and large experiment sets. The evaluation has proved the visual words efficiency, and shown that the VSR is a speaker dependent problem.

Scorebox extraction from mobile sports videos using Support Vector Machines

NASA Astrophysics Data System (ADS)

Kim, Wonjun; Park, Jimin; Kim, Changick

2008-08-01

Scorebox plays an important role in understanding contents of sports videos. However, the tiny scorebox may give the small-display-viewers uncomfortable experience in grasping the game situation. In this paper, we propose a novel framework to extract the scorebox from sports video frames. We first extract candidates by using accumulated intensity and edge information after short learning period. Since there are various types of scoreboxes inserted in sports videos, multiple attributes need to be used for efficient extraction. Based on those attributes, the optimal information gain is computed and top three ranked attributes in terms of information gain are selected as a three-dimensional feature vector for Support Vector Machines (SVM) to distinguish the scorebox from other candidates, such as logos and advertisement boards. The proposed method is tested on various videos of sports games and experimental results show the efficiency and robustness of our proposed method.

Multi scales based sparse matrix spectral clustering image segmentation

NASA Astrophysics Data System (ADS)

Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

2018-04-01

In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

Object-Based Arctic Sea Ice Feature Extraction through High Spatial Resolution Aerial photos

NASA Astrophysics Data System (ADS)

Miao, X.; Xie, H.

2015-12-01

High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice features, such as melt ponds, submerged ice, water, ice/snow, and pressure ridges, is time-consuming and labor-intensive. An object-based classification algorithm is developed to automatically extract sea ice features efficiently from aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the MIZ near the Alaska coast. The algorithm includes four steps: (1) the image segmentation groups the neighboring pixels into objects based on the similarity of spectral and textural information; (2) the random forest classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice), shadow, and ice/snow; (3) the polygon neighbor analysis separates melt ponds and submerged ice based on spatial relationship; and (4) pressure ridge features are extracted from shadow based on local illumination geometry. The producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection, and shadow shows a user's accuracy of 88.9% and producer's accuracies of 91.4%. Finally, pond density, pond fraction, ice floes, mean ice concentration, average ridge height, ridge profile, and ridge frequency are extracted from batch processing of aerial photos, and their uncertainties are estimated.

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

PubMed Central

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-01-01

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

Autonomous celestial navigation based on Earth ultraviolet radiance and fast gradient statistic feature extraction

NASA Astrophysics Data System (ADS)

Lu, Shan; Zhang, Hanmo

2016-01-01

To meet the requirement of autonomous orbit determination, this paper proposes a fast curve fitting method based on earth ultraviolet features to obtain accurate earth vector direction, in order to achieve the high precision autonomous navigation. Firstly, combining the stable characters of earth ultraviolet radiance and the use of transmission model software of atmospheric radiation, the paper simulates earth ultraviolet radiation model on different time and chooses the proper observation band. Then the fast improved edge extracting method combined Sobel operator and local binary pattern (LBP) is utilized, which can both eliminate noises efficiently and extract earth ultraviolet limb features accurately. And earth's centroid locations on simulated images are estimated via the least square fitting method using part of the limb edges. Taken advantage of the estimated earth vector direction and earth distance, Extended Kalman Filter (EKF) is applied to realize the autonomous navigation finally. Experiment results indicate the proposed method can achieve a sub-pixel earth centroid location estimation and extremely enhance autonomous celestial navigation precision.

Remote measurement methods for 3-D modeling purposes using BAE Systems' Software

NASA Astrophysics Data System (ADS)

Walker, Stewart; Pietrzak, Arleta

2015-06-01

Efficient, accurate data collection from imagery is the key to an economical generation of useful geospatial products. Incremental developments of traditional geospatial data collection and the arrival of new image data sources cause new software packages to be created and existing ones to be adjusted to enable such data to be processed. In the past, BAE Systems' digital photogrammetric workstation, SOCET SET®, met fin de siècle expectations in data processing and feature extraction. Its successor, SOCET GXP®, addresses today's photogrammetric requirements and new data sources. SOCET GXP is an advanced workstation for mapping and photogrammetric tasks, with automated functionality for triangulation, Digital Elevation Model (DEM) extraction, orthorectification and mosaicking, feature extraction and creation of 3-D models with texturing. BAE Systems continues to add sensor models to accommodate new image sources, in response to customer demand. New capabilities added in the latest version of SOCET GXP facilitate modeling, visualization and analysis of 3-D features.

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

PubMed

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-08-13

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

Feature Selection in Order to Extract Multiple Sclerosis Lesions Automatically in 3D Brain Magnetic Resonance Images Using Combination of Support Vector Machine and Genetic Algorithm.

PubMed

Khotanlou, Hassan; Afrasiabi, Mahlagha

2012-10-01

This paper presents a new feature selection approach for automatically extracting multiple sclerosis (MS) lesions in three-dimensional (3D) magnetic resonance (MR) images. Presented method is applicable to different types of MS lesions. In this method, T1, T2, and fluid attenuated inversion recovery (FLAIR) images are firstly preprocessed. In the next phase, effective features to extract MS lesions are selected by using a genetic algorithm (GA). The fitness function of the GA is the Similarity Index (SI) of a support vector machine (SVM) classifier. The results obtained on different types of lesions have been evaluated by comparison with manual segmentations. This algorithm is evaluated on 15 real 3D MR images using several measures. As a result, the SI between MS regions determined by the proposed method and radiologists was 87% on average. Experiments and comparisons with other methods show the effectiveness and the efficiency of the proposed approach.

An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image

NASA Astrophysics Data System (ADS)

Liu, Hang; Xie, Ting; Ran, Jian; Gao, Shan

2017-10-01

It is essential for a data center to maintain server security and stability. Long-time overload operation or high room temperature may cause service disruption even a server crash, which would result in great economic loss for business. Currently, the methods to avoid server outages are monitoring and forecasting. Thermal camera can provide fine texture information for monitoring and intelligent thermal management in large data center. This paper presents an efficient method for server thermal fault monitoring and diagnosis based on infrared image. Initially thermal distribution of server is standardized and the interest regions of the image are segmented manually. Then the texture feature, Hu moments feature as well as modified entropy feature are extracted from the segmented regions. These characteristics are applied to analyze and classify thermal faults, and then make efficient energy-saving thermal management decisions such as job migration. For the larger feature space, the principal component analysis is employed to reduce the feature dimensions, and guarantee high processing speed without losing the fault feature information. Finally, different feature vectors are taken as input for SVM training, and do the thermal fault diagnosis after getting the optimized SVM classifier. This method supports suggestions for optimizing data center management, it can improve air conditioning efficiency and reduce the energy consumption of the data center. The experimental results show that the maximum detection accuracy is 81.5%.

[Road Extraction in Remote Sensing Images Based on Spectral and Edge Analysis].

PubMed

Zhao, Wen-zhi; Luo, Li-qun; Guo, Zhou; Yue, Jun; Yu, Xue-ying; Liu, Hui; Wei, Jing

2015-10-01

Roads are typically man-made objects in urban areas. Road extraction from high-resolution images has important applications for urban planning and transportation development. However, due to the confusion of spectral characteristic, it is difficult to distinguish roads from other objects by merely using traditional classification methods that mainly depend on spectral information. Edge is an important feature for the identification of linear objects (e. g. , roads). The distribution patterns of edges vary greatly among different objects. It is crucial to merge edge statistical information into spectral ones. In this study, a new method that combines spectral information and edge statistical features has been proposed. First, edge detection is conducted by using self-adaptive mean-shift algorithm on the panchromatic band, which can greatly reduce pseudo-edges and noise effects. Then, edge statistical features are obtained from the edge statistical model, which measures the length and angle distribution of edges. Finally, by integrating the spectral and edge statistical features, SVM algorithm is used to classify the image and roads are ultimately extracted. A series of experiments are conducted and the results show that the overall accuracy of proposed method is 93% comparing with only 78% overall accuracy of the traditional. The results demonstrate that the proposed method is efficient and valuable for road extraction, especially on high-resolution images.

A flower image retrieval method based on ROI feature.

PubMed

Hong, An-Xiang; Chen, Gang; Li, Jun-Li; Chi, Zhe-Ru; Zhang, Dan

2004-07-01

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Roychoudhury, Indranil; Balaban, Edward; Saxena, Abhinav

2010-01-01

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

FacetGist: Collective Extraction of Document Facets in Large Technical Corpora.

PubMed

Siddiqui, Tarique; Ren, Xiang; Parameswaran, Aditya; Han, Jiawei

2016-10-01

Given the large volume of technical documents available, it is crucial to automatically organize and categorize these documents to be able to understand and extract value from them. Towards this end, we introduce a new research problem called Facet Extraction. Given a collection of technical documents, the goal of Facet Extraction is to automatically label each document with a set of concepts for the key facets ( e.g. , application, technique, evaluation metrics, and dataset) that people may be interested in. Facet Extraction has numerous applications, including document summarization, literature search, patent search and business intelligence. The major challenge in performing Facet Extraction arises from multiple sources: concept extraction, concept to facet matching, and facet disambiguation. To tackle these challenges, we develop FacetGist, a framework for facet extraction. Facet Extraction involves constructing a graph-based heterogeneous network to capture information available across multiple local sentence-level features, as well as global context features. We then formulate a joint optimization problem, and propose an efficient algorithm for graph-based label propagation to estimate the facet of each concept mention. Experimental results on technical corpora from two domains demonstrate that Facet Extraction can lead to an improvement of over 25% in both precision and recall over competing schemes.

FacetGist: Collective Extraction of Document Facets in Large Technical Corpora

PubMed Central

Siddiqui, Tarique; Ren, Xiang; Parameswaran, Aditya; Han, Jiawei

2017-01-01

Given the large volume of technical documents available, it is crucial to automatically organize and categorize these documents to be able to understand and extract value from them. Towards this end, we introduce a new research problem called Facet Extraction. Given a collection of technical documents, the goal of Facet Extraction is to automatically label each document with a set of concepts for the key facets (e.g., application, technique, evaluation metrics, and dataset) that people may be interested in. Facet Extraction has numerous applications, including document summarization, literature search, patent search and business intelligence. The major challenge in performing Facet Extraction arises from multiple sources: concept extraction, concept to facet matching, and facet disambiguation. To tackle these challenges, we develop FacetGist, a framework for facet extraction. Facet Extraction involves constructing a graph-based heterogeneous network to capture information available across multiple local sentence-level features, as well as global context features. We then formulate a joint optimization problem, and propose an efficient algorithm for graph-based label propagation to estimate the facet of each concept mention. Experimental results on technical corpora from two domains demonstrate that Facet Extraction can lead to an improvement of over 25% in both precision and recall over competing schemes. PMID:28210517

Parametric Time-Frequency Analysis and Its Applications in Music Classification

NASA Astrophysics Data System (ADS)

Shen, Ying; Li, Xiaoli; Ma, Ngok-Wah; Krishnan, Sridhar

2010-12-01

Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.

Effects of band selection on endmember extraction for forestry applications

NASA Astrophysics Data System (ADS)

Karathanassi, Vassilia; Andreou, Charoula; Andronis, Vassilis; Kolokoussis, Polychronis

2014-10-01

In spectral unmixing theory, data reduction techniques play an important role as hyperspectral imagery contains an immense amount of data, posing many challenging problems such as data storage, computational efficiency, and the so called "curse of dimensionality". Feature extraction and feature selection are the two main approaches for dimensionality reduction. Feature extraction techniques are used for reducing the dimensionality of the hyperspectral data by applying transforms on hyperspectral data. Feature selection techniques retain the physical meaning of the data by selecting a set of bands from the input hyperspectral dataset, which mainly contain the information needed for spectral unmixing. Although feature selection techniques are well-known for their dimensionality reduction potentials they are rarely used in the unmixing process. The majority of the existing state-of-the-art dimensionality reduction methods set criteria to the spectral information, which is derived by the whole wavelength, in order to define the optimum spectral subspace. These criteria are not associated with any particular application but with the data statistics, such as correlation and entropy values. However, each application is associated with specific land c over materials, whose spectral characteristics present variations in specific wavelengths. In forestry for example, many applications focus on tree leaves, in which specific pigments such as chlorophyll, xanthophyll, etc. determine the wavelengths where tree species, diseases, etc., can be detected. For such applications, when the unmixing process is applied, the tree species, diseases, etc., are considered as the endmembers of interest. This paper focuses on investigating the effects of band selection on the endmember extraction by exploiting the information of the vegetation absorbance spectral zones. More precisely, it is explored whether endmember extraction can be optimized when specific sets of initial bands related to leaf spectral characteristics are selected. Experiments comprise application of well-known signal subspace estimation and endmember extraction methods on a hyperspectral imagery that presents a forest area. Evaluation of the extracted endmembers showed that more forest species can be extracted as endmembers using selected bands.

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement

PubMed Central

Hao, Yansong; Song, Liuyang; Tang, Gang; Yuan, Hongfang

2018-01-01

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency. PMID:29597280

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement.

PubMed

Ren, Bangyue; Hao, Yansong; Wang, Huaqing; Song, Liuyang; Tang, Gang; Yuan, Hongfang

2018-03-28

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency.

The Speech multi features fusion perceptual hash algorithm based on tensor decomposition

NASA Astrophysics Data System (ADS)

Huang, Y. B.; Fan, M. H.; Zhang, Q. Y.

2018-03-01

With constant progress in modern speech communication technologies, the speech data is prone to be attacked by the noise or maliciously tampered. In order to make the speech perception hash algorithm has strong robustness and high efficiency, this paper put forward a speech perception hash algorithm based on the tensor decomposition and multi features is proposed. This algorithm analyses the speech perception feature acquires each speech component wavelet packet decomposition. LPCC, LSP and ISP feature of each speech component are extracted to constitute the speech feature tensor. Speech authentication is done by generating the hash values through feature matrix quantification which use mid-value. Experimental results showing that the proposed algorithm is robust for content to maintain operations compared with similar algorithms. It is able to resist the attack of the common background noise. Also, the algorithm is highly efficiency in terms of arithmetic, and is able to meet the real-time requirements of speech communication and complete the speech authentication quickly.

Feasibility of feature-based indexing, clustering, and search of clinical trials: A case study of breast cancer trials from ClinicalTrials.gov

PubMed Central

Boland, Mary Regina; Miotto, Riccardo; Gao, Junfeng; Weng, Chunhua

2013-01-01

Summary Background When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space. Objectives This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes. Methods We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively. Results We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction. Conclusions It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency. PMID:23666475

Feasibility of feature-based indexing, clustering, and search of clinical trials. A case study of breast cancer trials from ClinicalTrials.gov.

PubMed

Boland, M R; Miotto, R; Gao, J; Weng, C

2013-01-01

When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space. This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes. We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively. We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction. It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency.

Vaccine adverse event text mining system for extracting features from vaccine safety reports.

PubMed

Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

2012-01-01

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

Computationally Efficient Clustering of Audio-Visual Meeting Data

NASA Astrophysics Data System (ADS)

Hung, Hayley; Friedland, Gerald; Yeo, Chuohao

This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and visual events related to each of the participants in a group discussion using the example of business meeting recordings. The recording setup involves relatively few audio-visual sensors, comprising a limited number of cameras and microphones. We first demonstrate computationally efficient algorithms that can identify who spoke and when, a problem in speech processing known as speaker diarization. We also extract visual activity features efficiently from MPEG4 video by taking advantage of the processing that was already done for video compression. Then, we present a method of associating the audio-visual data together so that the content of each participant can be managed individually. The methods presented in this article can be used as a principal component that enables many higher-level semantic analysis tasks needed in search, retrieval, and navigation.

ELiXIR—Solid-State Luminaire With Enhanced Light Extraction by Internal Reflection

NASA Astrophysics Data System (ADS)

Allen, Steven C.; Steckl, Andrew J.

2007-06-01

A phosphor-converted light-emitting diode (pcLED) luminaire featuring enhanced light extraction by internal reflection (ELiXIR) with efficacy of 60 lm/W producing 18 lumens of yellowish green light at 100 mA is presented. The luminaire consists of a commercial blue high power LED, a polymer hemispherical shell lens with interior phosphor coating, and planar aluminized reflector. High extraction efficiency of the phosphor-converted light is achieved by separating the phosphor from the LED and using internal reflection to steer the light away from lossy reflectors and the LED package and out of the device. At 10 and 500 mA, the luminaire produces 2.1 and 66 lumens with efficacies of 80 and 37 lm/W, respectively. Technological improvements over existing commercial LEDs, such as more efficient pcLED packages or, alternatively, higher efficiency green or yellow for color mixing, will be essential to achieving 150 200 lm/W solid-state lighting. Advances in both areas are demonstrated.

Automatic Target Recognition Based on Cross-Plot

PubMed Central

Wong, Kelvin Kian Loong; Abbott, Derek

2011-01-01

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

Real time groove characterization combining partial least squares and SVR strategies: application to eddy current testing

NASA Astrophysics Data System (ADS)

Ahmed, S.; Salucci, M.; Miorelli, R.; Anselmi, N.; Oliveri, G.; Calmon, P.; Reboud, C.; Massa, A.

2017-10-01

A quasi real-time inversion strategy is presented for groove characterization of a conductive non-ferromagnetic tube structure by exploiting eddy current testing (ECT) signal. Inversion problem has been formulated by non-iterative Learning-by-Examples (LBE) strategy. Within the framework of LBE, an efficient training strategy has been adopted with the combination of feature extraction and a customized version of output space filling (OSF) adaptive sampling in order to get optimal training set during offline phase. Partial Least Squares (PLS) and Support Vector Regression (SVR) have been exploited for feature extraction and prediction technique respectively to have robust and accurate real time inversion during online phase.

Latest Progress of Fault Detection and Localization in Complex Electrical Engineering

NASA Astrophysics Data System (ADS)

Zhao, Zheng; Wang, Can; Zhang, Yagang; Sun, Yi

2014-01-01

In the researches of complex electrical engineering, efficient fault detection and localization schemes are essential to quickly detect and locate faults so that appropriate and timely corrective mitigating and maintenance actions can be taken. In this paper, under the current measurement precision of PMU, we will put forward a new type of fault detection and localization technology based on fault factor feature extraction. Lots of simulating experiments indicate that, although there are disturbances of white Gaussian stochastic noise, based on fault factor feature extraction principal, the fault detection and localization results are still accurate and reliable, which also identifies that the fault detection and localization technology has strong anti-interference ability and great redundancy.

Marker Registration Technique for Handwritten Text Marker in Augmented Reality Applications

NASA Astrophysics Data System (ADS)

Thanaborvornwiwat, N.; Patanukhom, K.

2018-04-01

Marker registration is a fundamental process to estimate camera poses in marker-based Augmented Reality (AR) systems. We developed AR system that creates correspondence virtual objects on handwritten text markers. This paper presents a new method for registration that is robust for low-content text markers, variation of camera poses, and variation of handwritten styles. The proposed method uses Maximally Stable Extremal Regions (MSER) and polygon simplification for a feature point extraction. The experiment shows that we need to extract only five feature points per image which can provide the best registration results. An exhaustive search is used to find the best matching pattern of the feature points in two images. We also compared performance of the proposed method to some existing registration methods and found that the proposed method can provide better accuracy and time efficiency.

Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection.

PubMed

Hu, Weiming; Fan, Yabo; Xing, Junliang; Sun, Liang; Cai, Zhaoquan; Maybank, Stephen

2018-09-01

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

Continuous nucleus extraction by optically-induced cell lysis on a batch-type microfluidic platform.

PubMed

Huang, Shih-Hsuan; Hung, Lien-Yu; Lee, Gwo-Bin

2016-04-21

The extraction of a cell's nucleus is an essential technique required for a number of procedures, such as disease diagnosis, genetic replication, and animal cloning. However, existing nucleus extraction techniques are relatively inefficient and labor-intensive. Therefore, this study presents an innovative, microfluidics-based approach featuring optically-induced cell lysis (OICL) for nucleus extraction and collection in an automatic format. In comparison to previous micro-devices designed for nucleus extraction, the new OICL device designed herein is superior in terms of flexibility, selectivity, and efficiency. To facilitate this OICL module for continuous nucleus extraction, we further integrated an optically-induced dielectrophoresis (ODEP) module with the OICL device within the microfluidic chip. This on-chip integration circumvents the need for highly trained personnel and expensive, cumbersome equipment. Specifically, this microfluidic system automates four steps by 1) automatically focusing and transporting cells, 2) releasing the nuclei on the OICL module, 3) isolating the nuclei on the ODEP module, and 4) collecting the nuclei in the outlet chamber. The efficiency of cell membrane lysis and the ODEP nucleus separation was measured to be 78.04 ± 5.70% and 80.90 ± 5.98%, respectively, leading to an overall nucleus extraction efficiency of 58.21 ± 2.21%. These results demonstrate that this microfluidics-based system can successfully perform nucleus extraction, and the integrated platform is therefore promising in cell fusion technology with the goal of achieving genetic replication, or even animal cloning, in the near future.

Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU: A Case Study from Microscopy Image Analysis

PubMed Central

Teodoro, George; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel

2014-01-01

We study and characterize the performance of operations in an important class of applications on GPUs and Many Integrated Core (MIC) architectures. Our work is motivated by applications that analyze low-dimensional spatial datasets captured by high resolution sensors, such as image datasets obtained from whole slide tissue specimens using microscopy scanners. Common operations in these applications involve the detection and extraction of objects (object segmentation), the computation of features of each extracted object (feature computation), and characterization of objects based on these features (object classification). In this work, we have identify the data access and computation patterns of operations in the object segmentation and feature computation categories. We systematically implement and evaluate the performance of these operations on modern CPUs, GPUs, and MIC systems for a microscopy image analysis application. Our results show that the performance on a MIC of operations that perform regular data access is comparable or sometimes better than that on a GPU. On the other hand, GPUs are significantly more efficient than MICs for operations that access data irregularly. This is a result of the low performance of MICs when it comes to random data access. We also have examined the coordinated use of MICs and CPUs. Our experiments show that using a performance aware task strategy for scheduling application operations improves performance about 1.29× over a first-come-first-served strategy. This allows applications to obtain high performance efficiency on CPU-MIC systems - the example application attained an efficiency of 84% on 192 nodes (3072 CPU cores and 192 MICs). PMID:25419088

Filter bank common spatial patterns in mental workload estimation.

PubMed

Arvaneh, Mahnaz; Umilta, Alberto; Robertson, Ian H

2015-01-01

EEG-based workload estimation technology provides a real time means of assessing mental workload. Such technology can effectively enhance the performance of the human-machine interaction and the learning process. When designing workload estimation algorithms, a crucial signal processing component is the feature extraction step. Despite several studies on this field, the spatial properties of the EEG signals were mostly neglected. Since EEG inherently has a poor spacial resolution, features extracted individually from each EEG channel may not be sufficiently efficient. This problem becomes more pronounced when we use low-cost but convenient EEG sensors with limited stability which is the case in practical scenarios. To address this issue, in this paper, we introduce a filter bank common spatial patterns algorithm combined with a feature selection method to extract spatio-spectral features discriminating different mental workload levels. To evaluate the proposed algorithm, we carry out a comparative analysis between two representative types of working memory tasks using data recorded from an Emotiv EPOC headset which is a mobile low-cost EEG recording device. The experimental results showed that the proposed spatial filtering algorithm outperformed the state-of-the algorithms in terms of the classification accuracy.

Semi-Supervised Geographical Feature Detection

NASA Astrophysics Data System (ADS)

Yu, H.; Yu, L.; Kuo, K. S.

2016-12-01

Extraction and tracking geographical features is a fundamental requirement in many geoscience fields. However, this operation has become an increasingly challenging task for domain scientists when tackling a large amount of geoscience data. Although domain scientists may have a relatively clear definition of features, it is difficult to capture the presence of features in an accurate and efficient fashion. We propose a semi-supervised approach to address large geographical feature detection. Our approach has two main components. First, we represent a heterogeneous geoscience data in a unified high-dimensional space, which can facilitate us to evaluate the similarity of data points with respect to geolocation, time, and variable values. We characterize the data from these measures, and use a set of hash functions to parameterize the initial knowledge of the data. Second, for any user query, our approach can automatically extract the initial results based on the hash functions. To improve the accuracy of querying, our approach provides a visualization interface to display the querying results and allow users to interactively explore and refine them. The user feedback will be used to enhance our knowledge base in an iterative manner. In our implementation, we use high-performance computing techniques to accelerate the construction of hash functions. Our design facilitates a parallelization scheme for feature detection and extraction, which is a traditionally challenging problem for large-scale data. We evaluate our approach and demonstrate the effectiveness using both synthetic and real world datasets.

Versatile and efficient pore network extraction method using marker-based watershed segmentation

NASA Astrophysics Data System (ADS)

Gostick, Jeff T.

2017-08-01

Obtaining structural information from tomographic images of porous materials is a critical component of porous media research. Extracting pore networks is particularly valuable since it enables pore network modeling simulations which can be useful for a host of tasks from predicting transport properties to simulating performance of entire devices. This work reports an efficient algorithm for extracting networks using only standard image analysis techniques. The algorithm was applied to several standard porous materials ranging from sandstone to fibrous mats, and in all cases agreed very well with established or known values for pore and throat sizes, capillary pressure curves, and permeability. In the case of sandstone, the present algorithm was compared to the network obtained using the current state-of-the-art algorithm, and very good agreement was achieved. Most importantly, the network extracted from an image of fibrous media correctly predicted the anisotropic permeability tensor, demonstrating the critical ability to detect key structural features. The highly efficient algorithm allows extraction on fairly large images of 5003 voxels in just over 200 s. The ability for one algorithm to match materials as varied as sandstone with 20% porosity and fibrous media with 75% porosity is a significant advancement. The source code for this algorithm is provided.

Cross-Domain Multi-View Object Retrieval via Multi-Scale Topic Models.

PubMed

Hong, Richang; Hu, Zhenzhen; Wang, Ruxin; Wang, Meng; Tao, Dacheng

2016-09-27

The increasing number of 3D objects in various applications has increased the requirement for effective and efficient 3D object retrieval methods, which attracted extensive research efforts in recent years. Existing works mainly focus on how to extract features and conduct object matching. With the increasing applications, 3D objects come from different areas. In such circumstances, how to conduct object retrieval becomes more important. To address this issue, we propose a multi-view object retrieval method using multi-scale topic models in this paper. In our method, multiple views are first extracted from each object, and then the dense visual features are extracted to represent each view. To represent the 3D object, multi-scale topic models are employed to extract the hidden relationship among these features with respected to varied topic numbers in the topic model. In this way, each object can be represented by a set of bag of topics. To compare the objects, we first conduct topic clustering for the basic topics from two datasets, and then generate the common topic dictionary for new representation. Then, the two objects can be aligned to the same common feature space for comparison. To evaluate the performance of the proposed method, experiments are conducted on two datasets. The 3D object retrieval experimental results and comparison with existing methods demonstrate the effectiveness of the proposed method.

Neuroanatomic organization of sound memory in humans.

PubMed

Kraut, Michael A; Pitcock, Jeffery A; Calhoun, Vince; Li, Juan; Freeman, Thomas; Hart, John

2006-11-01

The neural interface between sensory perception and memory is a central issue in neuroscience, particularly initial memory organization following perceptual analyses. We used functional magnetic resonance imaging to identify anatomic regions extracting initial auditory semantic memory information related to environmental sounds. Two distinct anatomic foci were detected in the right superior temporal gyrus when subjects identified sounds representing either animals or threatening items. Threatening animal stimuli elicited signal changes in both foci, suggesting a distributed neural representation. Our results demonstrate both category- and feature-specific responses to nonverbal sounds in early stages of extracting semantic memory information from these sounds. This organization allows for these category-feature detection nodes to extract early, semantic memory information for efficient processing of transient sound stimuli. Neural regions selective for threatening sounds are similar to those of nonhuman primates, demonstrating semantic memory organization for basic biological/survival primitives are present across species.

Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

NASA Astrophysics Data System (ADS)

Agarwal, Smriti; Singh, Dharmendra

2016-04-01

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

PubMed Central

Ye, Qing; Pan, Hao; Liu, Changhua

2015-01-01

This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

Yarn-dyed fabric defect classification based on convolutional neural network

NASA Astrophysics Data System (ADS)

Jing, Junfeng; Dong, Amei; Li, Pengfei; Zhang, Kaibing

2017-09-01

Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

PubMed

Yu, Sheng; Liao, Katherine P; Shaw, Stanley Y; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Cai, Tianxi

2015-09-01

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Generalized spin filtering and an improved derivative-sign binary image method for the extraction of fringe skeletons

NASA Astrophysics Data System (ADS)

Yu, Qifeng; Liu, Xiaolin; Sun, Xiangyi

1998-07-01

Generalized spin filters, including several directional filters such as the directional median filter and the directional binary filter, are proposed for removal of the noise of fringe patterns and the extraction of fringe skeletons with the help of fringe-orientation maps (FOM s). The generalized spin filters can filter off noise on fringe patterns and binary fringe patterns efficiently, without distortion of fringe features. A quadrantal angle filter is developed to filter off the FOM. With these new filters, the derivative-sign binary image (DSBI) method for extraction of fringe skeletons is improved considerably. The improved DSBI method can extract high-density skeletons as well as common density skeletons.

A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

New auto-segment method of cerebral hemorrhage

NASA Astrophysics Data System (ADS)

Wang, Weijiang; Shen, Tingzhi; Dang, Hua

2007-12-01

A novel method for Computerized tomography (CT) cerebral hemorrhage (CH) image automatic segmentation is presented in the paper, which uses expert system that models human knowledge about the CH automatic segmentation problem. The algorithm adopts a series of special steps and extracts some easy ignored CH features which can be found by statistic results of mass real CH images, such as region area, region CT number, region smoothness and some statistic CH region relationship. And a seven steps' extracting mechanism will ensure these CH features can be got correctly and efficiently. By using these CH features, a decision tree which models the human knowledge about the CH automatic segmentation problem has been built and it will ensure the rationality and accuracy of the algorithm. Finally some experiments has been taken to verify the correctness and reasonable of the automatic segmentation, and the good correct ratio and fast speed make it possible to be widely applied into practice.

Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

PubMed Central

Addeh, Jalil; Ebrahimzadeh, Ata

2012-01-01

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy. PMID:23626945

An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

NASA Astrophysics Data System (ADS)

Jiang, Li; Shi, Tielin; Xuan, Jianping

2012-05-01

Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

Coding Local and Global Binary Visual Features Extracted From Video Sequences.

PubMed

Baroffio, Luca; Canclini, Antonio; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2015-11-01

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the bag-of-visual word model. Several applications, including, for example, visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget while attaining a target level of efficiency. In this paper, we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can conveniently be adopted to support the analyze-then-compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs the visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the compress-then-analyze (CTA) paradigm. In this paper, we experimentally compare the ATC and the CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: 1) homography estimation and 2) content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with the CTA, especially in bandwidth limited scenarios.

Coding Local and Global Binary Visual Features Extracted From Video Sequences

NASA Astrophysics Data System (ADS)

Baroffio, Luca; Canclini, Antonio; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2015-11-01

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks, while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW) model. Several applications, including for example visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget, while attaining a target level of efficiency. In this paper we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the Compress-Then-Analyze (CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: homography estimation and content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with CTA, especially in bandwidth limited scenarios.

Device reflectivity as a simple rule for predicting the suitability of scattering foils for improved OLED light extraction

NASA Astrophysics Data System (ADS)

Levell, Jack W.; Harkema, Stephan; Pendyala, Raghu K.; Rensing, Peter A.; Senes, Alessia; Bollen, Dirk; MacKerron, Duncan; Wilson, Joanne S.

2013-09-01

A general challenge in Organic Light Emitting Diodes (OLEDs) is to extract the light efficiently from waveguided modes within the device structure. This can be accomplished by applying an additional scattering layer to the substrate which results in outcoupling increases between 0% to <100% in external quantum efficiency. In this work, we aim to address this large variation and show that the reflectivity of the OLED is a simple and useful predictor of the efficiency of substrate scattering techniques without the need for detailed modeling. We show that by optimizing the cathode and anode structure of glass based OLEDs by using silver and an ITO free high conductive Agfa Orgacon™ PEDOT:PSS we are able to increase the external quantum efficiency of OLEDs with the same outcoupling substrates from 2.4% to 5.6%, an increase of 130%. In addition, Holst Centre and partners are developing flexible substrates with integrated light extraction features and roll to roll compatible processing techniques to enable this next step in OLED development both for lighting and display applications. These devices show promise as they are shatterproof substrates and facilitate low cost manufacture.

Orientation Modeling for Amateur Cameras by Matching Image Line Features and Building Vector Data

NASA Astrophysics Data System (ADS)

Hung, C. H.; Chang, W. C.; Chen, L. C.

2016-06-01

With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier.

PubMed

Barbosa, Jocelyn; Lee, Kyubum; Lee, Sunwon; Lodhi, Bilal; Cho, Jae-Gu; Seo, Woo-Keun; Kang, Jaewoo

2016-03-12

Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician's judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman's algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features' segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region.

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

PubMed

Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

2017-03-16

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Facial expression identification using 3D geometric features from Microsoft Kinect device

NASA Astrophysics Data System (ADS)

Han, Dongxu; Al Jawad, Naseer; Du, Hongbo

2016-05-01

Facial expression identification is an important part of face recognition and closely related to emotion detection from face images. Various solutions have been proposed in the past using different types of cameras and features. Microsoft Kinect device has been widely used for multimedia interactions. More recently, the device has been increasingly deployed for supporting scientific investigations. This paper explores the effectiveness of using the device in identifying emotional facial expressions such as surprise, smile, sad, etc. and evaluates the usefulness of 3D data points on a face mesh structure obtained from the Kinect device. We present a distance-based geometric feature component that is derived from the distances between points on the face mesh and selected reference points in a single frame. The feature components extracted across a sequence of frames starting and ending by neutral emotion represent a whole expression. The feature vector eliminates the need for complex face orientation correction, simplifying the feature extraction process and making it more efficient. We applied the kNN classifier that exploits a feature component based similarity measure following the principle of dynamic time warping to determine the closest neighbors. Preliminary tests on a small scale database of different facial expressions show promises of the newly developed features and the usefulness of the Kinect device in facial expression identification.

Mapping axonal density and average diameter using non-monotonic time-dependent gradient-echo MRI

NASA Astrophysics Data System (ADS)

Nunes, Daniel; Cruz, Tomás L.; Jespersen, Sune N.; Shemesh, Noam

2017-04-01

White Matter (WM) microstructures, such as axonal density and average diameter, are crucial to the normal function of the Central Nervous System (CNS) as they are closely related with axonal conduction velocities. Conversely, disruptions of these microstructural features may result in severe neurological deficits, suggesting that their noninvasive mapping could be an important step towards diagnosing and following pathophysiology. Whereas diffusion based MRI methods have been proposed to map these features, they typically entail the application of powerful gradients, which are rarely available in the clinic, or extremely long acquisition schemes to extract information from parameter-intensive models. In this study, we suggest that simple and time-efficient multi-gradient-echo (MGE) MRI can be used to extract the axon density from susceptibility-driven non-monotonic decay in the time-dependent signal. We show, both theoretically and with simulations, that a non-monotonic signal decay will occur for multi-compartmental microstructures - such as axons and extra-axonal spaces, which were here used as a simple model for the microstructure - and that, for axons parallel to the main magnetic field, the axonal density can be extracted. We then experimentally demonstrate in ex-vivo rat spinal cords that its different tracts - characterized by different microstructures - can be clearly contrasted using the MGE-derived maps. When the quantitative results are compared against ground-truth histology, they reflect the axonal fraction (though with a bias, as evident from Bland-Altman analysis). As well, the extra-axonal fraction can be estimated. The results suggest that our model is oversimplified, yet at the same time evidencing a potential and usefulness of the approach to map underlying microstructures using a simple and time-efficient MRI sequence. We further show that a simple general-linear-model can predict the average axonal diameters from the four model parameters, and map these average axonal diameters in the spinal cords. While clearly further modelling and theoretical developments are necessary, we conclude that salient WM microstructural features can be extracted from simple, SNR-efficient multi-gradient echo MRI, and that this paves the way towards easier estimation of WM microstructure in vivo.

Mapping axonal density and average diameter using non-monotonic time-dependent gradient-echo MRI.

PubMed

Nunes, Daniel; Cruz, Tomás L; Jespersen, Sune N; Shemesh, Noam

2017-04-01

White Matter (WM) microstructures, such as axonal density and average diameter, are crucial to the normal function of the Central Nervous System (CNS) as they are closely related with axonal conduction velocities. Conversely, disruptions of these microstructural features may result in severe neurological deficits, suggesting that their noninvasive mapping could be an important step towards diagnosing and following pathophysiology. Whereas diffusion based MRI methods have been proposed to map these features, they typically entail the application of powerful gradients, which are rarely available in the clinic, or extremely long acquisition schemes to extract information from parameter-intensive models. In this study, we suggest that simple and time-efficient multi-gradient-echo (MGE) MRI can be used to extract the axon density from susceptibility-driven non-monotonic decay in the time-dependent signal. We show, both theoretically and with simulations, that a non-monotonic signal decay will occur for multi-compartmental microstructures - such as axons and extra-axonal spaces, which were here used as a simple model for the microstructure - and that, for axons parallel to the main magnetic field, the axonal density can be extracted. We then experimentally demonstrate in ex-vivo rat spinal cords that its different tracts - characterized by different microstructures - can be clearly contrasted using the MGE-derived maps. When the quantitative results are compared against ground-truth histology, they reflect the axonal fraction (though with a bias, as evident from Bland-Altman analysis). As well, the extra-axonal fraction can be estimated. The results suggest that our model is oversimplified, yet at the same time evidencing a potential and usefulness of the approach to map underlying microstructures using a simple and time-efficient MRI sequence. We further show that a simple general-linear-model can predict the average axonal diameters from the four model parameters, and map these average axonal diameters in the spinal cords. While clearly further modelling and theoretical developments are necessary, we conclude that salient WM microstructural features can be extracted from simple, SNR-efficient multi-gradient echo MRI, and that this paves the way towards easier estimation of WM microstructure in vivo. Copyright © 2017 Elsevier Inc. All rights reserved.

Efficient Boundary Extraction of BSP Solids Based on Clipping Operations.

PubMed

Wang, Charlie C L; Manocha, Dinesh

2013-01-01

We present an efficient algorithm to extract the manifold surface that approximates the boundary of a solid represented by a Binary Space Partition (BSP) tree. Our polygonization algorithm repeatedly performs clipping operations on volumetric cells that correspond to a spatial convex partition and computes the boundary by traversing the connected cells. We use point-based representations along with finite-precision arithmetic to improve the efficiency and generate the B-rep approximation of a BSP solid. The core of our polygonization method is a novel clipping algorithm that uses a set of logical operations to make it resistant to degeneracies resulting from limited precision of floating-point arithmetic. The overall BSP to B-rep conversion algorithm can accurately generate boundaries with sharp and small features, and is faster than prior methods. At the end of this paper, we use this algorithm for a few geometric processing applications including Boolean operations, model repair, and mesh reconstruction.

Sensor-Based Vibration Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm

PubMed Central

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-01-01

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective. PMID:25207870

Sensor-based vibration signal feature extraction using an improved composite dictionary matching pursuit algorithm.

PubMed

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-09-09

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective.

Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification

PubMed Central

Satheesha, T. Y.; Prasad, M. N. Giri; Dhruve, Kashyap D.

2017-01-01

Melanoma mortality rates are the highest amongst skin cancer patients. Melanoma is life threating when it grows beyond the dermis of the skin. Hence, depth is an important factor to diagnose melanoma. This paper introduces a non-invasive computerized dermoscopy system that considers the estimated depth of skin lesions for diagnosis. A 3-D skin lesion reconstruction technique using the estimated depth obtained from regular dermoscopic images is presented. On basis of the 3-D reconstruction, depth and 3-D shape features are extracted. In addition to 3-D features, regular color, texture, and 2-D shape features are also extracted. Feature extraction is critical to achieve accurate results. Apart from melanoma, in-situ melanoma the proposed system is designed to diagnose basal cell carcinoma, blue nevus, dermatofibroma, haemangioma, seborrhoeic keratosis, and normal mole lesions. For experimental evaluations, the PH2, ISIC: Melanoma Project, and ATLAS dermoscopy data sets is considered. Different feature set combinations is considered and performance is evaluated. Significant performance improvement is reported the post inclusion of estimated depth and 3-D features. The good classification scores of sensitivity = 96%, specificity = 97% on PH2 data set and sensitivity = 98%, specificity = 99% on the ATLAS data set is achieved. Experiments conducted to estimate tumor depth from 3-D lesion reconstruction is presented. Experimental results achieved prove that the proposed computerized dermoscopy system is efficient and can be used to diagnose varied skin lesion dermoscopy images. PMID:28512610

A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

PubMed Central

Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

2017-01-01

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification

NASA Astrophysics Data System (ADS)

He, Zhi; Liu, Lin

2016-11-01

Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2 -norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods.

FEX: A Knowledge-Based System For Planimetric Feature Extraction

NASA Astrophysics Data System (ADS)

Zelek, John S.

1988-10-01

Topographical planimetric features include natural surfaces (rivers, lakes) and man-made surfaces (roads, railways, bridges). In conventional planimetric feature extraction, a photointerpreter manually interprets and extracts features from imagery on a stereoplotter. Visual planimetric feature extraction is a very labour intensive operation. The advantages of automating feature extraction include: time and labour savings; accuracy improvements; and planimetric data consistency. FEX (Feature EXtraction) combines techniques from image processing, remote sensing and artificial intelligence for automatic feature extraction. The feature extraction process co-ordinates the information and knowledge in a hierarchical data structure. The system simulates the reasoning of a photointerpreter in determining the planimetric features. Present efforts have concentrated on the extraction of road-like features in SPOT imagery. Keywords: Remote Sensing, Artificial Intelligence (AI), SPOT, image understanding, knowledge base, apars.

Phase stability analysis of chirp evoked auditory brainstem responses by Gabor frame operators.

PubMed

Corona-Strauss, Farah I; Delb, Wolfgang; Schick, Bernhard; Strauss, Daniel J

2009-12-01

We have recently shown that click evoked auditory brainstem responses (ABRs) can be efficiently processed using a novelty detection paradigm. Here, ABRs as a large-scale reflection of a stimulus locked neuronal group synchronization at the brainstem level are detected as novel instance-novel as compared to the spontaneous activity which does not exhibit a regular stimulus locked synchronization. In this paper we propose for the first time Gabor frame operators as an efficient feature extraction technique for ABR single sweep sequences that is in line with this paradigm. In particular, we use this decomposition technique to derive the Gabor frame phase stability (GFPS) of sweep sequences of click and chirp evoked ABRs. We show that the GFPS of chirp evoked ABRs provides a stable discrimination of the spontaneous activity from stimulations above the hearing threshold with a small number of sweeps, even at low stimulation intensities. It is concluded that the GFPS analysis represents a robust feature extraction method for ABR single sweep sequences. Further studies are necessary to evaluate the value of the presented approach for clinical applications.

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

NASA Astrophysics Data System (ADS)

Weinmann, Martin; Jutzi, Boris; Hinz, Stefan; Mallet, Clément

2015-07-01

3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Dietary fibre components and pectin chemical features of peels during ripening in banana and plantain varieties.

PubMed

Happi Emaga, Thomas; Robert, Christelle; Ronkart, Sébastien N; Wathelet, Bernard; Paquot, Michel

2008-07-01

The effects of the ripeness stage of banana (Musa AAA) and plantain (Musa AAB) peels on neutral detergent fibre, acid detergent fibre, cellulose, hemicelluloses, lignin, pectin contents, and pectin chemical features were studied. Plantain peels contained a higher amount of lignin but had a lower hemicellulose content than banana peels. A sequential extraction of pectins showed that acid extraction was the most efficient to isolate banana peel pectins, whereas an ammonium oxalate extraction was more appropriate for plantain peels. In all the stages of maturation, the pectin content in banana peels was higher compared to plantain peels. Moreover, the galacturonic acid and methoxy group contents in banana peels were higher than in plantain peels. The average molecular weights of the extracted pectins were in the range of 132.6-573.8 kDa and were not dependant on peel variety, while the stage of maturation did not affect the dietary fibre yields and the composition in pectic polysaccharides in a consistent manner. This study has showed that banana peels are a potential source of dietary fibres and pectins.

Feature hashing for fast image retrieval

NASA Astrophysics Data System (ADS)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

A novel approach for fire recognition using hybrid features and manifold learning-based classifier

NASA Astrophysics Data System (ADS)

Zhu, Rong; Hu, Xueying; Tang, Jiajun; Hu, Sheng

2018-03-01

Although image/video based fire recognition has received growing attention, an efficient and robust fire detection strategy is rarely explored. In this paper, we propose a novel approach to automatically identify the flame or smoke regions in an image. It is composed to three stages: (1) a block processing is applied to divide an image into several nonoverlapping image blocks, and these image blocks are identified as suspicious fire regions or not by using two color models and a color histogram-based similarity matching method in the HSV color space, (2) considering that compared to other information, the flame and smoke regions have significant visual characteristics, so that two kinds of image features are extracted for fire recognition, where local features are obtained based on the Scale Invariant Feature Transform (SIFT) descriptor and the Bags of Keypoints (BOK) technique, and texture features are extracted based on the Gray Level Co-occurrence Matrices (GLCM) and the Wavelet-based Analysis (WA) methods, and (3) a manifold learning-based classifier is constructed based on two image manifolds, which is designed via an improve Globular Neighborhood Locally Linear Embedding (GNLLE) algorithm, and the extracted hybrid features are used as input feature vectors to train the classifier, which is used to make decision for fire images or non fire images. Experiments and comparative analyses with four approaches are conducted on the collected image sets. The results show that the proposed approach is superior to the other ones in detecting fire and achieving a high recognition accuracy and a low error rate.

A novel automated spike sorting algorithm with adaptable feature extraction.

PubMed

Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

CCD-Based Skinning Injury Recognition on Potato Tubers (Solanum tuberosum L.): A Comparison between Visible and Biospeckle Imaging

PubMed Central

Gao, Yingwang; Geng, Jinfeng; Rao, Xiuqin; Ying, Yibin

2016-01-01

Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests). The features extracted from visible images were pixel-wise color and texture features, while region-wise BA (Biospeckle Activity) was calculated from biospeckle imaging. In addition, the calculation of BA using varied numbers of speckle patterns were compared. Finally, extracted features were implemented into classifiers of LS-SVM (Least Square Support Vector Machine) and BLR (Binary Logistic Regression), respectively. Results showed that color features performed better than texture features in classifying sound skin and injured skin, especially for injured skin stored no less than 1 day, with the average classification accuracy of 90%. Image capturing and processing efficiency can be speeded up in biospeckle imaging, with captured 512 frames reduced to 125 frames. Classification results obtained based on the feature of BA were acceptable for early skinning injury stored within 1 day, with the accuracy of 88.10%. It is concluded that skinning injury can be recognized by visible and biospeckle imaging during different stages. Visible imaging has the aptitude in recognizing stale skinning injury, while fresh injury can be discriminated by biospeckle imaging. PMID:27763555

CCD-Based Skinning Injury Recognition on Potato Tubers (Solanum tuberosum L.): A Comparison between Visible and Biospeckle Imaging.

PubMed

Gao, Yingwang; Geng, Jinfeng; Rao, Xiuqin; Ying, Yibin

2016-10-18

Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests). The features extracted from visible images were pixel-wise color and texture features, while region-wise BA (Biospeckle Activity) was calculated from biospeckle imaging. In addition, the calculation of BA using varied numbers of speckle patterns were compared. Finally, extracted features were implemented into classifiers of LS-SVM (Least Square Support Vector Machine) and BLR (Binary Logistic Regression), respectively. Results showed that color features performed better than texture features in classifying sound skin and injured skin, especially for injured skin stored no less than 1 day, with the average classification accuracy of 90%. Image capturing and processing efficiency can be speeded up in biospeckle imaging, with captured 512 frames reduced to 125 frames. Classification results obtained based on the feature of BA were acceptable for early skinning injury stored within 1 day, with the accuracy of 88.10%. It is concluded that skinning injury can be recognized by visible and biospeckle imaging during different stages. Visible imaging has the aptitude in recognizing stale skinning injury, while fresh injury can be discriminated by biospeckle imaging.

3D palmprint data fast acquisition and recognition

NASA Astrophysics Data System (ADS)

Wang, Xiaoxu; Huang, Shujun; Gao, Nan; Zhang, Zonghua

2014-11-01

This paper presents a fast 3D (Three-Dimension) palmprint capturing system and develops an efficient 3D palmprint feature extraction and recognition method. In order to fast acquire accurate 3D shape and texture of palmprint, a DLP projector triggers a CCD camera to realize synchronization. By generating and projecting green fringe pattern images onto the measured palm surface, 3D palmprint data are calculated from the fringe pattern images. The periodic feature vector can be derived from the calculated 3D palmprint data, so undistorted 3D biometrics is obtained. Using the obtained 3D palmprint data, feature matching test have been carried out by Gabor filter, competition rules and the mean curvature. Experimental results on capturing 3D palmprint show that the proposed acquisition method can fast get 3D shape information of palmprint. Some initial experiments on recognition show the proposed method is efficient by using 3D palmprint data.

Motorcyclists safety system to avoid rear end collisions based on acoustic signatures

NASA Astrophysics Data System (ADS)

Muzammel, M.; Yusoff, M. Zuki; Malik, A. Saeed; Mohamad Saad, M. Naufal; Meriaudeau, F.

2017-03-01

In many Asian countries, motorcyclists have a higher fatality rate as compared to other vehicles. Among many other factors, rear end collisions are also contributing for these fatalities. Collision detection systems can be useful to minimize these accidents. However, the designing of efficient and cost effective collision detection system for motorcyclist is still a major challenge. In this paper, an acoustic information based, cost effective and efficient collision detection system is proposed for motorcycle applications. The proposed technique uses the Short time Fourier Transform (STFT) to extract the features from the audio signal and Principal component analysis (PCA) has been used to reduce the feature vector length. The reduction of feature length, further increases the performance of this technique. The proposed technique has been tested on self recorded dataset and gives accuracy of 97.87%. We believe that this method can help to reduce a significant number of motorcycle accidents.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

VHDL implementation of feature-extraction algorithm for the PANDA electromagnetic calorimeter

NASA Astrophysics Data System (ADS)

Guliyev, E.; Kavatsyuk, M.; Lemmens, P. J. J.; Tambave, G.; Löhner, H.; Panda Collaboration

2012-02-01

A simple, efficient, and robust feature-extraction algorithm, developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA spectrometer at FAIR, Darmstadt, is implemented in VHDL for a commercial 16 bit 100 MHz sampling ADC. The source-code is available as an open-source project and is adaptable for other projects and sampling ADCs. Best performance with different types of signal sources can be achieved through flexible parameter selection. The on-line data-processing in FPGA enables to construct an almost dead-time free data acquisition system which is successfully evaluated as a first step towards building a complete trigger-less readout chain. Prototype setups are studied to determine the dead-time of the implemented algorithm, the rate of false triggering, timing performance, and event correlations.

Optimized hardware framework of MLP with random hidden layers for classification applications

NASA Astrophysics Data System (ADS)

Zyarah, Abdullah M.; Ramesh, Abhishek; Merkel, Cory; Kudithipudi, Dhireesha

2016-05-01

Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collection of fixed, random features, and are expedient for form-factor constrained embedded platforms. In this work, a reconfigurable and scalable architecture is proposed for the MLPs with random hidden layers with a customized building block based on CORDIC algorithm. The proposed architecture also exploits fixed point operations for area efficiency. The design is validated for classification on two different datasets. An accuracy of ~ 90% for MNIST dataset and 75% for gender classification on LFW dataset was observed. The hardware has 299 speed-up over the corresponding software realization.

An Efficient Moving Target Detection Algorithm Based on Sparsity-Aware Spectrum Estimation

PubMed Central

Shen, Mingwei; Wang, Jie; Wu, Di; Zhu, Daiyin

2014-01-01

In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results. PMID:25222035

Communication target object recognition for D2D connection with feature size limit

NASA Astrophysics Data System (ADS)

Ok, Jiheon; Kim, Soochang; Kim, Young-hoon; Lee, Chulhee

2015-03-01

Recently, a new concept of device-to-device (D2D) communication, which is called "point-and-link communication" has attracted great attentions due to its intuitive and simple operation. This approach enables user to communicate with target devices without any pre-identification information such as SSIDs, MAC addresses by selecting the target image displayed on the user's own device. In this paper, we present an efficient object matching algorithm that can be applied to look(point)-and-link communications for mobile services. Due to the limited channel bandwidth and low computational power of mobile terminals, the matching algorithm should satisfy low-complexity, low-memory and realtime requirements. To meet these requirements, we propose fast and robust feature extraction by considering the descriptor size and processing time. The proposed algorithm utilizes a HSV color histogram, SIFT (Scale Invariant Feature Transform) features and object aspect ratios. To reduce the descriptor size under 300 bytes, a limited number of SIFT key points were chosen as feature points and histograms were binarized while maintaining required performance. Experimental results show the robustness and the efficiency of the proposed algorithm.

Wavelet-based energy features for glaucomatous image classification.

PubMed

Dua, Sumeet; Acharya, U Rajendra; Chowriappa, Pradeep; Sree, S Vinitha

2012-01-01

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

Multi-focus image fusion using a guided-filter-based difference image.

PubMed

Yan, Xiang; Qin, Hanlin; Li, Jia; Zhou, Huixin; Yang, Tingwu

2016-03-20

The aim of multi-focus image fusion technology is to integrate different partially focused images into one all-focused image. To realize this goal, a new multi-focus image fusion method based on a guided filter is proposed and an efficient salient feature extraction method is presented in this paper. Furthermore, feature extraction is primarily the main objective of the present work. Based on salient feature extraction, the guided filter is first used to acquire the smoothing image containing the most sharpness regions. To obtain the initial fusion map, we compose a mixed focus measure by combining the variance of image intensities and the energy of the image gradient together. Then, the initial fusion map is further processed by a morphological filter to obtain a good reprocessed fusion map. Lastly, the final fusion map is determined via the reprocessed fusion map and is optimized by a guided filter. Experimental results demonstrate that the proposed method does markedly improve the fusion performance compared to previous fusion methods and can be competitive with or even outperform state-of-the-art fusion methods in terms of both subjective visual effects and objective quality metrics.

L1-norm kernel discriminant analysis via Bayes error bound optimization for robust feature extraction.

PubMed

Zheng, Wenming; Lin, Zhouchen; Wang, Haixian

2014-04-01

A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

PubMed Central

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses.

PubMed

Zamani, Majid; Demosthenous, Andreas

2014-07-01

Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.

Extraction and LOD control of colored interval volumes

NASA Astrophysics Data System (ADS)

Miyamura, Hiroko N.; Takeshima, Yuriko; Fujishiro, Issei; Saito, Takafumi

2005-03-01

Interval volume serves as a generalized isosurface and represents a three-dimensional subvolume for which the associated scalar filed values lie within a user-specified closed interval. In general, it is not an easy task for novices to specify the scalar field interval corresponding to their ROIs. In order to extract interval volumes from which desirable geometric features can be mined effectively, we propose a suggestive technique which extracts interval volumes automatically based on the global examination of the field contrast structure. Also proposed here is a simplification scheme for decimating resultant triangle patches to realize efficient transmission and rendition of large-scale interval volumes. Color distributions as well as geometric features are taken into account to select best edges to be collapsed. In addition, when a user wants to selectively display and analyze the original dataset, the simplified dataset is restructured to the original quality. Several simulated and acquired datasets are used to demonstrate the effectiveness of the present methods.

[Effect of Mn(II) on the error-prone DNA polymerase iota activity in extracts from human normal and tumor cells].

PubMed

Lakhin, A V; Efremova, A S; Makarova, I V; Grishina, E E; Shram, S I; Tarantul, V Z; Gening, L V

2013-01-01

The DNA polymerase iota (Pol iota), which has some peculiar features and is characterized by an extremely error-prone DNA synthesis, belongs to the group of enzymes preferentially activated by Mn2+ instead of Mg2+. In this work, the effect of Mn2+ on DNA synthesis in cell extracts from a) normal human and murine tissues, b) human tumor (uveal melanoma), and c) cultured human tumor cell lines SKOV-3 and HL-60 was tested. Each group displayed characteristic features of Mn-dependent DNA synthesis. The changes in the Mn-dependent DNA synthesis caused by malignant transformation of normal tissues are described. It was also shown that the error-prone DNA synthesis catalyzed by Pol iota in extracts of all cell types was efficiently suppressed by an RNA aptamer (IKL5) against Pol iota obtained in our work earlier. The obtained results suggest that IKL5 might be used to suppress the enhanced activity of Pol iota in tumor cells.

Interactive Exploration and Analysis of Large-Scale Simulations Using Topology-Based Data Segmentation.

PubMed

Bremer, Peer-Timo; Weber, Gunther; Tierny, Julien; Pascucci, Valerio; Day, Marcus S; Bell, John B

2011-09-01

Large-scale simulations are increasingly being used to study complex scientific and engineering phenomena. As a result, advanced visualization and data analysis are also becoming an integral part of the scientific process. Often, a key step in extracting insight from these large simulations involves the definition, extraction, and evaluation of features in the space and time coordinates of the solution. However, in many applications, these features involve a range of parameters and decisions that will affect the quality and direction of the analysis. Examples include particular level sets of a specific scalar field, or local inequalities between derived quantities. A critical step in the analysis is to understand how these arbitrary parameters/decisions impact the statistical properties of the features, since such a characterization will help to evaluate the conclusions of the analysis as a whole. We present a new topological framework that in a single-pass extracts and encodes entire families of possible features definitions as well as their statistical properties. For each time step we construct a hierarchical merge tree a highly compact, yet flexible feature representation. While this data structure is more than two orders of magnitude smaller than the raw simulation data it allows us to extract a set of features for any given parameter selection in a postprocessing step. Furthermore, we augment the trees with additional attributes making it possible to gather a large number of useful global, local, as well as conditional statistic that would otherwise be extremely difficult to compile. We also use this representation to create tracking graphs that describe the temporal evolution of the features over time. Our system provides a linked-view interface to explore the time-evolution of the graph interactively alongside the segmentation, thus making it possible to perform extensive data analysis in a very efficient manner. We demonstrate our framework by extracting and analyzing burning cells from a large-scale turbulent combustion simulation. In particular, we show how the statistical analysis enabled by our techniques provides new insight into the combustion process.

Detection of Perlger-Huet anomaly based on augmented fast marching method and speeded up robust features.

PubMed

Sun, Minglei; Yang, Shaobao; Jiang, Jinling; Wang, Qiwei

2015-01-01

Pelger-Huet anomaly (PHA) and Pseudo Pelger-Huet anomaly (PPHA) are neutrophil with abnormal morphology. They have the bilobed or unilobed nucleus and excessive clumping chromatin. Currently, detection of this kind of cell mainly depends on the manual microscopic examination by a clinician, thus, the quality of detection is limited by the efficiency and a certain subjective consciousness of the clinician. In this paper, a detection method for PHA and PPHA is proposed based on karyomorphism and chromatin distribution features. Firstly, the skeleton of the nucleus is extracted using an augmented Fast Marching Method (AFMM) and width distribution is obtained through distance transform. Then, caryoplastin in the nucleus is extracted based on Speeded Up Robust Features (SURF) and a K-nearest-neighbor (KNN) classifier is constructed to analyze the features. Experiment shows that the sensitivity and specificity of this method achieved 87.5% and 83.33%, which means that the detection accuracy of PHA is acceptable. Meanwhile, the detection method should be helpful to the automatic morphological classification of blood cells.

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Fourier transform infrared spectroscopy microscopic imaging classification based on spatial-spectral features

NASA Astrophysics Data System (ADS)

Liu, Lian; Yang, Xiukun; Zhong, Mingliang; Liu, Yao; Jing, Xiaojun; Yang, Qin

2018-04-01

The discrete fractional Brownian incremental random (DFBIR) field is used to describe the irregular, random, and highly complex shapes of natural objects such as coastlines and biological tissues, for which traditional Euclidean geometry cannot be used. In this paper, an anisotropic variable window (AVW) directional operator based on the DFBIR field model is proposed for extracting spatial characteristics of Fourier transform infrared spectroscopy (FTIR) microscopic imaging. Probabilistic principal component analysis first extracts spectral features, and then the spatial features of the proposed AVW directional operator are combined with the former to construct a spatial-spectral structure, which increases feature-related information and helps a support vector machine classifier to obtain more efficient distribution-related information. Compared to Haralick’s grey-level co-occurrence matrix, Gabor filters, and local binary patterns (e.g. uniform LBPs, rotation-invariant LBPs, uniform rotation-invariant LBPs), experiments on three FTIR spectroscopy microscopic imaging datasets show that the proposed AVW directional operator is more advantageous in terms of classification accuracy, particularly for low-dimensional spaces of spatial characteristics.

Image features dependant correlation-weighting function for efficient PRNU based source camera identification.

PubMed

Tiwari, Mayank; Gupta, Bhupendra

2018-04-01

For source camera identification (SCI), photo response non-uniformity (PRNU) has been widely used as the fingerprint of the camera. The PRNU is extracted from the image by applying a de-noising filter then taking the difference between the original image and the de-noised image. However, it is observed that intensity-based features and high-frequency details (edges and texture) of the image, effect quality of the extracted PRNU. This effects correlation calculation and creates problems in SCI. For solving this problem, we propose a weighting function based on image features. We have experimentally identified image features (intensity and high-frequency contents) effect on the estimated PRNU, and then develop a weighting function which gives higher weights to image regions which give reliable PRNU and at the same point it gives comparatively less weights to the image regions which do not give reliable PRNU. Experimental results show that the proposed weighting function is able to improve the accuracy of SCI up to a great extent. Copyright © 2018 Elsevier B.V. All rights reserved.

An efficient cloud detection method for high resolution remote sensing panchromatic imagery

NASA Astrophysics Data System (ADS)

Li, Chaowei; Lin, Zaiping; Deng, Xinpu

2018-04-01

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

An adaptive tensor voting algorithm combined with texture spectrum

NASA Astrophysics Data System (ADS)

Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi

2015-01-01

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

Securing SIFT: Privacy-preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.

PubMed

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Qin, Zhan; Ren, Kui

2016-05-13

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, addition of noise and change in 3D viewpoint and illumination.

SecSIFT: Privacy-preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.

PubMed

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Qin, Zhan; Ren, Kui

2016-05-13

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, addition of noise and change in 3D viewpoint and illumination.

An iris recognition algorithm based on DCT and GLCM

NASA Astrophysics Data System (ADS)

Feng, G.; Wu, Ye-qing

2008-04-01

With the enlargement of mankind's activity range, the significance for person's status identity is becoming more and more important. So many different techniques for person's status identity were proposed for this practical usage. Conventional person's status identity methods like password and identification card are not always reliable. A wide variety of biometrics has been developed for this challenge. Among those biologic characteristics, iris pattern gains increasing attention for its stability, reliability, uniqueness, noninvasiveness and difficult to counterfeit. The distinct merits of the iris lead to its high reliability for personal identification. So the iris identification technique had become hot research point in the past several years. This paper presents an efficient algorithm for iris recognition using gray-level co-occurrence matrix(GLCM) and Discrete Cosine transform(DCT). To obtain more representative iris features, features from space and DCT transformation domain are extracted. Both GLCM and DCT are applied on the iris image to form the feature sequence in this paper. The combination of GLCM and DCT makes the iris feature more distinct. Upon GLCM and DCT the eigenvector of iris extracted, which reflects features of spatial transformation and frequency transformation. Experimental results show that the algorithm is effective and feasible with iris recognition.

Superpixel-Augmented Endmember Detection for Hyperspectral Images

NASA Technical Reports Server (NTRS)

Thompson, David R.; Castano, Rebecca; Gilmore, Martha

2011-01-01

Superpixels are homogeneous image regions comprised of several contiguous pixels. They are produced by shattering the image into contiguous, homogeneous regions that each cover between 20 and 100 image pixels. The segmentation aims for a many-to-one mapping from superpixels to image features; each image feature could contain several superpixels, but each superpixel occupies no more than one image feature. This conservative segmentation is relatively easy to automate in a robust fashion. Superpixel processing is related to the more general idea of improving hyperspectral analysis through spatial constraints, which can recognize subtle features at or below the level of noise by exploiting the fact that their spectral signatures are found in neighboring pixels. Recent work has explored spatial constraints for endmember extraction, showing significant advantages over techniques that ignore pixels relative positions. Methods such as AMEE (automated morphological endmember extraction) express spatial influence using fixed isometric relationships a local square window or Euclidean distance in pixel coordinates. In other words, two pixels covariances are based on their spatial proximity, but are independent of their absolute location in the scene. These isometric spatial constraints are most appropriate when spectral variation is smooth and constant over the image. Superpixels are simple to implement, efficient to compute, and are empirically effective. They can be used as a preprocessing step with any desired endmember extraction technique. Superpixels also have a solid theoretical basis in the hyperspectral linear mixing model, making them a principled approach for improving endmember extraction. Unlike existing approaches, superpixels can accommodate non-isometric covariance between image pixels (characteristic of discrete image features separated by step discontinuities). These kinds of image features are common in natural scenes. Analysts can substitute superpixels for image pixels during endmember analysis that leverages the spatial contiguity of scene features to enhance subtle spectral features. Superpixels define populations of image pixels that are independent samples from each image feature, permitting robust estimation of spectral properties, and reducing measurement noise in proportion to the area of the superpixel. This permits improved endmember extraction, and enables automated search for novel and constituent minerals in very noisy, hyperspatial images. This innovation begins with a graph-based segmentation based on the work of Felzenszwalb et al., but then expands their approach to the hyperspectral image domain with a Euclidean distance metric. Then, the mean spectrum of each segment is computed, and the resulting data cloud is used as input into sequential maximum angle convex cone (SMACC) endmember extraction.

Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts.

PubMed

Qiao, Xiaojun; Jiang, Jinbao; Qi, Xiaotong; Guo, Haiqiang; Yuan, Deshuai

2017-04-01

It's well-known fungi-contaminated peanuts contain potent carcinogen. Efficiently identifying and separating the contaminated can help prevent aflatoxin entering in food chain. In this study, shortwave infrared (SWIR) hyperspectral images for identifying the prepared contaminated kernels. Feature selection method of analysis of variance (ANOVA) and feature extraction method of nonparametric weighted feature extraction (NWFE) were used to concentrate spectral information into a subspace where contaminated and healthy peanuts can have favorable separability. Then, peanut pixels were classified using SVM. Moreover, image segmentation method of region growing was applied to segment the image as kernel-scale patches and meanwhile to number the kernels. The result shows that pixel-wise classification accuracies are 99.13% for breed A, 96.72% for B and 99.73% for C in learning images, and are 96.32%, 94.2% and 97.51% in validation images. Contaminated peanuts were correctly marked as aberrant kernels in both learning images and validation images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Datum Feature Extraction and Deformation Analysis Method Based on Normal Vector of Point Cloud

NASA Astrophysics Data System (ADS)

Sun, W.; Wang, J.; Jin, F.; Liang, Z.; Yang, Y.

2018-04-01

In order to solve the problem lacking applicable analysis method in the application of three-dimensional laser scanning technology to the field of deformation monitoring, an efficient method extracting datum feature and analysing deformation based on normal vector of point cloud was proposed. Firstly, the kd-tree is used to establish the topological relation. Datum points are detected by tracking the normal vector of point cloud determined by the normal vector of local planar. Then, the cubic B-spline curve fitting is performed on the datum points. Finally, datum elevation and the inclination angle of the radial point are calculated according to the fitted curve and then the deformation information was analyzed. The proposed approach was verified on real large-scale tank data set captured with terrestrial laser scanner in a chemical plant. The results show that the method could obtain the entire information of the monitor object quickly and comprehensively, and reflect accurately the datum feature deformation.

Interpretation of fingerprint image quality features extracted by self-organizing maps

NASA Astrophysics Data System (ADS)

Danov, Ivan; Olsen, Martin A.; Busch, Christoph

2014-05-01

Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

PubMed

Sinha, S K; Karray, F

2002-01-01

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery

PubMed Central

Chaddad, Ahmad; Desrosiers, Christian; Bouridane, Ahmed; Toews, Matthew; Hassan, Lama; Tanougast, Camel

2016-01-01

Purpose This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. Materials and Methods In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. Results Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. Conclusions These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images. PMID:26901134

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

PubMed Central

Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

2017-01-01

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body. PMID:28300783

Highly Scalable Matching Pursuit Signal Decomposition Algorithm

NASA Technical Reports Server (NTRS)

Christensen, Daniel; Das, Santanu; Srivastava, Ashok N.

2009-01-01

Matching Pursuit Decomposition (MPD) is a powerful iterative algorithm for signal decomposition and feature extraction. MPD decomposes any signal into linear combinations of its dictionary elements or atoms . A best fit atom from an arbitrarily defined dictionary is determined through cross-correlation. The selected atom is subtracted from the signal and this procedure is repeated on the residual in the subsequent iterations until a stopping criterion is met. The reconstructed signal reveals the waveform structure of the original signal. However, a sufficiently large dictionary is required for an accurate reconstruction; this in return increases the computational burden of the algorithm, thus limiting its applicability and level of adoption. The purpose of this research is to improve the scalability and performance of the classical MPD algorithm. Correlation thresholds were defined to prune insignificant atoms from the dictionary. The Coarse-Fine Grids and Multiple Atom Extraction techniques were proposed to decrease the computational burden of the algorithm. The Coarse-Fine Grids method enabled the approximation and refinement of the parameters for the best fit atom. The ability to extract multiple atoms within a single iteration enhanced the effectiveness and efficiency of each iteration. These improvements were implemented to produce an improved Matching Pursuit Decomposition algorithm entitled MPD++. Disparate signal decomposition applications may require a particular emphasis of accuracy or computational efficiency. The prominence of the key signal features required for the proper signal classification dictates the level of accuracy necessary in the decomposition. The MPD++ algorithm may be easily adapted to accommodate the imposed requirements. Certain feature extraction applications may require rapid signal decomposition. The full potential of MPD++ may be utilized to produce incredible performance gains while extracting only slightly less energy than the standard algorithm. When the utmost accuracy must be achieved, the modified algorithm extracts atoms more conservatively but still exhibits computational gains over classical MPD. The MPD++ algorithm was demonstrated using an over-complete dictionary on real life data. Computational times were reduced by factors of 1.9 and 44 for the emphases of accuracy and performance, respectively. The modified algorithm extracted similar amounts of energy compared to classical MPD. The degree of the improvement in computational time depends on the complexity of the data, the initialization parameters, and the breadth of the dictionary. The results of the research confirm that the three modifications successfully improved the scalability and computational efficiency of the MPD algorithm. Correlation Thresholding decreased the time complexity by reducing the dictionary size. Multiple Atom Extraction also reduced the time complexity by decreasing the number of iterations required for a stopping criterion to be reached. The Course-Fine Grids technique enabled complicated atoms with numerous variable parameters to be effectively represented in the dictionary. Due to the nature of the three proposed modifications, they are capable of being stacked and have cumulative effects on the reduction of the time complexity.

Principal axes estimation using the vibration modes of physics-based deformable models.

PubMed

Krinidis, Stelios; Chatzis, Vassilios

2008-06-01

This paper addresses the issue of accurate, effective, computationally efficient, fast, and fully automated 2-D object orientation and scaling factor estimation. The object orientation is calculated using object principal axes estimation. The approach relies on the object's frequency-based features. The frequency-based features used by the proposed technique are extracted by a 2-D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations.

Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants

PubMed Central

Doppler, Maria; Kluger, Bernhard; Bueschl, Christoph; Schneider, Christina; Krska, Rudolf; Delcambre, Sylvie; Hiller, Karsten; Lemmens, Marc; Schuhmacher, Rainer

2016-01-01

The evaluation of extraction protocols for untargeted metabolomics approaches is still difficult. We have applied a novel stable isotope-assisted workflow for untargeted LC-HRMS-based plant metabolomics , which allows for the first time every detected feature to be considered for method evaluation. The efficiency and complementarity of commonly used extraction solvents, namely 1 + 3 (v/v) mixtures of water and selected organic solvents (methanol, acetonitrile or methanol/acetonitrile 1 + 1 (v/v)), with and without the addition of 0.1% (v/v) formic acid were compared. Four different wheat organs were sampled, extracted and analysed by LC-HRMS. Data evaluation was performed with the in-house-developed MetExtract II software and R. With all tested solvents a total of 871 metabolites were extracted in ear, 785 in stem, 733 in leaf and 517 in root samples, respectively. Between 48% (stem) and 57% (ear) of the metabolites detected in a particular organ were found with all extraction mixtures, and 127 of 996 metabolites were consistently shared between all extraction agent/organ combinations. In aqueous methanol, acidification with formic acid led to pronounced pH dependency regarding the precision of metabolite abundance and the number of detectable metabolites, whereas extracts of acetonitrile-containing mixtures were less affected. Moreover, methanol and acetonitrile have been found to be complementary with respect to extraction efficiency. Interestingly, the beneficial properties of both solvents can be combined by the use of a water-methanol-acetonitrile mixture for global metabolite extraction instead of aqueous methanol or aqueous acetonitrile alone. PMID:27367667

Secondary iris recognition method based on local energy-orientation feature

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing

2015-01-01

This paper proposes a secondary iris recognition based on local features. The application of the energy-orientation feature (EOF) by two-dimensional Gabor filter to the extraction of the iris goes before the first recognition by the threshold of similarity, which sets the whole iris database into two categories-a correctly recognized class and a class to be recognized. Therefore, the former are accepted and the latter are transformed by histogram to achieve an energy-orientation histogram feature (EOHF), which is followed by a second recognition with the chi-square distance. The experiment has proved that the proposed method, because of its higher correct recognition rate, could be designated as the most efficient and effective among its companion studies in iris recognition algorithms.

Deep convolutional neural networks for classifying GPR B-scans

NASA Astrophysics Data System (ADS)

Besaw, Lance E.; Stimac, Philip J.

2015-05-01

Symmetric and asymmetric buried explosive hazards (BEHs) present real, persistent, deadly threats on the modern battlefield. Current approaches to mitigate these threats rely on highly trained operatives to reliably detect BEHs with reasonable false alarm rates using handheld Ground Penetrating Radar (GPR) and metal detectors. As computers become smaller, faster and more efficient, there exists greater potential for automated threat detection based on state-of-the-art machine learning approaches, reducing the burden on the field operatives. Recent advancements in machine learning, specifically deep learning artificial neural networks, have led to significantly improved performance in pattern recognition tasks, such as object classification in digital images. Deep convolutional neural networks (CNNs) are used in this work to extract meaningful signatures from 2-dimensional (2-D) GPR B-scans and classify threats. The CNNs skip the traditional "feature engineering" step often associated with machine learning, and instead learn the feature representations directly from the 2-D data. A multi-antennae, handheld GPR with centimeter-accurate positioning data was used to collect shallow subsurface data over prepared lanes containing a wide range of BEHs. Several heuristics were used to prevent over-training, including cross validation, network weight regularization, and "dropout." Our results show that CNNs can extract meaningful features and accurately classify complex signatures contained in GPR B-scans, complementing existing GPR feature extraction and classification techniques.

Three-dimensional tracking for efficient fire fighting in complex situations

NASA Astrophysics Data System (ADS)

Akhloufi, Moulay; Rossi, Lucile

2009-05-01

Each year, hundred millions hectares of forests burn causing human and economic losses. For efficient fire fighting, the personnel in the ground need tools permitting the prediction of fire front propagation. In this work, we present a new technique for automatically tracking fire spread in three-dimensional space. The proposed approach uses a stereo system to extract a 3D shape from fire images. A new segmentation technique is proposed and permits the extraction of fire regions in complex unstructured scenes. It works in the visible spectrum and combines information extracted from YUV and RGB color spaces. Unlike other techniques, our algorithm does not require previous knowledge about the scene. The resulting fire regions are classified into different homogenous zones using clustering techniques. Contours are then extracted and a feature detection algorithm is used to detect interest points like local maxima and corners. Extracted points from stereo images are then used to compute the 3D shape of the fire front. The resulting data permits to build the fire volume. The final model is used to compute important spatial and temporal fire characteristics like: spread dynamics, local orientation, heading direction, etc. Tests conducted on the ground show the efficiency of the proposed scheme. This scheme is being integrated with a fire spread mathematical model in order to predict and anticipate the fire behaviour during fire fighting. Also of interest to fire-fighters, is the proposed automatic segmentation technique that can be used in early detection of fire in complex scenes.

Coarse-to-fine wavelet-based airport detection

NASA Astrophysics Data System (ADS)

Li, Cheng; Wang, Shuigen; Pang, Zhaofeng; Zhao, Baojun

2015-10-01

Airport detection on optical remote sensing images has attracted great interest in the applications of military optics scout and traffic control. However, most of the popular techniques for airport detection from optical remote sensing images have three weaknesses: 1) Due to the characteristics of optical images, the detection results are often affected by imaging conditions, like weather situation and imaging distortion; and 2) optical images contain comprehensive information of targets, so that it is difficult for extracting robust features (e.g., intensity and textural information) to represent airport area; 3) the high resolution results in large data volume, which makes real-time processing limited. Most of the previous works mainly focus on solving one of those problems, and thus, the previous methods cannot achieve the balance of performance and complexity. In this paper, we propose a novel coarse-to-fine airport detection framework to solve aforementioned three issues using wavelet coefficients. The framework includes two stages: 1) an efficient wavelet-based feature extraction is adopted for multi-scale textural feature representation, and support vector machine(SVM) is exploited for classifying and coarsely deciding airport candidate region; and then 2) refined line segment detection is used to obtain runway and landing field of airport. Finally, airport recognition is achieved by applying the fine runway positioning to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy and processing efficiency.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis.

PubMed

Taherisadr, Mojtaba; Dehzangi, Omid; Parsaei, Hossein

2017-12-13

As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain-computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time-frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique-namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

Application of an enhanced fuzzy algorithm for MR brain tumor image segmentation

NASA Astrophysics Data System (ADS)

Hemanth, D. Jude; Vijila, C. Kezi Selva; Anitha, J.

2010-02-01

Image segmentation is one of the significant digital image processing techniques commonly used in the medical field. One of the specific applications is tumor detection in abnormal Magnetic Resonance (MR) brain images. Fuzzy approaches are widely preferred for tumor segmentation which generally yields superior results in terms of accuracy. But most of the fuzzy algorithms suffer from the drawback of slow convergence rate which makes the system practically non-feasible. In this work, the application of modified Fuzzy C-means (FCM) algorithm to tackle the convergence problem is explored in the context of brain image segmentation. This modified FCM algorithm employs the concept of quantization to improve the convergence rate besides yielding excellent segmentation efficiency. This algorithm is experimented on real time abnormal MR brain images collected from the radiologists. A comprehensive feature vector is extracted from these images and used for the segmentation technique. An extensive feature selection process is performed which reduces the convergence time period and improve the segmentation efficiency. After segmentation, the tumor portion is extracted from the segmented image. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures. Thus, this work highlights the application of the modified algorithm for brain tumor detection in abnormal MR brain images.

Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at participating nodes. Therefore, the feature-extraction method based on the Haar DWT is presented that employs a maximum-entropy measure to determine significant wavelet coefficients. Features are formed by calculating the energy of coefficients grouped around the competing clusters. A DWT-based feature extraction algorithm used for vehicle classification in WSNs can be enhanced by an added rule for selecting the optimal number of resolution levels to improve the correct classification rate and reduce energy consumption expended in local algorithm computations. Published field trial data for vehicular ground targets, measured with multiple sensor types, are used to evaluate the wavelet-assisted algorithms. Extracted features are used in established target recognition routines, e.g., the Bayesian minimum-error-rate classifier, to compare the effects on the classification performance of the wavelet compression. Simulations of feature sets and recognition routines at different resolution levels in target scenarios indicate the impact on classification rates, while formulas are provided to estimate reduction in resource use due to distributed compression.

A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition.

PubMed

Khushaba, Rami N; Al-Timemy, Ali H; Al-Ani, Ahmed; Al-Jumaily, Adel

2017-10-01

The extraction of the accurate and efficient descriptors of muscular activity plays an important role in tackling the challenging problem of myoelectric control of powered prostheses. In this paper, we present a new feature extraction framework that aims to give an enhanced representation of muscular activities through increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels. We propose to use time-domain descriptors (TDDs) in estimating the EMG signal power spectrum characteristics; a step that preserves the computational power required for the construction of spectral features. Subsequently, TDD is used in a process that involves: 1) representing the temporal evolution of the EMG signals by progressively tracking the correlation between the TDD extracted from each analysis time window and a nonlinearly mapped version of it across the same EMG channel and 2) representing the spatial coherence between the different EMG channels, which is achieved by calculating the correlation between the TDD extracted from the differences of all possible combinations of pairs of channels and their nonlinearly mapped versions. The proposed temporal-spatial descriptors (TSDs) are validated on multiple sparse and high-density (HD) EMG data sets collected from a number of intact-limbed and amputees performing a large number of hand and finger movements. Classification results showed significant reductions in the achieved error rates in comparison to other methods, with the improvement of at least 8% on average across all subjects. Additionally, the proposed TSDs achieved significantly well in problems with HD-EMG with average classification errors of <5% across all subjects using windows lengths of 50 ms only.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harmon, S; Jeraj, R; Galavis, P

Purpose: Sensitivity of PET-derived texture features to reconstruction methods has been reported for features extracted from axial planes; however, studies often utilize three dimensional techniques. This work aims to quantify the impact of multi-plane (3D) vs. single-plane (2D) feature extraction on radiomics-based analysis, including sensitivity to reconstruction parameters and potential loss of spatial information. Methods: Twenty-three patients with solid tumors underwent [{sup 18}F]FDG PET/CT scans under identical protocols. PET data were reconstructed using five sets of reconstruction parameters. Tumors were segmented using an automatic, in-house algorithm robust to reconstruction variations. 50 texture features were extracted using two Methods: 2D patchesmore » along axial planes and 3D patches. For each method, sensitivity of features to reconstruction parameters was calculated as percent difference relative to the average value across reconstructions. Correlations between feature values were compared when using 2D and 3D extraction. Results: 21/50 features showed significantly different sensitivity to reconstruction parameters when extracted in 2D vs 3D (wilcoxon α<0.05), assessed by overall range of variation, Rangevar(%). Eleven showed greater sensitivity to reconstruction in 2D extraction, primarily first-order and co-occurrence features (average Rangevar increase 83%). The remaining ten showed higher variation in 3D extraction (average Range{sub var}increase 27%), mainly co-occurence and greylevel run-length features. Correlation of feature value extracted in 2D and feature value extracted in 3D was poor (R<0.5) in 12/50 features, including eight co-occurrence features. Feature-to-feature correlations in 2D were marginally higher than 3D, ∣R∣>0.8 in 16% and 13% of all feature combinations, respectively. Larger sensitivity to reconstruction parameters were seen for inter-feature correlation in 2D(σ=6%) than 3D (σ<1%) extraction. Conclusion: Sensitivity and correlation of various texture features were shown to significantly differ between 2D and 3D extraction. Additionally, inter-feature correlations were more sensitive to reconstruction variation using single-plane extraction. This work highlights a need for standardized feature extraction/selection techniques in radiomics.« less

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

The Study of Residential Areas Extraction Based on GF-3 Texture Image Segmentation

NASA Astrophysics Data System (ADS)

Shao, G.; Luo, H.; Tao, X.; Ling, Z.; Huang, Y.

2018-04-01

The study chooses the standard stripe and dual polarization SAR images of GF-3 as the basic data. Residential areas extraction processes and methods based upon GF-3 images texture segmentation are compared and analyzed. GF-3 images processes include radiometric calibration, complex data conversion, multi-look processing, images filtering, and then conducting suitability analysis for different images filtering methods, the filtering result show that the filtering method of Kuan is efficient for extracting residential areas, then, we calculated and analyzed the texture feature vectors using the GLCM (the Gary Level Co-occurrence Matrix), texture feature vectors include the moving window size, step size and angle, the result show that window size is 11*11, step is 1, and angle is 0°, which is effective and optimal for the residential areas extracting. And with the FNEA (Fractal Net Evolution Approach), we segmented the GLCM texture images, and extracted the residential areas by threshold setting. The result of residential areas extraction verified and assessed by confusion matrix. Overall accuracy is 0.897, kappa is 0.881, and then we extracted the residential areas by SVM classification based on GF-3 images, the overall accuracy is less 0.09 than the accuracy of extraction method based on GF-3 Texture Image Segmentation. We reached the conclusion that residential areas extraction based on GF-3 SAR texture image multi-scale segmentation is simple and highly accurate. although, it is difficult to obtain multi-spectrum remote sensing image in southern China, in cloudy and rainy weather throughout the year, this paper has certain reference significance.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

NASA Astrophysics Data System (ADS)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Detection of Neuron Membranes in Electron Microscopy Images Using Multi-scale Context and Radon-Like Features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seyedhosseini, Mojtaba; Kumar, Ritwik; Jurrus, Elizabeth R.

2011-10-01

Automated neural circuit reconstruction through electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that exploits multi-scale contextual information together with Radon-like features (RLF) to learn a series of discriminative models. The main idea is to build a framework which is capable of extracting information about cell membranes from a large contextual area of an EM image in a computationally efficient way. Toward this goal, we extract RLF that can be computed efficiently from the input image and generate a scale-space representation of the context images that are obtained at the output ofmore » each discriminative model in the series. Compared to a single-scale model, the use of a multi-scale representation of the context image gives the subsequent classifiers access to a larger contextual area in an effective way. Our strategy is general and independent of the classifier and has the potential to be used in any context based framework. We demonstrate that our method outperforms the state-of-the-art algorithms in detection of neuron membranes in EM images.« less

Efficient video-equipped fire detection approach for automatic fire alarm systems

NASA Astrophysics Data System (ADS)

Kang, Myeongsu; Tung, Truong Xuan; Kim, Jong-Myon

2013-01-01

This paper proposes an efficient four-stage approach that automatically detects fire using video capabilities. In the first stage, an approximate median method is used to detect video frame regions involving motion. In the second stage, a fuzzy c-means-based clustering algorithm is employed to extract candidate regions of fire from all of the movement-containing regions. In the third stage, a gray level co-occurrence matrix is used to extract texture parameters by tracking red-colored objects in the candidate regions. These texture features are, subsequently, used as inputs of a back-propagation neural network to distinguish between fire and nonfire. Experimental results indicate that the proposed four-stage approach outperforms other fire detection algorithms in terms of consistently increasing the accuracy of fire detection in both indoor and outdoor test videos.

A GPU-Accelerated Approach for Feature Tracking in Time-Varying Imagery Datasets.

PubMed

Peng, Chao; Sahani, Sandip; Rushing, John

2017-10-01

We propose a novel parallel connected component labeling (CCL) algorithm along with efficient out-of-core data management to detect and track feature regions of large time-varying imagery datasets. Our approach contributes to the big data field with parallel algorithms tailored for GPU architectures. We remove the data dependency between frames and achieve pixel-level parallelism. Due to the large size, the entire dataset cannot fit into cached memory. Frames have to be streamed through the memory hierarchy (disk to CPU main memory and then to GPU memory), partitioned, and processed as batches, where each batch is small enough to fit into the GPU. To reconnect the feature regions that are separated due to data partitioning, we present a novel batch merging algorithm to extract the region connection information across multiple batches in a parallel fashion. The information is organized in a memory-efficient structure and supports fast indexing on the GPU. Our experiment uses a commodity workstation equipped with a single GPU. The results show that our approach can efficiently process a weather dataset composed of terabytes of time-varying radar images. The advantages of our approach are demonstrated by comparing to the performance of an efficient CPU cluster implementation which is being used by the weather scientists.

Vertical Feature Mask Feature Classification Flag Extraction

Atmospheric Science Data Center

2013-03-28

  Vertical Feature Mask Feature Classification Flag Extraction This routine demonstrates extraction of the ... in a CALIPSO Lidar Level 2 Vertical Feature Mask feature classification flag value. It is written in Interactive Data Language (IDL) ...

Agile Multi-Scale Decompositions for Automatic Image Registration

NASA Technical Reports Server (NTRS)

Murphy, James M.; Leija, Omar Navarro; Le Moigne, Jacqueline

2016-01-01

In recent works, the first and third authors developed an automatic image registration algorithm based on a multiscale hybrid image decomposition with anisotropic shearlets and isotropic wavelets. This prototype showed strong performance, improving robustness over registration with wavelets alone. However, this method imposed a strict hierarchy on the order in which shearlet and wavelet features were used in the registration process, and also involved an unintegrated mixture of MATLAB and C code. In this paper, we introduce a more agile model for generating features, in which a flexible and user-guided mix of shearlet and wavelet features are computed. Compared to the previous prototype, this method introduces a flexibility to the order in which shearlet and wavelet features are used in the registration process. Moreover, the present algorithm is now fully coded in C, making it more efficient and portable than the MATLAB and C prototype. We demonstrate the versatility and computational efficiency of this approach by performing registration experiments with the fully-integrated C algorithm. In particular, meaningful timing studies can now be performed, to give a concrete analysis of the computational costs of the flexible feature extraction. Examples of synthetically warped and real multi-modal images are analyzed.

Off-lexicon online Arabic handwriting recognition using neural network

NASA Astrophysics Data System (ADS)

Yahia, Hamdi; Chaabouni, Aymen; Boubaker, Houcine; Alimi, Adel M.

2017-03-01

This paper highlights a new method for online Arabic handwriting recognition based on graphemes segmentation. The main contribution of our work is to explore the utility of Beta-elliptic model in segmentation and features extraction for online handwriting recognition. Indeed, our method consists in decomposing the input signal into continuous part called graphemes based on Beta-Elliptical model, and classify them according to their position in the pseudo-word. The segmented graphemes are then described by the combination of geometric features and trajectory shape modeling. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results using the benchmarking ADAB Database show the performance of the proposed method.

Character feature integration of Chinese calligraphy and font

NASA Astrophysics Data System (ADS)

Shi, Cao; Xiao, Jianguo; Jia, Wenhua; Xu, Canhui

2013-01-01

A framework is proposed in this paper to effectively generate a new hybrid character type by means of integrating local contour feature of Chinese calligraphy with structural feature of font in computer system. To explore traditional art manifestation of calligraphy, multi-directional spatial filter is applied for local contour feature extraction. Then the contour of character image is divided into sub-images. The sub-images in the identical position from various characters are estimated by Gaussian distribution. According to its probability distribution, the dilation operator and erosion operator are designed to adjust the boundary of font image. And then new Chinese character images are generated which possess both contour feature of artistical calligraphy and elaborate structural feature of font. Experimental results demonstrate the new characters are visually acceptable, and the proposed framework is an effective and efficient strategy to automatically generate the new hybrid character of calligraphy and font.

Iris recognition based on key image feature extraction.

PubMed

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Final Report DOE SSL Grant (No. DE-EE0006673) Advanced Light Extraction Structure for OLED Lighting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cooper, Gregory; Monickam, Selina

The innovation proposed in this grant is to demonstrate a novel internal light extraction (ILE) design that can maximize the energy efficiency of Organic Light Emitting Diode (OLED) lighting devices without negatively impacting the device voltage, efficacy or angular color dependences. Even though, OLEDs have unique features compared to its inorganic counterparts, LEDs, in terms of technology development and market readiness levels, it still lags LEDs by several years. The main challenges as identified in the National Research Council’s 2013 Assessment on Solid State Lighting, are the cost of the materials and the low light extraction efficacy [1]. Improving themore » light extraction will improve both the $/Klm and lm/W, two important metrics DOE uses to measure the cost effectiveness of a light source.« less

Simultaneous Enrichment of Polycyclic Aromatic Hydrocarbons and Cu(2+) in Water Using Tetraazacalix[2]arene[2]triazine as a Solid-Phase Extraction Selector.

PubMed

Zhao, Wenjie; Yang, Liu; He, Lijun; Zhang, Shusheng

2016-08-10

On the basis of the definite retention mechanism proven by the stationary phase for high-performance liquid chromatography, tetraazacalix[2]arene[2]triazine featuring multiple recognition sites was assessed as a solid-phase extraction (SPE) selector. The applicability of its silica support was used for the extraction of trace amounts of polycyclic aromatic hydrocarbons (PAHs) and Cu(2+) in aqueous samples, followed by high-performance liquid chromatography fluorometric and graphite furnace atomic absorption spectrometric determination. On the basis of the π-π interaction with PAHs and the chelating interaction with Cu(2+), the simultaneous extraction of PAHs and Cu(2+) and stepwise elution through tuning the eluent were successfully achieved, respectively. The SPE conditions affecting the extraction efficiency were optimized, including type and concentration of organic modifier, sample solution pH, flow rate, and volume. As a result of the special adsorption and desorption mechanism, high extraction efficiency was achieved with relative recoveries of 94.3-102.4% and relative standard deviations of less than 10.5%. The limits of detection were obtained with 0.4-3.1 ng L(-1) for PAHs and 15 ng L(-1) for Cu(2+), respectively. The method was applied to the analyses of PAHs and Cu(2+) in Xiliu Lake water samples collected in Zhengzhou, China.

Solid-phase extraction and purification of membrane proteins using a UV-modified PMMA microfluidic bioaffinity μSPE device.

PubMed

Battle, Katrina N; Jackson, Joshua M; Witek, Małgorzata A; Hupert, Mateusz L; Hunsucker, Sally A; Armistead, Paul M; Soper, Steven A

2014-03-21

We present a novel microfluidic solid-phase extraction (μSPE) device for the affinity enrichment of biotinylated membrane proteins from whole cell lysates. The device offers features that address challenges currently associated with the extraction and purification of membrane proteins from whole cell lysates, including the ability to release the enriched membrane protein fraction from the extraction surface so that they are available for downstream processing. The extraction bed was fabricated in PMMA using hot embossing and was comprised of 3600 micropillars. Activation of the PMMA micropillars by UV/O3 treatment permitted generation of surface-confined carboxylic acid groups and the covalent attachment of NeutrAvidin onto the μSPE device surfaces, which was used to affinity select biotinylated MCF-7 membrane proteins directly from whole cell lysates. The inclusion of a disulfide linker within the biotin moiety permitted release of the isolated membrane proteins via DTT incubation. Very low levels (∼20 fmol) of membrane proteins could be isolated and recovered with ∼89% efficiency with a bed capacity of 1.7 pmol. Western blotting indicated no traces of cytosolic proteins in the membrane protein fraction as compared to significant contamination using a commercial detergent-based method. We highlight future avenues for enhanced extraction efficiency and increased dynamic range of the μSPE device using computational simulations of different micropillar geometries to guide future device designs.

Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation

PubMed Central

Młynarski, Wiktor

2014-01-01

To date a number of studies have shown that receptive field shapes of early sensory neurons can be reproduced by optimizing coding efficiency of natural stimulus ensembles. A still unresolved question is whether the efficient coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. This paper proposes that the spatial selectivity of higher auditory neurons emerges as a direct consequence of learning efficient codes for natural binaural sounds. Firstly, it is demonstrated that a linear efficient coding transform—Independent Component Analysis (ICA) trained on spectrograms of naturalistic simulated binaural sounds extracts spatial information present in the signal. A simple hierarchical ICA extension allowing for decoding of sound position is proposed. Furthermore, it is shown that units revealing spatial selectivity can be learned from a binaural recording of a natural auditory scene. In both cases a relatively small subpopulation of learned spectrogram features suffices to perform accurate sound localization. Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment. PMID:24639644

Experience improves feature extraction in Drosophila.

PubMed

Peng, Yueqing; Xi, Wang; Zhang, Wei; Zhang, Ke; Guo, Aike

2007-05-09

Previous exposure to a pattern in the visual scene can enhance subsequent recognition of that pattern in many species from honeybees to humans. However, whether previous experience with a visual feature of an object, such as color or shape, can also facilitate later recognition of that particular feature from multiple visual features is largely unknown. Visual feature extraction is the ability to select the key component from multiple visual features. Using a visual flight simulator, we designed a novel protocol for visual feature extraction to investigate the effects of previous experience on visual reinforcement learning in Drosophila. We found that, after conditioning with a visual feature of objects among combinatorial shape-color features, wild-type flies exhibited poor ability to extract the correct visual feature. However, the ability for visual feature extraction was greatly enhanced in flies trained previously with that visual feature alone. Moreover, we demonstrated that flies might possess the ability to extract the abstract category of "shape" but not a particular shape. Finally, this experience-dependent feature extraction is absent in flies with defective MBs, one of the central brain structures in Drosophila. Our results indicate that previous experience can enhance visual feature extraction in Drosophila and that MBs are required for this experience-dependent visual cognition.

Improved system integration for integrated gasification combined cycle (IGCC) systems.

PubMed

Frey, H Christopher; Zhu, Yunhua

2006-03-01

Integrated gasification combined cycle (IGCC) systems are a promising technology for power generation. They include an air separation unit (ASU), a gasification system, and a gas turbine combined cycle power block, and feature competitive efficiency and lower emissions compared to conventional power generation technology. IGCC systems are not yet in widespread commercial use and opportunities remain to improve system feasibility via improved process integration. A process simulation model was developed for IGCC systems with alternative types of ASU and gas turbine integration. The model is applied to evaluate integration schemes involving nitrogen injection, air extraction, and combinations of both, as well as different ASU pressure levels. The optimal nitrogen injection only case in combination with an elevated pressure ASU had the highest efficiency and power output and approximately the lowest emissions per unit output of all cases considered, and thus is a recommended design option. The optimal combination of air extraction coupled with nitrogen injection had slightly worse efficiency, power output, and emissions than the optimal nitrogen injection only case. Air extraction alone typically produced lower efficiency, lower power output, and higher emissions than all other cases. The recommended nitrogen injection only case is estimated to provide annualized cost savings compared to a nonintegrated design. Process simulation modeling is shown to be a useful tool for evaluation and screening of technology options.

An expert botanical feature extraction technique based on phenetic features for identifying plant species.

PubMed

Kolivand, Hoshang; Fern, Bong Mei; Rahim, Mohd Shafry Mohd; Sulong, Ghazali; Baker, Thar; Tully, David

2018-01-01

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.

An expert botanical feature extraction technique based on phenetic features for identifying plant species

PubMed Central

Fern, Bong Mei; Rahim, Mohd Shafry Mohd; Sulong, Ghazali; Baker, Thar; Tully, David

2018-01-01

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost. PMID:29420568

Using an image-extended relational database to support content-based image retrieval in a PACS.

PubMed

Traina, Caetano; Traina, Agma J M; Araújo, Myrian R B; Bueno, Josiane M; Chino, Fabio J T; Razente, Humberto; Azevedo-Marques, Paulo M

2005-12-01

This paper presents a new Picture Archiving and Communication System (PACS), called cbPACS, which has content-based image retrieval capabilities. The cbPACS answers range and k-nearest- neighbor similarity queries, employing a relational database manager extended to support images. The images are compared through their features, which are extracted by an image-processing module and stored in the extended relational database. The database extensions were developed aiming at efficiently answering similarity queries by taking advantage of specialized indexing methods. The main concept supporting the extensions is the definition, inside the relational manager, of distance functions based on features extracted from the images. An extension to the SQL language enables the construction of an interpreter that intercepts the extended commands and translates them to standard SQL, allowing any relational database server to be used. By now, the system implemented works on features based on color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant regarding scale, translation and rotation of images and also to brightness transformations. The cbPACS is prepared to integrate new image features, based on texture and shape of the main objects in the image.

Feature-constrained surface reconstruction approach for point cloud data acquired with 3D laser scanner

NASA Astrophysics Data System (ADS)

Wang, Yongbo; Sheng, Yehua; Lu, Guonian; Tian, Peng; Zhang, Kai

2008-04-01

Surface reconstruction is an important task in the field of 3d-GIS, computer aided design and computer graphics (CAD & CG), virtual simulation and so on. Based on available incremental surface reconstruction methods, a feature-constrained surface reconstruction approach for point cloud is presented. Firstly features are extracted from point cloud under the rules of curvature extremes and minimum spanning tree. By projecting local sample points to the fitted tangent planes and using extracted features to guide and constrain the process of local triangulation and surface propagation, topological relationship among sample points can be achieved. For the constructed models, a process named consistent normal adjustment and regularization is adopted to adjust normal of each face so that the correct surface model is achieved. Experiments show that the presented approach inherits the convenient implementation and high efficiency of traditional incremental surface reconstruction method, meanwhile, it avoids improper propagation of normal across sharp edges, which means the applicability of incremental surface reconstruction is greatly improved. Above all, appropriate k-neighborhood can help to recognize un-sufficient sampled areas and boundary parts, the presented approach can be used to reconstruct both open and close surfaces without additional interference.

Extensions of algebraic image operators: An approach to model-based vision

NASA Technical Reports Server (NTRS)

Lerner, Bao-Ting; Morelli, Michael V.

1990-01-01

Researchers extend their previous research on a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets. Addition and multiplication are defined for the set of all grey-level images, which can then be described as polynomials of two variables. Utilizing this new algebraic structure, researchers devised an innovative, efficient edge detection scheme. An accurate method for deriving gradient component information from this edge detector is presented. Based upon this new edge detection system researchers developed a robust method for linear feature extraction by combining the techniques of a Hough transform and a line follower. The major advantage of this feature extractor is its general, object-independent nature. Target attributes, such as line segment lengths, intersections, angles of intersection, and endpoints are derived by the feature extraction algorithm and employed during model matching. The algebraic operators are global operations which are easily reconfigured to operate on any size or shape region. This provides a natural platform from which to pursue dynamic scene analysis. A method for optimizing the linear feature extractor which capitalizes on the spatially reconfiguration nature of the edge detector/gradient component operator is discussed.

Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices.

PubMed

Padma, A; Sukanesh, R

2013-01-01

A computer software system is designed for the segmentation and classification of benign from malignant tumour slices in brain computed tomography (CT) images. This paper presents a method to find and select both the dominant run length and co-occurrence texture features of region of interest (ROI) of the tumour region of each slice to be segmented by Fuzzy c means clustering (FCM) and evaluate the performance of support vector machine (SVM)-based classifiers in classifying benign and malignant tumour slices. Two hundred and six tumour confirmed CT slices are considered in this study. A total of 17 texture features are extracted by a feature extraction procedure, and six features are selected using Principal Component Analysis (PCA). This study constructed the SVM-based classifier with the selected features and by comparing the segmentation results with the experienced radiologist labelled ground truth (target). Quantitative analysis between ground truth and segmented tumour is presented in terms of segmentation accuracy, segmentation error and overlap similarity measures such as the Jaccard index. The classification performance of the SVM-based classifier with the same selected features is also evaluated using a 10-fold cross-validation method. The proposed system provides some newly found texture features have an important contribution in classifying benign and malignant tumour slices efficiently and accurately with less computational time. The experimental results showed that the proposed system is able to achieve the highest segmentation and classification accuracy effectiveness as measured by jaccard index and sensitivity and specificity.

A fast and automatic mosaic method for high-resolution satellite images

NASA Astrophysics Data System (ADS)

Chen, Hongshun; He, Hui; Xiao, Hongyu; Huang, Jing

2015-12-01

We proposed a fast and fully automatic mosaic method for high-resolution satellite images. First, the overlapped rectangle is computed according to geographical locations of the reference and mosaic images and feature points on both the reference and mosaic images are extracted by a scale-invariant feature transform (SIFT) algorithm only from the overlapped region. Then, the RANSAC method is used to match feature points of both images. Finally, the two images are fused into a seamlessly panoramic image by the simple linear weighted fusion method or other method. The proposed method is implemented in C++ language based on OpenCV and GDAL, and tested by Worldview-2 multispectral images with a spatial resolution of 2 meters. Results show that the proposed method can detect feature points efficiently and mosaic images automatically.

Fuzzy based finger vein recognition with rotation invariant feature matching

NASA Astrophysics Data System (ADS)

Ezhilmaran, D.; Joseph, Rose Bindu

2017-11-01

Finger vein recognition is a promising biometric with commercial applications which is explored widely in the recent years. In this paper, a finger vein recognition system is proposed using rotation invariant feature descriptors for matching after enhancing the finger vein images with an interval type-2 fuzzy method. SIFT features are extracted and matched using a matching score based on Euclidian distance. Rotation invariance of the proposed method is verified in the experiment and the results are compared with SURF matching and minutiae matching. It is seen that rotation invariance is verified and the poor quality issues are solved efficiently with the designed system of finger vein recognition during the analysis. The experiments underlines the robustness and reliability of the interval type-2 fuzzy enhancement and SIFT feature matching.

Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.

PubMed

Fiot, Jean-Baptiste; Cohen, Laurent D; Raniga, Parnesh; Fripp, Jurgen

2013-09-01

Support vector machines (SVM) are machine learning techniques that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities [T1-w, T2-w, proton-density and fluid attenuated inversion recovery(FLAIR)], differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of 0.54  ±  0.12, 0.72  ±  0.06 and 0.82  ±  0.06 respectively for small, moderate and severe lesion loads); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52  ±  0.13, 0.71  ±  0.08 and 0.81  ±  0.07). Furthermore, there was a negligible difference between using 5 × 5 × 5 and 3 × 3 × 3 features (p = 0.93). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification, which outperforms the one based on all features with post-processing. Copyright © 2013 John Wiley & Sons, Ltd.

Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech.

PubMed

Agarwalla, Swapna; Sarma, Kandarpa Kumar

2016-06-01

Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time. It is found that the proposed ML based sentence extraction techniques and the composite feature set used with RNN as classifier outperform all other approaches. By using ANN in FF form as feature extractor, the performance of the system is evaluated and a comparison is made. Experimental results show that the application of big data samples has enhanced the learning of the ASR system. Further, the ANN based sample and feature extraction techniques are found to be efficient enough to enable application of ML techniques in big data aspects as part of ASR systems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Structure learning in action

PubMed Central

Braun, Daniel A.; Mehring, Carsten; Wolpert, Daniel M.

2010-01-01

‘Learning to learn’ phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated—a process termed ‘learning to learn’. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a ‘learning to learn’ mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system. PMID:19720086

A Taxonomy of 3D Occluded Objects Recognition Techniques

NASA Astrophysics Data System (ADS)

Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

2016-03-01

The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

NASA Technical Reports Server (NTRS)

Li, Winston; Leung, Henry; Kwan, Chiman; Linnell, Bruce R.

2005-01-01

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Light extraction in planar light-emitting diode with nonuniform current injection: model and simulation.

PubMed

Khmyrova, Irina; Watanabe, Norikazu; Kholopova, Julia; Kovalchuk, Anatoly; Shapoval, Sergei

2014-07-20

We develop an analytical and numerical model for performing simulation of light extraction through the planar output interface of the light-emitting diodes (LEDs) with nonuniform current injection. Spatial nonuniformity of injected current is a peculiar feature of the LEDs in which top metal electrode is patterned as a mesh in order to enhance the output power of light extracted through the top surface. Basic features of the model are the bi-plane computation domain, related to other areas of numerical grid (NG) cells in these two planes, representation of light-generating layer by an ensemble of point light sources, numerical "collection" of light photons from the area limited by acceptance circle and adjustment of NG-cell areas in the computation procedure by the angle-tuned aperture function. The developed model and procedure are used to simulate spatial distributions of the output optical power as well as the total output power at different mesh pitches. The proposed model and simulation strategy can be very efficient in evaluation of the output optical performance of LEDs with periodical or symmetrical configuration of the electrodes.

A concept for extraction of habitat features from laser scanning and hypersprectral imaging for evaluation of Natura 2000 sites - the ChangeHabitats2 project approach

NASA Astrophysics Data System (ADS)

Székely, B.; Kania, A.; Pfeifer, N.; Heilmeier, H.; Tamás, J.; Szöllősi, N.; Mücke, W.

2012-04-01

The goal of the ChangeHabitats2 project is the development of cost- and time-efficient habitat assessment strategies by employing effective field work techniques supported by modern airborne remote sensing methods, i.e. hyperspectral imagery and laser scanning (LiDAR). An essential task of the project is the design of a novel field work technique that on the one hand fulfills the reporting requirements of the Flora-Fauna-Habitat (FFH-) directive and on the other hand serves as a reference for the aerial data analysis. Correlations between parameters derived from remotely sensed data and terrestrial field measurements shall be exploited in order to create half- or fully-automated methods for the extraction of relevant Natura2000 habitat parameters. As a result of these efforts a comprehensive conceptual model has been developed for extraction and integration of Natura 2000 relevant geospatial data. This scheme is an attempt to integrate various activities within ChangeHabitats2 project defining pathways of development, as well as encompassing existing data processing chains, theoretical approaches and field work. The conceptual model includes definition of processing levels (similar to those existing in remote sensing), whereas these levels cover the range from the raw data to the extracted habitat feature. For instance, the amount of dead wood (standing or lying on the surface) is an important evaluation criterion for the habitat. The tree trunks lying on the ground surface typically can be extracted from the LiDAR point cloud, and the amount of wood can be estimated accordingly. The final result will be considered as a habitat feature derived from laser scanning data. Furthermore, we are also interested not only in the determination of the specific habitat feature, but also in the detection of its variations (especially in deterioration). In this approach the variation of this important habitat feature is considered to be a differential habitat feature, that can be immediately used in the evaluation of the Natura 2000 sites. The goal of the project is the identification of many potential habitat features that can be extracted or implied from remotely sensed data, and the development of processing chains to provide data that can be used in the everyday field work of ecological site assessment. This is a contribution of ChangeHabitats2 project financed by the European Union within the Industry Academia Partnership Pathways (IAPP), as a part of FP7 Marie Curie Actions.

Computer-assisted diagnosis of melanoma.

PubMed

Fuller, Collin; Cellura, A Paul; Hibler, Brian P; Burris, Katy

2016-03-01

The computer-assisted diagnosis of melanoma is an exciting area of research where imaging techniques are combined with diagnostic algorithms in an attempt to improve detection and outcomes for patients with skin lesions suspicious for malignancy. Once an image has been acquired, it undergoes a processing pathway which includes preprocessing, enhancement, segmentation, feature extraction, feature selection, change detection, and ultimately classification. Practicality for everyday clinical use remains a vital question. A successful model must obtain results that are on par or outperform experienced dermatologists, keep costs at a minimum, be user-friendly, and be time efficient with high sensitivity and specificity. ©2015 Frontline Medical Communications.

Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

PubMed

Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

2017-08-01

This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

Multi-Stage System for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

The research of edge extraction and target recognition based on inherent feature of objects

NASA Astrophysics Data System (ADS)

Xie, Yu-chan; Lin, Yu-chi; Huang, Yin-guo

2008-03-01

Current research on computer vision often needs specific techniques for particular problems. Little use has been made of high-level aspects of computer vision, such as three-dimensional (3D) object recognition, that are appropriate for large classes of problems and situations. In particular, high-level vision often focuses mainly on the extraction of symbolic descriptions, and pays little attention to the speed of processing. In order to extract and recognize target intelligently and rapidly, in this paper we developed a new 3D target recognition method based on inherent feature of objects in which cuboid was taken as model. On the basis of analysis cuboid nature contour and greyhound distributing characteristics, overall fuzzy evaluating technique was utilized to recognize and segment the target. Then Hough transform was used to extract and match model's main edges, we reconstruct aim edges by stereo technology in the end. There are three major contributions in this paper. Firstly, the corresponding relations between the parameters of cuboid model's straight edges lines in an image field and in the transform field were summed up. By those, the aimless computations and searches in Hough transform processing can be reduced greatly and the efficiency is improved. Secondly, as the priori knowledge about cuboids contour's geometry character known already, the intersections of the component extracted edges are taken, and assess the geometry of candidate edges matches based on the intersections, rather than the extracted edges. Therefore the outlines are enhanced and the noise is depressed. Finally, a 3-D target recognition method is proposed. Compared with other recognition methods, this new method has a quick response time and can be achieved with high-level computer vision. The method present here can be used widely in vision-guide techniques to strengthen its intelligence and generalization, which can also play an important role in object tracking, port AGV, robots fields. The results of simulation experiments and theory analyzing demonstrate that the proposed method could suppress noise effectively, extracted target edges robustly, and achieve the real time need. Theory analysis and experiment shows the method is reasonable and efficient.

Feature extraction for document text using Latent Dirichlet Allocation

NASA Astrophysics Data System (ADS)

Prihatini, P. M.; Suryawan, I. K.; Mandia, IN

2018-01-01

Feature extraction is one of stages in the information retrieval system that used to extract the unique feature values of a text document. The process of feature extraction can be done by several methods, one of which is Latent Dirichlet Allocation. However, researches related to text feature extraction using Latent Dirichlet Allocation method are rarely found for Indonesian text. Therefore, through this research, a text feature extraction will be implemented for Indonesian text. The research method consists of data acquisition, text pre-processing, initialization, topic sampling and evaluation. The evaluation is done by comparing Precision, Recall and F-Measure value between Latent Dirichlet Allocation and Term Frequency Inverse Document Frequency KMeans which commonly used for feature extraction. The evaluation results show that Precision, Recall and F-Measure value of Latent Dirichlet Allocation method is higher than Term Frequency Inverse Document Frequency KMeans method. This shows that Latent Dirichlet Allocation method is able to extract features and cluster Indonesian text better than Term Frequency Inverse Document Frequency KMeans method.

Density-Gradient Mediated Band Extraction of Leukocytes from Whole Blood Using Centrifugo-Pneumatic Siphon Valving on Centrifugal Microfluidic Discs

PubMed Central

Kearney, Sinéad M.; Kilcawley, Niamh A.; Early, Philip L.; Glynn, Macdara T.; Ducrée, Jens

2016-01-01

Here we present retrieval of Peripheral Blood Mononuclear Cells by density-gradient medium based centrifugation for subsequent analysis of the leukocytes on an integrated microfluidic “Lab-on-a-Disc” cartridge. Isolation of white blood cells constitutes a critical sample preparation step for many bioassays. Centrifugo-pneumatic siphon valves are particularly suited for blood processing as they function without need of surface treatment and are ‘low-pass’, i.e., holding at high centrifugation speeds and opening upon reduction of the spin rate. Both ‘hydrostatically’ and ‘hydrodynamically’ triggered centrifugo-pneumatic siphon valving schemes are presented. Firstly, the geometry of the pneumatic chamber of hydrostatically primed centrifugo-pneumatic siphon valves is optimised to enable smooth and uniform layering of blood on top of the density-gradient medium; this feature proves to be key for efficient Peripheral Blood Mononuclear Cell extraction. A theoretical analysis of hydrostatically primed valves is also presented which determines the optimum priming pressure for the individual valves. Next, ‘dual siphon’ configurations for both hydrostatically and hydrodynamically primed centrifugo-pneumatic siphon valves are introduced; here plasma and Peripheral Blood Mononuclear Cells are extracted through a distinct siphon valve. This work represents a first step towards enabling on disc multi-parameter analysis. Finally, the efficiency of Peripheral Blood Mononuclear Cells extraction in these structures is characterised using a simplified design. A microfluidic mechanism, which we termed phase switching, is identified which affects the efficiency of Peripheral Blood Mononuclear Cell extraction. PMID:27167376

Driving profile modeling and recognition based on soft computing approach.

PubMed

Wahab, Abdul; Quek, Chai; Tan, Chin Keong; Takeda, Kazuya

2009-04-01

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

On Feature Extraction from Large Scale Linear LiDAR Data

NASA Astrophysics Data System (ADS)

Acharjee, Partha Pratim

Airborne light detection and ranging (LiDAR) can generate co-registered elevation and intensity map over large terrain. The co-registered 3D map and intensity information can be used efficiently for different feature extraction application. In this dissertation, we developed two algorithms for feature extraction, and usages of features for practical applications. One of the developed algorithms can map still and flowing waterbody features, and another one can extract building feature and estimate solar potential on rooftops and facades. Remote sensing capabilities, distinguishing characteristics of laser returns from water surface and specific data collection procedures provide LiDAR data an edge in this application domain. Furthermore, water surface mapping solutions must work on extremely large datasets, from a thousand square miles, to hundreds of thousands of square miles. National and state-wide map generation/upgradation and hydro-flattening of LiDAR data for many other applications are two leading needs of water surface mapping. These call for as much automation as possible. Researchers have developed many semi-automated algorithms using multiple semi-automated tools and human interventions. This reported work describes a consolidated algorithm and toolbox developed for large scale, automated water surface mapping. Geometric features such as flatness of water surface, higher elevation change in water-land interface and, optical properties such as dropouts caused by specular reflection, bimodal intensity distributions were some of the linear LiDAR features exploited for water surface mapping. Large-scale data handling capabilities are incorporated by automated and intelligent windowing, by resolving boundary issues and integrating all results to a single output. This whole algorithm is developed as an ArcGIS toolbox using Python libraries. Testing and validation are performed on a large datasets to determine the effectiveness of the toolbox and results are presented. Significant power demand is located in urban areas, where, theoretically, a large amount of building surface area is also available for solar panel installation. Therefore, property owners and power generation companies can benefit from a citywide solar potential map, which can provide available estimated annual solar energy at a given location. An efficient solar potential measurement is a prerequisite for an effective solar energy system in an urban area. In addition, the solar potential calculation from rooftops and building facades could open up a wide variety of options for solar panel installations. However, complex urban scenes make it hard to estimate the solar potential, partly because of shadows cast by the buildings. LiDAR-based 3D city models could possibly be the right technology for solar potential mapping. Although, most of the current LiDAR-based local solar potential assessment algorithms mainly address rooftop potential calculation, whereas building facades can contribute a significant amount of viable surface area for solar panel installation. In this paper, we introduce a new algorithm to calculate solar potential of both rooftop and building facades. Solar potential received by the rooftops and facades over the year are also investigated in the test area.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Improved EEG Event Classification Using Differential Energy.

PubMed

Harati, A; Golmohammadi, M; Lopez, S; Obeid, I; Picone, J

2015-12-01

Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24 % absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.

Robust feature extraction for rapid classification of damage in composites

NASA Astrophysics Data System (ADS)

Coelho, Clyde K.; Reynolds, Whitney; Chattopadhyay, Aditi

2009-03-01

The ability to detect anomalies in signals from sensors is imperative for structural health monitoring (SHM) applications. Many of the candidate algorithms for these applications either require a lot of training examples or are very computationally inefficient for large sample sizes. The damage detection framework presented in this paper uses a combination of Linear Discriminant Analysis (LDA) along with Support Vector Machines (SVM) to obtain a computationally efficient classification scheme for rapid damage state determination. LDA was used for feature extraction of damage signals from piezoelectric sensors on a composite plate and these features were used to train the SVM algorithm in parts, reducing the computational intensity associated with the quadratic optimization problem that needs to be solved during training. SVM classifiers were organized into a binary tree structure to speed up classification, which also reduces the total training time required. This framework was validated on composite plates that were impacted at various locations. The results show that the algorithm was able to correctly predict the different impact damage cases in composite laminates using less than 21 percent of the total available training data after data reduction.

Deterministic implementation of a bright, on-demand single photon source with near-unity indistinguishability via quantum dot imaging.

PubMed

He, Yu-Ming; Liu, Jin; Maier, Sebastian; Emmerling, Monika; Gerhardt, Stefan; Davanço, Marcelo; Srinivasan, Kartik; Schneider, Christian; Höfling, Sven

2017-07-20

Deterministic techniques enabling the implementation and engineering of bright and coherent solid-state quantum light sources are key for the reliable realization of a next generation of quantum devices. Such a technology, at best, should allow one to significantly scale up the number of implemented devices within a given processing time. In this work, we discuss a possible technology platform for such a scaling procedure, relying on the application of nanoscale quantum dot imaging to the pillar microcavity architecture, which promises to combine very high photon extraction efficiency and indistinguishability. We discuss the alignment technology in detail, and present the optical characterization of a selected device which features a strongly Purcell-enhanced emission output. This device, which yields an extraction efficiency of η = (49 ± 4) %, facilitates the emission of photons with (94 ± 2.7) % indistinguishability.

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

NASA Astrophysics Data System (ADS)

Taşkin Kaya, Gülşen

2013-10-01

Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The HDMR method is developed to improve the efficiency of the deducing high dimensional behaviors. The method is formed by a particular organization of low dimensional component functions, in which each function is the contribution of one or more input variables to the output variables.

A framework for feature extraction from hospital medical data with applications in risk prediction.

PubMed

Tran, Truyen; Luo, Wei; Phung, Dinh; Gupta, Sunil; Rana, Santu; Kennedy, Richard Lee; Larkins, Ann; Venkatesh, Svetha

2014-12-30

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Data of furfural adsorption on nano zero valent iron (NZVI) synthesized from Nettle extract.

PubMed

Fazlzadeh, Mehdi; Ansarizadeh, Mohammad; Leili, Mostafa

2018-02-01

Among various water and wastewater treatment methods, adsorption techniques are widely used to remove certain classes of pollutants due to its unique features. Thus, the aim of this data article is to synthesize zero valent iron nanoparticles (NZVI) from Nettle leaf extract by green synthesis method as an environmentally friendly technique, and to evaluate it's efficiency in the removal of furfural from aqueous solutions. The data of possible adsorption mechanism and isotherm of furfural on the synthesized adsorbent are depicted in this data article. The data acquired showed that the adsorption trend follows the pseudo-second order kinetic model and that the Langmuir isotherm was suitable for correlation of equilibrium data with the maximum adsorption capacity of 454.4 mg/g. The information of initial furfural concentration, pH, adsorbent dosage and contact time effects on the removal efficiency are presented. Considering the findings data, the developed nanoparticle from Nettle leaf extract, as a low cost adsorbent, could be considered as promising adsorbent for furfural and probably similar organic pollutants removal from aqueous solutions.

A tool for NDVI time series extraction from wide-swath remotely sensed images

NASA Astrophysics Data System (ADS)

Li, Zhishan; Shi, Runhe; Zhou, Cong

2015-09-01

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

Expression of a plant-derived peptide harboring water-cleaning and antimicrobial activities.

PubMed

Suarez, M; Entenza, J M; Doerries, C; Meyer, E; Bourquin, L; Sutherland, J; Marison, I; Moreillon, P; Mermod, N

2003-01-05

Drinking water is currently a scarce world resource, the preparation of which requires complex treatments that include clarification of suspended particles and disinfection. Seed extracts of Moringa oleifera Lam., a tropical tree, have been proposed as an environment-friendly alternative, due to their traditional use for the clarification of drinking water. However, the precise nature of the active components of the extract and whether they may be produced in recombinant form are unknown. Here we show that recombinant or synthetic forms of a cationic seed polypeptide mediate efficient sedimentation of suspended mineral particles and bacteria. Unexpectedly, the polypeptide was also found to possesses a bactericidal activity capable of disinfecting heavily contaminated water. Furthermore, the polypeptide has been shown to efficiently kill several pathogenic bacteria, including antibiotic-resistant isolates of Staphylococcus, Streptococcus, and Legionella species. Thus, this polypeptide displays the unprecedented feature of combining water purification and disinfectant properties. Identification of an active principle derived from the seed extracts points to a range of potential for drinking water treatment or skin and mucosal disinfection in clinical settings. Copyright 2002 Wiley Periodicals, Inc.

Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction

PubMed Central

Wu, Yueying; Liu, Pengyu; Gao, Yuan; Jia, Kebin

2016-01-01

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems. PMID:27814367

Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction.

PubMed

Wu, Yueying; Liu, Pengyu; Gao, Yuan; Jia, Kebin

2016-01-01

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems.

Efficient Method for Scalable Registration of Remote Sensing Images

NASA Astrophysics Data System (ADS)

Prouty, R.; LeMoigne, J.; Halem, M.

2017-12-01

The goal of this project is to build a prototype of a resource-efficient pipeline that will provide registration within subpixel accuracy of multitemporal Earth science data. Accurate registration of Earth-science data is imperative to proper data integration and seamless mosaicing of data from multiple times, sensors, and/or observation geometries. Modern registration methods make use of many arithmetic operations and sometimes require complete knowledge of the image domain. As such, while sensors become more advanced and are able to provide higher-resolution data, the memory resources required to properly register these data become prohibitive. The proposed pipeline employs a region of interest extraction algorithm in order to extract image subsets with high local feature density. These image subsets are then used to generate local solutions to the global registration problem. The local solutions are then 'globalized' to determine the deformation model that best solves the registration problem. The region of interest extraction and globalization routines are tested for robustness among the variety of scene-types and spectral locations provided by Earth-observing instruments such as Landsat, MODIS, or ASTER.

Comparative analysis of feature extraction methods in satellite imagery

NASA Astrophysics Data System (ADS)

Karim, Shahid; Zhang, Ye; Asif, Muhammad Rizwan; Ali, Saad

2017-10-01

Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Majzoobi, A.; Joshi, R. P., E-mail: ravi.joshi@ttu.edu; Neuber, A. A.

Particle-in-cell simulations are performed to analyze the efficiency, output power and leakage currents in a 12-Cavity, 12-Cathode rising-sun magnetron with diffraction output (MDO). The central goal is to conduct a parameter study of a rising-sun magnetron that comprehensively incorporates performance enhancing features such as transparent cathodes, axial extraction, the use of endcaps, and cathode extensions. Our optimum results demonstrate peak output power of about 2.1 GW, with efficiencies of ∼70% and low leakage currents at a magnetic field of 0.45 Tesla, a 400 kV bias with a single endcap, for a range of cathode extensions between 3 and 6 centimeters.

Microwave-assisted extraction of total bioactive saponin fraction from Gymnema sylvestre with reference to gymnemagenin: a potential biomarker.

PubMed

Mandal, Vivekananda; Dewanjee, Saikat; Mandal, Subhash C

2009-01-01

To develop a fast and ecofriendly microwave assisted extraction (MAE) technique for the effective and exhaustive extraction of gymnemagenin as an indicative biomarker for the quality control of Gymnema sylvestre. Several extraction parameters such as microwave power, extraction time, solvent composition, pre-leaching time, loading ratio and extraction cycle were studied for the determination of the optimum extraction condition. Scanning electron micrographs were obtained to elucidate the mechanism of extraction. The final optimum extraction conditions as obtained from the study were: 40% microwave power, 6 min irradiation time, 85% v/v methanol as the extraction solvent, 15 min pre-leaching time and 25 : 1 (mL/g) as the solvent-to-material loading ratio. The proposed extraction technique produced a maximum yield of 4.3% w/w gymnemagenin in 6 min which was 1.3, 2.5 and 1.95 times more efficient than 6 h of heat reflux, 24 h of maceration and stirring extraction, respectively. A synergistic heat and mass transfer theory was also proposed to support the extraction mechanism. Comparison with conventional extraction methods revealed that MAE could save considerable amounts of time and energy, whilst the reduction of volume of organic solvent consumed provides an ecofriendly feature.

Local-search based prediction of medical image registration error

NASA Astrophysics Data System (ADS)

Saygili, Görkem

2018-03-01

Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.

User-assisted video segmentation system for visual communication

NASA Astrophysics Data System (ADS)

Wu, Zhengping; Chen, Chun

2002-01-01

Video segmentation plays an important role for efficient storage and transmission in visual communication. In this paper, we introduce a novel video segmentation system using point tracking and contour formation techniques. Inspired by the results from the study of the human visual system, we intend to solve the video segmentation problem into three separate phases: user-assisted feature points selection, feature points' automatic tracking, and contour formation. This splitting relieves the computer of ill-posed automatic segmentation problems, and allows a higher level of flexibility of the method. First, the precise feature points can be found using a combination of user assistance and an eigenvalue-based adjustment. Second, the feature points in the remaining frames are obtained using motion estimation and point refinement. At last, contour formation is used to extract the object, and plus a point insertion process to provide the feature points for next frame's tracking.

Automatic extraction of planetary image features

NASA Technical Reports Server (NTRS)

LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

2013-01-01

A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

Event-driven processing for hardware-efficient neural spike sorting

NASA Astrophysics Data System (ADS)

Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

Audio fingerprint extraction for content identification

NASA Astrophysics Data System (ADS)

Shiu, Yu; Yeh, Chia-Hung; Kuo, C. C. J.

2003-11-01

In this work, we present an audio content identification system that identifies some unknown audio material by comparing its fingerprint with those extracted off-line and saved in the music database. We will describe in detail the procedure to extract audio fingerprints and demonstrate that they are robust to noise and content-preserving manipulations. The main feature in the proposed system is the zero-crossing rate extracted with the octave-band filter bank. The zero-crossing rate can be used to describe the dominant frequency in each subband with a very low computational cost. The size of audio fingerprint is small and can be efficiently stored along with the compressed files in the database. It is also robust to many modifications such as tempo change and time-alignment distortion. Besides, the octave-band filter bank is used to enhance the robustness to distortion, especially those localized on some frequency regions.

High efficiency power generation from coal and wastes utilizing high temperature air combustion technology (Part 1: Performance of pebble bed gasifier for coal and wastes)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kosaka, Hitoshi; Iwahashi, Takashi; Yoshida, Nobuhiro

1998-07-01

A new concept of a gasifier for coal and wastes is proposed where entrained bed and fixed pebble bed are combined. Main features of this pebble bed gasifier are high efficiency molten slag capture, high efficiency gasification and compactness. Coal and RFD combustion experiments using the pebble bed gasifier demonstrated high efficiency capture and continuous extraction of molten slag as well as complete char combustion with extra ordinarily short residence time of pulverized coal and crushed RDF at the temperature level of about 1,500 C within the pebble bed. Durability tests using high temperature electric furnace has shown that highmore » density alumna is a good candidate for pebble material.« less

Indexing and retrieval of MPEG compressed video

NASA Astrophysics Data System (ADS)

Kobla, Vikrant; Doermann, David S.

1998-04-01

To keep pace with the increased popularity of digital video as an archival medium, the development of techniques for fast and efficient analysis of ideo streams is essential. In particular, solutions to the problems of storing, indexing, browsing, and retrieving video data from large multimedia databases are necessary to a low access to these collections. Given that video is often stored efficiently in a compressed format, the costly overhead of decompression can be reduced by analyzing the compressed representation directly. In earlier work, we presented compressed domain parsing techniques which identified shots, subshots, and scenes. In this article, we present efficient key frame selection, feature extraction, indexing, and retrieval techniques that are directly applicable to MPEG compressed video. We develop a frame type independent representation which normalizes spatial and temporal features including frame type, frame size, macroblock encoding, and motion compensation vectors. Features for indexing are derived directly from this representation and mapped to a low- dimensional space where they can be accessed using standard database techniques. Spatial information is used as primary index into the database and temporal information is used to rank retrieved clips and enhance the robustness of the system. The techniques presented enable efficient indexing, querying, and retrieval of compressed video as demonstrated by our system which typically takes a fraction of a second to retrieve similar video scenes from a database, with over 95 percent recall.

Joint and collaborative representation with local Volterra kernels convolution feature for face recognition

NASA Astrophysics Data System (ADS)

Feng, Guang; Li, Hengjian; Dong, Jiwen; Chen, Xi; Yang, Huiru

2018-04-01

In this paper, we proposed a joint and collaborative representation with Volterra kernel convolution feature (JCRVK) for face recognition. Firstly, the candidate face images are divided into sub-blocks in the equal size. The blocks are extracted feature using the two-dimensional Voltera kernels discriminant analysis, which can better capture the discrimination information from the different faces. Next, the proposed joint and collaborative representation is employed to optimize and classify the local Volterra kernels features (JCR-VK) individually. JCR-VK is very efficiently for its implementation only depending on matrix multiplication. Finally, recognition is completed by using the majority voting principle. Extensive experiments on the Extended Yale B and AR face databases are conducted, and the results show that the proposed approach can outperform other recently presented similar dictionary algorithms on recognition accuracy.

Lipase-mediated lipid removal from propolis extract and its antiradical and antimicrobial activity.

PubMed

Park, Hyein; Bae, Song Hwan; Park, Yooheon; Choi, Hyeon-Son; Suh, Hyung Joo

2015-06-01

Propolis contains many antioxidants such as polyphenols and flavonoids. However, propolis-derived lipid components interrupt an efficient isolation of antioxidants from propolis extract. We examined the effectiveness of various lipase treatments for the removal of lipids from propolis extract and evaluated the biological features of the extract. Lipase OF and Novozyme 435 treatments did not reduce fatty acid level in propolis extract. However, Lipozyme TL IM-treated propolis extract showed a significant decrease in fatty acid level, suggesting the removal of lipids. Lipozyme RM IM also significantly decreased the fatty acid level of the extract, but was accompanied by the reduction of polyphenols and flavonoids, which are antioxidants. In Lipozyme TL IM treatment, an increase in active flavonoids, such as Artepillin C and kaempferide, was observed, with a slight increase of ferric reducing/antioxidant power (FRAP) radical-scavenging activity. In addition, antimicrobial activity towards skin health-related bacteria such as Staphylococcus epidermidis and Propionibacterium acnes was enhanced by Lipozyme TL IM treatment. Lipozyme TL IM treatment effectively removes lipids from propolis extract and enhances antibacterial activity. Therefore, we suggest that Lipozyme TL IM is a useful lipase for lipid removal of propolis extract. © 2014 Society of Chemical Industry.

Efficient Feature Extraction and Likelihood Fusion for Vehicle Tracking in Low Frame Rate Airborne Video

DTIC Science & Technology

2010-07-01

imagery, persistent sensor array I. Introduction New device fabrication technologies and heterogeneous embedded processors have led to the emergence of a...geometric occlusions between target and sensor , motion blur, urban scene complexity, and high data volumes. In practical terms the targets are small...distributed airborne narrow-field-of-view video sensor networks. Airborne camera arrays combined with com- putational photography techniques enable the

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

NASA Astrophysics Data System (ADS)

Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

Recent Advances in the Separation of Rare Earth Elements Using Mesoporous Hybrid Materials.

PubMed

Hu, Yimu; Florek, Justyna; Larivière, Dominic; Fontaine, Frédéric-Georges; Kleitz, Freddy

2018-05-27

Over the past decades, the need for rare earth elements (REEs) has increased substantially, mostly because these elements are used as valuable additives in advanced technologies. However, the difference in ionic radius between neighboring REEs is small, which renders an efficient sized-based separation extremely challenging. Among different types of extraction methods, solid-phase extraction (SPE) is a promising candidate, featuring high enrichment factor, rapid adsorption kinetics, reduced solvent consumption and minimized waste generation. The great challenge remains yet to develop highly efficient and selective adsorbents for this process. In this regard, ordered mesoporous materials (OMMs) possess high specific surface area, tunable pore size, large pore volume, as well as stable and interconnected frameworks with active pore surfaces for functionalization. Such features meet the requirements for enhanced adsorbents, not only providing huge reactional interface and large surface capable of accommodating guest species, but also enabling the possibility of ion-specific binding for enrichment and separation purposes. This short personal account summarizes some of the recent advances in the use of porous hybrid materials as selective sorbents for REE separation and purification, with particular attention devoted to ordered mesoporous silica and carbon-based sorbents. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Fuzzy Relational Compression Applied on Feature Vectors for Infant Cry Recognition

NASA Astrophysics Data System (ADS)

Reyes-Galaviz, Orion Fausto; Reyes-García, Carlos Alberto

Data compression is always advisable when it comes to handling and processing information quickly and efficiently. There are two main problems that need to be solved when it comes to handling data; store information in smaller spaces and processes it in the shortest possible time. When it comes to infant cry analysis (ICA), there is always the need to construct large sound repositories from crying babies. Samples that have to be analyzed and be used to train and test pattern recognition algorithms; making this a time consuming task when working with uncompressed feature vectors. In this work, we show a simple, but efficient, method that uses Fuzzy Relational Product (FRP) to compresses the information inside a feature vector, building with this a compressed matrix that will help us recognize two kinds of pathologies in infants; Asphyxia and Deafness. We describe the sound analysis, which consists on the extraction of Mel Frequency Cepstral Coefficients that generate vectors which will later be compressed by using FRP. There is also a description of the infant cry database used in this work, along with the training and testing of a Time Delay Neural Network with the compressed features, which shows a performance of 96.44% with our proposed feature vector compression.

Practical low-cost visual communication using binary images for deaf sign language.

PubMed

Manoranjan, M D; Robinson, J A

2000-03-01

Deaf sign language transmitted by video requires a temporal resolution of 8 to 10 frames/s for effective communication. Conventional videoconferencing applications, when operated over low bandwidth telephone lines, provide very low temporal resolution of pictures, of the order of less than a frame per second, resulting in jerky movement of objects. This paper presents a practical solution for sign language communication, offering adequate temporal resolution of images using moving binary sketches or cartoons, implemented on standard personal computer hardware with low-cost cameras and communicating over telephone lines. To extract cartoon points an efficient feature extraction algorithm adaptive to the global statistics of the image is proposed. To improve the subjective quality of the binary images, irreversible preprocessing techniques, such as isolated point removal and predictive filtering, are used. A simple, efficient and fast recursive temporal prefiltering scheme, using histograms of successive frames, reduces the additive and multiplicative noise from low-cost cameras. An efficient three-dimensional (3-D) compression scheme codes the binary sketches. Subjective tests performed on the system confirm that it can be used for sign language communication over telephone lines.

Automated Extraction of Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne (Technical Monitor); Haimes, Robert

2005-01-01

Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process of most fluid handling devices. In order to efficiently and effectively use the results of a CFD simulation, visualization tools are often used. These tools are used in all stages of the CFD simulation including pre-processing, interim-processing, and post-processing, to interpret the results. Each of these stages requires visualization tools that allow one to examine the geometry of the device, as well as the partial or final results of the simulation. An engineer will typically generate a series of contour and vector plots to better understand the physics of how the fluid is interacting with the physical device. Of particular interest are detecting features such as shocks, re-circulation zones, and vortices (which will highlight areas of stress and loss). As the demand for CFD analyses continues to increase the need for automated feature extraction capabilities has become vital. In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like; isc-surface, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snapshot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments). Methods must be developed to abstract the feature of interest and display it in a manner that physically makes sense.

Automated Extraction of Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne (Technical Monitor); Haimes, Robert

2004-01-01

Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process of most fluid handling devices. In order to efficiently and effectively use the results of a CFD simulation, visualization tools are often used. These tools are used in all stages of the CFD simulation including pre-processing, interim-processing, and post-processing, to interpret the results. Each of these stages requires visualization tools that allow one to examine the geometry of the device, as well as the partial or final results of the simulation. An engineer will typically generate a series of contour and vector plots to better understand the physics of how the fluid is interacting with the physical device. Of particular interest are detecting features such as shocks, recirculation zones, and vortices (which will highlight areas of stress and loss). As the demand for CFD analyses continues to increase the need for automated feature extraction capabilities has become vital. In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like; iso-surface, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snapshot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for (co-processing environments). Methods must be developed to abstract the feature of interest and display it in a manner that physically makes sense.

ECG Identification System Using Neural Network with Global and Local Features

ERIC Educational Resources Information Center

Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

2016-01-01

This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Exploring convolutional neural networks for drug–drug interaction extraction

PubMed Central

Segura-Bedmar, Isabel; Martínez, Paloma

2017-01-01

Abstract Drug–drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which usually require considerable time and effort. One possible alternative to these approaches includes deep learning. This technique aims to automatically learn the best feature representation from the input data for a given task. The purpose of this paper is to examine whether a convolutional neural network (CNN), which only uses word embeddings as input features, can be applied successfully to classify DDIs from biomedical texts. Proposed herein, is a CNN architecture with only one hidden layer, thus making the model more computationally efficient, and we perform detailed experiments in order to determine the best settings of the model. The goal is to determine the best parameter of this basic CNN that should be considered for future research. The experimental results show that the proposed approach is promising because it attained the second position in the 2013 rankings of the DDI extraction challenge. However, it obtained worse results than previous works using neural networks with more complex architectures. PMID:28605776

Method and Apparatus for Automated Isolation of Nucleic Acids from Small Cell Samples

NASA Technical Reports Server (NTRS)

Sundaram, Shivshankar; Prabhakarpandian, Balabhaskar; Pant, Kapil; Wang, Yi

2014-01-01

RNA isolation is a ubiquitous need, driven by current emphasis on microarrays and miniaturization. With commercial systems requiring 100,000 to 1,000,000 cells for successful isolation, there is a growing need for a small-footprint, easy-to-use device that can harvest nucleic acids from much smaller cell samples (1,000 to 10,000 cells). The process of extraction of RNA from cell cultures is a complex, multi-step one, and requires timed, asynchronous operations with multiple reagents/buffers. An added complexity is the fragility of RNA (subject to degradation) and its reactivity to surface. A novel, microfluidics-based, integrated cartridge has been developed that can fully automate the complex process of RNA isolation (lyse, capture, and elute RNA) from small cell culture samples. On-cartridge cell lysis is achieved using either reagents or high-strength electric fields made possible by the miniaturized format. Traditionally, silica-based, porous-membrane formats have been used for RNA capture, requiring slow perfusion for effective capture. In this design, high efficiency capture/elution are achieved using a microsphere-based "microfluidized" format. Electrokinetic phenomena are harnessed to actively mix microspheres with the cell lysate and capture/elution buffer, providing important advantages in extraction efficiency, processing time, and operational flexibility. Successful RNA isolation was demonstrated using both suspension (HL-60) and adherent (BHK-21) cells. Novel features associated with this development are twofold. First, novel designs that execute needed processes with improved speed and efficiency were developed. These primarily encompass electric-field-driven lysis of cells. The configurations include electrode-containing constructs, or an "electrode-less" chip design, which is easy to fabricate and mitigates fouling at the electrode surface; and the "fluidized" extraction format based on electrokinetically assisted mixing and contacting of microbeads in a shape-optimized chamber. A secondary proprietary feature is in the particular layout integrating these components to perform the desired operation of RNA isolation. Apart from a novel functional capability, advantages of the innovation include reduced or eliminated use of toxic reagents, and operator-independent extraction of RNA.

High-efficiency Light-emitting Devices based on Semipolar III-Nitrides

NASA Astrophysics Data System (ADS)

Oh, Sang Ho

In the future, the light-emitting diodes (LEDs) are expected to fully penetrate into the lighting market. A tremendous amount of energy will be saved through the LED-based lighting. Apparently, the amount of the energy saving strongly depends on the efficiency of the LEDs: this dissertation is all about the efficiency. First, the III-nitride LEDs grown on free-standing semipolar (202¯1¯) GaN substrates will be discussed. In many studies, LEDs grown on semipolar III-nitride substrates exhibited high efficiency at high current density. In this dissertation, "droop-free" (202¯1¯) blue LEDs will be demonstrated, especially for the standard industrial chip size. In addition, contact optimization process for (202¯1¯) LEDs will be discussed. Series resistance of the (202¯1¯) LED devices has been improved through the contact optimization. As a result, the wall-plug efficiency (WPE) of the device was boosted by ˜50%, compared to that of the previously reported (202¯1¯) LEDs. Also, chip shaping for the semipolar LEDs to enhance the extraction efficiency will be covered as well. A new mesa design will be introduced, and the cleaving scheme for semipolar LED wafers will be thoroughly discussed. Lastly, as a future work, selective area growth of ZnO light extraction features will be introduced and its preliminary result will be demonstrated.

Reduced multiple empirical kernel learning machine.

PubMed

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3) this paper adopts the Gauss Elimination, one of the on-the-shelf techniques, to generate a basis of the original feature space, which is stable and efficient.

Efficient method of protein extraction from Theobroma cacao L. roots for two-dimensional gel electrophoresis and mass spectrometry analyses.

PubMed

Bertolde, F Z; Almeida, A-A F; Silva, F A C; Oliveira, T M; Pirovani, C P

2014-07-04

Theobroma cacao is a woody and recalcitrant plant with a very high level of interfering compounds. Standard protocols for protein extraction were proposed for various types of samples, but the presence of interfering compounds in many samples prevented the isolation of proteins suitable for two-dimensional gel electrophoresis (2-DE). An efficient method to extract root proteins for 2-DE was established to overcome these problems. The main features of this protocol are: i) precipitation with trichloroacetic acid/acetone overnight to prepare the acetone dry powder (ADP), ii) several additional steps of sonication in the ADP preparation and extractions with dense sodium dodecyl sulfate and phenol, and iii) adding two stages of phenol extractions. Proteins were extracted from roots using this new protocol (Method B) and a protocol described in the literature for T. cacao leaves and meristems (Method A). Using these methods, we obtained a protein yield of about 0.7 and 2.5 mg per 1.0 g lyophilized root, and a total of 60 and 400 spots could be separated, respectively. Through Method B, it was possible to isolate high-quality protein and a high yield of roots from T. cacao for high-quality 2-DE gels. To demonstrate the quality of the extracted proteins from roots of T. cacao using Method B, several protein spots were cut from the 2-DE gels, analyzed by tandem mass spectrometry, and identified. Method B was further tested on Citrus roots, with a protein yield of about 2.7 mg per 1.0 g lyophilized root and 800 detected spots.

The technique of entropy optimization in motor current signature analysis and its application in the fault diagnosis of gear transmission

NASA Astrophysics Data System (ADS)

Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong

2012-05-01

Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Xiaojia; Mao Qirong; Zhan Yongzhao

There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions.more » The experiments show that this method can improve the recognition rate and the time of feature extraction.« less

Pigment network-based skin cancer detection.

PubMed

Alfed, Naser; Khelifi, Fouad; Bouridane, Ahmed; Seker, Huseyin

2015-08-01

Diagnosing skin cancer in its early stages is a challenging task for dermatologists given the fact that the chance for a patient's survival is higher and hence the process of analyzing skin images and making decisions should be time efficient. Therefore, diagnosing the disease using automated and computerized systems has nowadays become essential. This paper proposes an efficient system for skin cancer detection on dermoscopic images. It has been shown that the statistical characteristics of the pigment network, extracted from the dermoscopic image, could be used as efficient discriminating features for cancer detection. The proposed system has been assessed on a dataset of 200 dermoscopic images of the `Hospital Pedro Hispano' [1] and the results of cross-validation have shown high detection accuracy.

Concept of turbines for ultrasupercritical, supercritical, and subcritical steam conditions

NASA Astrophysics Data System (ADS)

Mikhailov, V. E.; Khomenok, L. A.; Pichugin, I. I.; Kovalev, I. A.; Bozhko, V. V.; Vladimirskii, O. A.; Zaitsev, I. V.; Kachuriner, Yu. Ya.; Nosovitskii, I. A.; Orlik, V. G.

2017-11-01

The article describes the design features of condensing turbines for ultrasupercritical initial steam conditions (USSC) and large-capacity cogeneration turbines for super- and subcritical steam conditions having increased steam extractions for district heating purposes. For improving the efficiency and reliability indicators of USSC turbines, it is proposed to use forced cooling of the head high-temperature thermally stressed parts of the high- and intermediate-pressure rotors, reaction-type blades of the high-pressure cylinder (HPC) and at least the first stages of the intermediate-pressure cylinder (IPC), the double-wall HPC casing with narrow flanges of its horizontal joints, a rigid HPC rotor, an extended system of regenerative steam extractions without using extractions from the HPC flow path, and the low-pressure cylinder's inner casing moving in accordance with the IPC thermal expansions. For cogeneration turbines, it is proposed to shift the upper district heating extraction (or its significant part) to the feedwater pump turbine, which will make it possible to improve the turbine plant efficiency and arrange both district heating extractions in the IPC. In addition, in the case of using a disengaging coupling or precision conical bolts in the coupling, this solution will make it possible to disconnect the LPC in shifting the turbine to operate in the cogeneration mode. The article points out the need to intensify turbine development efforts with the use of modern methods for improving their efficiency and reliability involving, in particular, the use of relatively short 3D blades, last stages fitted with longer rotor blades, evaporation techniques for removing moisture in the last-stage diaphragm, and LPC rotor blades with radial grooves on their leading edges.

Depth-time interpolation of feature trends extracted from mobile microelectrode data with kernel functions.

PubMed

Wong, Stephen; Hargreaves, Eric L; Baltuch, Gordon H; Jaggi, Jurg L; Danish, Shabbar F

2012-01-01

Microelectrode recording (MER) is necessary for precision localization of target structures such as the subthalamic nucleus during deep brain stimulation (DBS) surgery. Attempts to automate this process have produced quantitative temporal trends (feature activity vs. time) extracted from mobile MER data. Our goal was to evaluate computational methods of generating spatial profiles (feature activity vs. depth) from temporal trends that would decouple automated MER localization from the clinical procedure and enhance functional localization in DBS surgery. We evaluated two methods of interpolation (standard vs. kernel) that generated spatial profiles from temporal trends. We compared interpolated spatial profiles to true spatial profiles that were calculated with depth windows, using correlation coefficient analysis. Excellent approximation of true spatial profiles is achieved by interpolation. Kernel-interpolated spatial profiles produced superior correlation coefficient values at optimal kernel widths (r = 0.932-0.940) compared to standard interpolation (r = 0.891). The choice of kernel function and kernel width resulted in trade-offs in smoothing and resolution. Interpolation of feature activity to create spatial profiles from temporal trends is accurate and can standardize and facilitate MER functional localization of subcortical structures. The methods are computationally efficient, enhancing localization without imposing additional constraints on the MER clinical procedure during DBS surgery. Copyright © 2012 S. Karger AG, Basel.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

2017-02-01

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Convex Hull Aided Registration Method (CHARM).

PubMed

Fan, Jingfan; Yang, Jian; Zhao, Yitian; Ai, Danni; Liu, Yonghuai; Wang, Ge; Wang, Yongtian

2017-09-01

Non-rigid registration finds many applications such as photogrammetry, motion tracking, model retrieval, and object recognition. In this paper we propose a novel convex hull aided registration method (CHARM) to match two point sets subject to a non-rigid transformation. First, two convex hulls are extracted from the source and target respectively. Then, all points of the point sets are projected onto the reference plane through each triangular facet of the hulls. From these projections, invariant features are extracted and matched optimally. The matched feature point pairs are mapped back onto the triangular facets of the convex hulls to remove outliers that are outside any relevant triangular facet. The rigid transformation from the source to the target is robustly estimated by the random sample consensus (RANSAC) scheme through minimizing the distance between the matched feature point pairs. Finally, these feature points are utilized as the control points to achieve non-rigid deformation in the form of thin-plate spline of the entire source point set towards the target one. The experimental results based on both synthetic and real data show that the proposed algorithm outperforms several state-of-the-art ones with respect to sampling, rotational angle, and data noise. In addition, the proposed CHARM algorithm also shows higher computational efficiency compared to these methods.

The formation method of the feature space for the identification of fatigued bills

NASA Astrophysics Data System (ADS)

Kang, Dongshik; Oshiro, Ayumu; Ozawa, Kenji; Mitsui, Ikugo

2014-10-01

Fatigued bills make a trouble such as the paper jam in a bill handling machine. In the discrimination of fatigued bills using an acoustic signal, the variation of an observed bill sound is considered to be one of causes in misclassification. Therefore a technique has demanded in order to make the classification of fatigued bills more efficient. In this paper, we proposed the algorithm that extracted feature quantity of bill sound from acoustic signal using the frequency difference, and carried out discrimination experiment of fatigued bill money by Support Vector Machine(SVM). The feature quantity of frequency difference can represent the frequency components of an acoustic signal is varied by the fatigued degree of bill money. The generalization performance of SVM does not depend on the size of dimensions of the feature space, even in a high dimensional feature space such as bill-acoustic signals. Furthermore, SVM can induce an optimal classifier which considers the combination of features by the virtue of polynomial kernel functions.

Spatiotemporal modelling of groundwater extraction in semi-arid central Queensland, Australia

NASA Astrophysics Data System (ADS)

Keir, Greg; Bulovic, Nevenka; McIntyre, Neil

2016-04-01

The semi-arid Surat Basin in central Queensland, Australia, forms part of the Great Artesian Basin, a groundwater resource of national significance. While this area relies heavily on groundwater supply bores to sustain agricultural industries and rural life in general, measurement of groundwater extraction rates is very limited. Consequently, regional groundwater extraction rates are not well known, which may have implications for regional numerical groundwater modelling. However, flows from a small number of bores are metered, and less precise anecdotal estimates of extraction are increasingly available. There is also an increasing number of other spatiotemporal datasets which may help predict extraction rates (e.g. rainfall, temperature, soils, stocking rates etc.). These can be used to construct spatial multivariate regression models to estimate extraction. The data exhibit complicated statistical features, such as zero-valued observations, non-Gaussianity, and non-stationarity, which limit the use of many classical estimation techniques, such as kriging. As well, water extraction histories may exhibit temporal autocorrelation. To account for these features, we employ a separable space-time model to predict bore extraction rates using the R-INLA package for computationally efficient Bayesian inference. A joint approach is used to model both the probability (using a binomial likelihood) and magnitude (using a gamma likelihood) of extraction. The correlation between extraction rates in space and time is modelled using a Gaussian Markov Random Field (GMRF) with a Matérn spatial covariance function which can evolve over time according to an autoregressive model. To reduce computational burden, we allow the GMRF to be evaluated at a relatively coarse temporal resolution, while still allowing predictions to be made at arbitrarily small time scales. We describe the process of model selection and inference using an information criterion approach, and present some preliminary results from the study area. We conclude by discussing issues related with upscaling of the modelling approach to the entire basin, including merging of extraction rate observations with different precision, temporal resolution, and even potentially different likelihoods.

High-frequency combination coding-based steady-state visual evoked potential for brain computer interface

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Feng; Zhang, Xin; Xie, Jun

2015-03-10

This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The goal of this study is to increase the number of targets using fewer stimulation high frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. The new paradigm is High-Frequency Combination Coding-Based High-Frequency Steady-State Visual Evoked Potential (HFCC-SSVEP).Firstly, we studied SSVEP high frequency(beyond 25 Hz)response of SSVEP, whose paradigm is presented on the LED. The SNR (Signal to Noise Ratio) of high frequency(beyond 40 Hz) response is very low, which is been unable to be distinguished through the traditional analysis method;more » Secondly we investigated the HFCC-SSVEP response (beyond 25 Hz) for 3 frequencies (25Hz, 33.33Hz, and 40Hz), HFCC-SSVEP produces n{sup n} with n high stimulation frequencies through Frequence Combination Code. Further, Animproved Hilbert-huang transform (IHHT)-based variable frequency EEG feature extraction method and a local spectrum extreme target identification algorithmare adopted to extract time-frequency feature of the proposed HFCC-SSVEP response.Linear predictions and fixed sifting (iterating) 10 time is used to overcome the shortage of end effect and stopping criterion,generalized zero-crossing (GZC) is used to compute the instantaneous frequency of the proposed SSVEP respondent signals, the improved HHT-based feature extraction method for the proposed SSVEP paradigm in this study increases recognition efficiency, so as to improve ITR and to increase the stability of the BCI system. what is more, SSVEPs evoked by high-frequency stimuli (beyond 25Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures, So as to ensure the system efficiency and undamaging.This study tests three subjects in order to verify the feasibility of the proposed method.« less

Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-Throughput Microscopy at Low Temporal Resolution

PubMed Central

Downey, Mike J.; Jeziorska, Danuta M.; Ott, Sascha; Tamai, T. Katherine; Koentges, Georgy; Vance, Keith W.; Bretschneider, Till

2011-01-01

The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers. PMID:22194797

Efficient local representations for three-dimensional palmprint recognition

NASA Astrophysics Data System (ADS)

Yang, Bing; Wang, Xiaohua; Yao, Jinliang; Yang, Xin; Zhu, Wenhua

2013-10-01

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

Efficient iris recognition by characterizing key local variations.

PubMed

Ma, Li; Tan, Tieniu; Wang, Yunhong; Zhang, Dexin

2004-06-01

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

Image segmentation-based robust feature extraction for color image watermarking

NASA Astrophysics Data System (ADS)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

NASA Astrophysics Data System (ADS)

Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

2015-07-01

Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

PubMed Central

Ma, Suliang; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

2018-01-01

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods. PMID:29659548

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

Fast object detection algorithm based on HOG and CNN

NASA Astrophysics Data System (ADS)

Lu, Tongwei; Wang, Dandan; Zhang, Yanduo

2018-04-01

In the field of computer vision, object classification and object detection are widely used in many fields. The traditional object detection have two main problems:one is that sliding window of the regional selection strategy is high time complexity and have window redundancy. And the other one is that Robustness of the feature is not well. In order to solve those problems, Regional Proposal Network (RPN) is used to select candidate regions instead of selective search algorithm. Compared with traditional algorithms and selective search algorithms, RPN has higher efficiency and accuracy. We combine HOG feature and convolution neural network (CNN) to extract features. And we use SVM to classify. For TorontoNet, our algorithm's mAP is 1.6 percentage points higher. For OxfordNet, our algorithm's mAP is 1.3 percentage higher.

Fault diagnosis of helical gearbox using acoustic signal and wavelets

NASA Astrophysics Data System (ADS)

Pranesh, SK; Abraham, Siju; Sugumaran, V.; Amarnath, M.

2017-05-01

The efficient transmission of power in machines is needed and gears are an appropriate choice. Faults in gears result in loss of energy and money. The monitoring and fault diagnosis are done by analysis of the acoustic and vibrational signals which are generally considered to be unwanted by products. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Artificial faults were created and subsequently signals were captured by a microphone. An extensive study using different wavelet transformations for feature extraction from the acoustic signals was done, followed by waveletselection and feature selection using J48 decision tree and feature classification was performed using K star algorithm. Classification accuracy of 100% was obtained in the study

Hexagonal wavelet processing of digital mammography

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Schuler, Sergio; Huda, Walter; Honeyman-Buck, Janice C.; Steinbach, Barbara G.

1993-09-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms and used to enhance features of importance to mammography within a continuum of scale-space. We present a method of contrast enhancement based on an overcomplete, non-separable multiscale representation: the hexagonal wavelet transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by local and global non-linear operators. Multiscale edges identified within distinct levels of transform space provide local support for enhancement. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

Improved initial guess with semi-subpixel level accuracy in digital image correlation by feature-based method

NASA Astrophysics Data System (ADS)

Zhang, Yunlu; Yan, Lei; Liou, Frank

2018-05-01

The quality initial guess of deformation parameters in digital image correlation (DIC) has a serious impact on convergence, robustness, and efficiency of the following subpixel level searching stage. In this work, an improved feature-based initial guess (FB-IG) scheme is presented to provide initial guess for points of interest (POIs) inside a large region. Oriented FAST and Rotated BRIEF (ORB) features are semi-uniformly extracted from the region of interest (ROI) and matched to provide initial deformation information. False matched pairs are eliminated by the novel feature guided Gaussian mixture model (FG-GMM) point set registration algorithm, and nonuniform deformation parameters of the versatile reproducing kernel Hilbert space (RKHS) function are calculated simultaneously. Validations on simulated images and real-world mini tensile test verify that this scheme can robustly and accurately compute initial guesses with semi-subpixel level accuracy in cases with small or large translation, deformation, or rotation.

Edge enhancement and noise suppression for infrared image based on feature analysis

NASA Astrophysics Data System (ADS)

Jiang, Meng

2018-06-01

Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

PubMed Central

Liu, Xiaojing; Ser, Zheng; Cluntun, Ahmad A.; Mentch, Samantha J.; Locasale, Jason W.

2014-01-01

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.  Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases. PMID:24894601

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

NASA Technical Reports Server (NTRS)

Solarna, David; Moser, Gabriele; Le Moigne-Stewart, Jacqueline; Serpico, Sebastiano B.

2017-01-01

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Age group classification and gender detection based on forced expiratory spirometry.

PubMed

Cosgun, Sema; Ozbek, I Yucel

2015-08-01

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

Bayesian decoding using unsorted spikes in the rat hippocampus

PubMed Central

Layton, Stuart P.; Chen, Zhe; Wilson, Matthew A.

2013-01-01

A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces. PMID:24089403

Online particle detection with Neural Networks based on topological calorimetry information

NASA Astrophysics Data System (ADS)

Ciodaro, T.; Deva, D.; de Seixas, J. M.; Damazio, D.

2012-06-01

This paper presents the latest results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction using the ATLAS calorimetry information (energy measurements). The extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time by 59%. Also, the total memory necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount.

Automatic movie skimming with general tempo analysis

NASA Astrophysics Data System (ADS)

Lee, Shih-Hung; Yeh, Chia-Hung; Kuo, C. C. J.

2003-11-01

Story units are extracted by general tempo analysis including tempos analysis including tempos of audio and visual information in this research. Although many schemes have been proposed to successfully segment video data into shots using basic low-level features, how to group shots into meaningful units called story units is still a challenging problem. By focusing on a certain type of video such as sport or news, we can explore models with the specific application domain knowledge. For movie contents, many heuristic rules based on audiovisual clues have been proposed with limited success. We propose a method to extract story units using general tempo analysis. Experimental results are given to demonstrate the feasibility and efficiency of the proposed technique.

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

Improved image retrieval based on fuzzy colour feature vector

NASA Astrophysics Data System (ADS)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

Robust hepatic vessel segmentation using multi deep convolution network

NASA Astrophysics Data System (ADS)

Kitrungrotsakul, Titinunt; Han, Xian-Hua; Iwamoto, Yutaro; Foruzan, Amir Hossein; Lin, Lanfen; Chen, Yen-Wei

2017-03-01

Extraction of blood vessels of the organ is a challenging task in the area of medical image processing. It is really difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the hepatic vessels from computed tomography (CT) image. We proposed novel deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of three deep convolution neural networks to extract features from difference planes of CT data. The three networks have share features at the first convolution layer but will separately learn their own features in the second layer. All three networks will join again at the top layer. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 12 CT volumes which training data are randomly generate from 5 CT volumes and 7 using for test. Our network can yield an average dice coefficient 0.830, while 3D deep convolution neural network can yield around 0.7 and multi-scale can yield only 0.6.

Low complexity feature extraction for classification of harmonic signals

NASA Astrophysics Data System (ADS)

William, Peter E.

In this dissertation, feature extraction algorithms have been developed for extraction of characteristic features from harmonic signals. The common theme for all developed algorithms is the simplicity in generating a significant set of features directly from the time domain harmonic signal. The features are a time domain representation of the composite, yet sparse, harmonic signature in the spectral domain. The algorithms are adequate for low-power unattended sensors which perform sensing, feature extraction, and classification in a standalone scenario. The first algorithm generates the characteristic features using only the duration between successive zero-crossing intervals. The second algorithm estimates the harmonics' amplitudes of the harmonic structure employing a simplified least squares method without the need to estimate the true harmonic parameters of the source signal. The third algorithm, resulting from a collaborative effort with Daniel White at the DSP Lab, University of Nebraska-Lincoln, presents an analog front end approach that utilizes a multichannel analog projection and integration to extract the sparse spectral features from the analog time domain signal. Classification is performed using a multilayer feedforward neural network. Evaluation of the proposed feature extraction algorithms for classification through the processing of several acoustic and vibration data sets (including military vehicles and rotating electric machines) with comparison to spectral features shows that, for harmonic signals, time domain features are simpler to extract and provide equivalent or improved reliability over the spectral features in both the detection probabilities and false alarm rate.

Quantitative 3-D Imaging, Segmentation and Feature Extraction of the Respiratory System in Small Mammals for Computational Biophysics Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trease, Lynn L.; Trease, Harold E.; Fowler, John

2007-03-15

One of the critical steps toward performing computational biology simulations, using mesh based integration methods, is in using topologically faithful geometry derived from experimental digital image data as the basis for generating the computational meshes. Digital image data representations contain both the topology of the geometric features and experimental field data distributions. The geometric features that need to be captured from the digital image data are three-dimensional, therefore the process and tools we have developed work with volumetric image data represented as data-cubes. This allows us to take advantage of 2D curvature information during the segmentation and feature extraction process.more » The process is basically: 1) segmenting to isolate and enhance the contrast of the features that we wish to extract and reconstruct, 2) extracting the geometry of the features in an isosurfacing technique, and 3) building the computational mesh using the extracted feature geometry. “Quantitative” image reconstruction and feature extraction is done for the purpose of generating computational meshes, not just for producing graphics "screen" quality images. For example, the surface geometry that we extract must represent a closed water-tight surface.« less

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

Dilated contour extraction and component labeling algorithm for object vector representation

NASA Astrophysics Data System (ADS)

Skourikhine, Alexei N.

2005-08-01

Object boundary extraction from binary images is important for many applications, e.g., image vectorization, automatic interpretation of images containing segmentation results, printed and handwritten documents and drawings, maps, and AutoCAD drawings. Efficient and reliable contour extraction is also important for pattern recognition due to its impact on shape-based object characterization and recognition. The presented contour tracing and component labeling algorithm produces dilated (sub-pixel) contours associated with corresponding regions. The algorithm has the following features: (1) it always produces non-intersecting, non-degenerate contours, including the case of one-pixel wide objects; (2) it associates the outer and inner (i.e., around hole) contours with the corresponding regions during the process of contour tracing in a single pass over the image; (3) it maintains desired connectivity of object regions as specified by 8-neighbor or 4-neighbor connectivity of adjacent pixels; (4) it avoids degenerate regions in both background and foreground; (5) it allows an easy augmentation that will provide information about the containment relations among regions; (6) it has a time complexity that is dominantly linear in the number of contour points. This early component labeling (contour-region association) enables subsequent efficient object-based processing of the image information.

In-situ suspended aggregate microextraction of gold nanoparticles from water samples and determination by electrothermal atomic absorption spectrometry.

PubMed

Choleva, Tatiana G; Kappi, Foteini A; Tsogas, George Z; Vlessidis, Athanasios G; Giokas, Dimosthenis L

2016-05-01

This work describes a new method for the extraction and determination of gold nanoparticles in environmental samples by means of in-situ suspended aggregate microextraction and electrothermal atomic absorption spectrometry. The method relies on the in-situ formation of a supramolecular aggregate phase through ion-association between a cationic surfactant and a benzene sulfonic acid derivative. Gold nanoparticles are physically entrapped into the aggregate phase which is separated from the bulk aqueous solution by vacuum filtration on the surface of a cellulose filter in the form of a thin film. The film is removed from the filter surface and is dissociated into an acidified methanolic solution which is used for analysis. Under the optimized experimental conditions, gold nanoparticles can be efficiently extracted from water samples with recovery rates between 81.0-93.3%, precision 5.4-12.0% and detection limits as low as 75femtomolL(-1) using only 20mL of sample volume. The satisfactory analytical features of the method along with the simplicity indicate the efficiency of this new approach to adequately collect and extract gold nanoparticle species from water samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Audio feature extraction using probability distribution function

NASA Astrophysics Data System (ADS)

Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.

2015-05-01

Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.

Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.

PubMed

Chaddad, Ahmad; Sabri, Siham; Niazi, Tamim; Abdulkarim, Bassam

2018-06-19

We propose a multiscale texture features based on Laplacian-of Gaussian (LoG) filter to predict progression free (PFS) and overall survival (OS) in patients newly diagnosed with glioblastoma (GBM). Experiments use the extracted features derived from 40 patients of GBM with T1-weighted imaging (T1-WI) and Fluid-attenuated inversion recovery (FLAIR) images that were segmented manually into areas of active tumor, necrosis, and edema. Multiscale texture features were extracted locally from each of these areas of interest using a LoG filter and the relation between features to OS and PFS was investigated using univariate (i.e., Spearman's rank correlation coefficient, log-rank test and Kaplan-Meier estimator) and multivariate analyses (i.e., Random Forest classifier). Three and seven features were statistically correlated with PFS and OS, respectively, with absolute correlation values between 0.32 and 0.36 and p < 0.05. Three features derived from active tumor regions only were associated with OS (p < 0.05) with hazard ratios (HR) of 2.9, 3, and 3.24, respectively. Combined features showed an AUC value of 85.37 and 85.54% for predicting the PFS and OS of GBM patients, respectively, using the random forest (RF) classifier. We presented a multiscale texture features to characterize the GBM regions and predict he PFS and OS. The efficiency achievable suggests that this technique can be developed into a GBM MR analysis system suitable for clinical use after a thorough validation involving more patients. Graphical abstract Scheme of the proposed model for characterizing the heterogeneity of GBM regions and predicting the overall survival and progression free survival of GBM patients. (1) Acquisition of pretreatment MRI images; (2) Affine registration of T1-WI image with its corresponding FLAIR images, and GBM subtype (phenotypes) labelling; (3) Extraction of nine texture features from the three texture scales fine, medium, and coarse derived from each of GBM regions; (4) Comparing heterogeneity between GBM regions by ANOVA test; Survival analysis using Univariate (Spearman rank correlation between features and survival (i.e., PFS and OS) based on each of the GBM regions, Kaplan-Meier estimator and log-rank test to predict the PFS and OS of patient groups that grouped based on median of feature), and multivariate (random forest model) for predicting the PFS and OS of patients groups that grouped based on median of PFS and OS.

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

2017-04-01

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects.

Morphological Feature Extraction for Automatic Registration of Multispectral Images

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2007-01-01

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

A highly efficient approach to protein interactome mapping based on collaborative filtering framework.

PubMed

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-09

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework

PubMed Central

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-01

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly. PMID:25572661

A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework

NASA Astrophysics Data System (ADS)

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-01

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

NASA Astrophysics Data System (ADS)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

A 400-kWe high-efficiency steam turbine for industrial cogeneration

NASA Technical Reports Server (NTRS)

Leibowitz, H. M.

1982-01-01

An advanced state-of-the-art steam turbine-generator developed to serve as the power conversion subsystem for the Department of Energy's Sandia National Laboratories' Solar Total-Energy Project (STEP) is described. The turbine-generator, which is designed to provide 400-kW of net electrical power, represents the largest turbine-generator built specifically for commercial solar-powered cogeneration. The controls for the turbine-generator incorporate a multiple, partial-arc entry to provide efficient off-design performance, as well as an extraction control scheme to permit extraction flow regulation while maintaining 110-spsig pressure. Normal turbine operation is achieved while synchronized to a local utility and in a stand-alone mode. In both cases, the turbine-generator features automatic load control as well as remote start-up and shutdown capability. Tests totaling 200 hours were conducted to confirm the integrity of the turbine's mechanical structure and control function. Performance tests resulted in a measured inlet throttle flow of 8,450 pounds per hour, which was near design conditions.

Identifying sports videos using replay, text, and camera motion features

NASA Astrophysics Data System (ADS)

Kobla, Vikrant; DeMenthon, Daniel; Doermann, David S.

1999-12-01

Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various classes such as sports, news, movies, or documentaries, increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we discuss the extraction of features that enable identification of sports videos directly from the compressed domain of MPEG video. These features include detecting the presence of action replays, determining the amount of scene text in vide, and calculating various statistics on camera and/or object motion. The features are derived from the macroblock, motion,and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to substantial gains in processing speeds. Full-decoding of selective frames is required only for text analysis. A decision tree classifier built using these features is able to identify sports clips with an accuracy of about 93 percent.

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

PubMed

Wen, Zaidao; Hou, Biao; Jiao, Licheng

2017-05-03

Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

Sentence alignment using feed forward neural network.

PubMed

Fattah, Mohamed Abdel; Ren, Fuji; Kuroiwa, Shingo

2006-12-01

Parallel corpora have become an essential resource for work in multi lingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to align sentences in bilingual parallel corpora based on feed forward neural network classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuate score, and cognate score values. A set of manually prepared training data has been assigned to train the feed forward neural network. Another set of data was used for testing. Using this new approach, we could achieve an error reduction of 60% over length based approach when applied on English-Arabic parallel documents. Moreover this new approach is valid for any language pair and it is quite flexible approach since the feature parameter vector may contain more/less or different features than that we used in our system such as lexical match feature.

Extraction and representation of common feature from uncertain facial expressions with cloud model.

PubMed

Wang, Shuliang; Chi, Hehua; Yuan, Hanning; Geng, Jing

2017-12-01

Human facial expressions are key ingredient to convert an individual's innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

PyEEG: an open source Python module for EEG/MEG feature extraction.

PubMed

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

PubMed Central

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. PMID:21512582

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination

NASA Astrophysics Data System (ADS)

Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael

2017-05-01

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

Detailed Hydrographic Feature Extraction from High-Resolution LiDAR Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Danny L. Anderson

Detailed hydrographic feature extraction from high-resolution light detection and ranging (LiDAR) data is investigated. Methods for quantitatively evaluating and comparing such extractions are presented, including the use of sinuosity and longitudinal root-mean-square-error (LRMSE). These metrics are then used to quantitatively compare stream networks in two studies. The first study examines the effect of raster cell size on watershed boundaries and stream networks delineated from LiDAR-derived digital elevation models (DEMs). The study confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes generally yielded better stream network delineations, based on sinuosity and LRMSE. The second study demonstrates amore » new method of delineating a stream directly from LiDAR point clouds, without the intermediate step of deriving a DEM. Direct use of LiDAR point clouds could improve efficiency and accuracy of hydrographic feature extractions. The direct delineation method developed herein and termed “mDn”, is an extension of the D8 method that has been used for several decades with gridded raster data. The method divides the region around a starting point into sectors, using the LiDAR data points within each sector to determine an average slope, and selecting the sector with the greatest downward slope to determine the direction of flow. An mDn delineation was compared with a traditional grid-based delineation, using TauDEM, and other readily available, common stream data sets. Although, the TauDEM delineation yielded a sinuosity that more closely matches the reference, the mDn delineation yielded a sinuosity that was higher than either the TauDEM method or the existing published stream delineations. Furthermore, stream delineation using the mDn method yielded the smallest LRMSE.« less

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

PubMed Central

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

PubMed

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

A data-driven dynamics simulation framework for railway vehicles

NASA Astrophysics Data System (ADS)

Nie, Yinyu; Tang, Zhao; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2018-03-01

The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy.

Analysis to feature-based video stabilization/registration techniques within application of traffic data collection

NASA Astrophysics Data System (ADS)

Sadat, Mojtaba T.; Viti, Francesco

2015-02-01

Machine vision is rapidly gaining popularity in the field of Intelligent Transportation Systems. In particular, advantages are foreseen by the exploitation of Aerial Vehicles (AV) in delivering a superior view on traffic phenomena. However, vibration on AVs makes it difficult to extract moving objects on the ground. To partly overcome this issue, image stabilization/registration procedures are adopted to correct and stitch multiple frames taken of the same scene but from different positions, angles, or sensors. In this study, we examine the impact of multiple feature-based techniques for stabilization, and we show that SURF detector outperforms the others in terms of time efficiency and output similarity.

Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations

DTIC Science & Technology

2008-07-01

classification of the targets of interest. The MMI features extracted in this manner have two properties that provide a sound justification for...are generalizations of well- known feature extraction methods such as Principal Components Analysis (PCA) and Independent Component Analysis (ICA...augment (without degrading performance) a large class of generic fusion processes. Ontologies Classifications Feature extraction Feature analysis

Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

NASA Astrophysics Data System (ADS)

Shi, Wenzhong; Deng, Susu; Xu, Wenbing

2018-02-01

For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (< 5 years) landslides and approximately 35% of historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should be filtered using a filtering strategy based on supplementary information provided by expert knowledge or other data sources.

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG.

PubMed

Supratak, Akara; Dong, Hao; Wu, Chao; Guo, Yike

2017-11-01

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.

Image search engine with selective filtering and feature-element-based classification

NASA Astrophysics Data System (ADS)

Li, Qing; Zhang, Yujin; Dai, Shengyang

2001-12-01

With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.

Ionic liquids gels: Soft materials for environmental remediation.

PubMed

Marullo, Salvatore; Rizzo, Carla; Dintcheva, Nadka T; Giannici, Francesco; D'Anna, Francesca

2018-05-01

Nanostructured sorbents and, in particular, supramolecular gels are emerging as efficient materials for the removal of toxic contaminants from water, like industrial dyes. It is also known that ionic liquids can dissolve significant amounts of dyes. Consequently, supramolecular ionic liquids gels could be highly efficient sorbents for dyes removal. This would also contribute to overcome the drawbacks associated with dye removal by liquid-liquid extraction with neat ionic liquids which would require large volumes of extractant and a more difficult separation of the phases. Herein we employed novel supramolecular ionic liquid gels based on diimidazolium salts bearing naturally occurring or biomass derived anions, to adsorb cationic and anionic dyes from wastewaters. We also carried out a detailed investigation of thermal, structural, morphological and rheological features of our gels to identify which of them are key in designing better sorbents for environmental remediation. The most effective gels showed fast and thorough removal of cationic dyes like Rhodamine B. These gels could also be reused up to 20 times without any loss in removal efficiency. Overall, our ionic gels outperform most of gel-based sorbents systems so far reported in literature. Copyright © 2018 Elsevier Inc. All rights reserved.

Color Shift Failure Prediction for Phosphor-Converted White LEDs by Modeling Features of Spectral Power Distribution with a Nonlinear Filter Approach

PubMed Central

Mohamed, Moumouni Guero; Fan, Xuejun; Zhang, Guoqi; Pecht, Michael

2017-01-01

With the expanding application of light-emitting diodes (LEDs), the color quality of white LEDs has attracted much attention in several color-sensitive application fields, such as museum lighting, healthcare lighting and displays. Reliability concerns for white LEDs are changing from the luminous efficiency to color quality. However, most of the current available research on the reliability of LEDs is still focused on luminous flux depreciation rather than color shift failure. The spectral power distribution (SPD), defined as the radiant power distribution emitted by a light source at a range of visible wavelength, contains the most fundamental luminescence mechanisms of a light source. SPD is used as the quantitative inference of an LED’s optical characteristics, including color coordinates that are widely used to represent the color shift process. Thus, to model the color shift failure of white LEDs during aging, this paper first extracts the features of an SPD, representing the characteristics of blue LED chips and phosphors, by multi-peak curve-fitting and modeling them with statistical functions. Then, because the shift processes of extracted features in aged LEDs are always nonlinear, a nonlinear state-space model is then developed to predict the color shift failure time within a self-adaptive particle filter framework. The results show that: (1) the failure mechanisms of LEDs can be identified by analyzing the extracted features of SPD with statistical curve-fitting and (2) the developed method can dynamically and accurately predict the color coordinates, correlated color temperatures (CCTs), and color rendering indexes (CRIs) of phosphor-converted (pc)-white LEDs, and also can estimate the residual color life. PMID:28773176

Color Shift Failure Prediction for Phosphor-Converted White LEDs by Modeling Features of Spectral Power Distribution with a Nonlinear Filter Approach.

PubMed

Fan, Jiajie; Mohamed, Moumouni Guero; Qian, Cheng; Fan, Xuejun; Zhang, Guoqi; Pecht, Michael

2017-07-18

With the expanding application of light-emitting diodes (LEDs), the color quality of white LEDs has attracted much attention in several color-sensitive application fields, such as museum lighting, healthcare lighting and displays. Reliability concerns for white LEDs are changing from the luminous efficiency to color quality. However, most of the current available research on the reliability of LEDs is still focused on luminous flux depreciation rather than color shift failure. The spectral power distribution (SPD), defined as the radiant power distribution emitted by a light source at a range of visible wavelength, contains the most fundamental luminescence mechanisms of a light source. SPD is used as the quantitative inference of an LED's optical characteristics, including color coordinates that are widely used to represent the color shift process. Thus, to model the color shift failure of white LEDs during aging, this paper first extracts the features of an SPD, representing the characteristics of blue LED chips and phosphors, by multi-peak curve-fitting and modeling them with statistical functions. Then, because the shift processes of extracted features in aged LEDs are always nonlinear, a nonlinear state-space model is then developed to predict the color shift failure time within a self-adaptive particle filter framework. The results show that: (1) the failure mechanisms of LEDs can be identified by analyzing the extracted features of SPD with statistical curve-fitting and (2) the developed method can dynamically and accurately predict the color coordinates, correlated color temperatures (CCTs), and color rendering indexes (CRIs) of phosphor-converted (pc)-white LEDs, and also can estimate the residual color life.

Performance enhancement for audio-visual speaker identification using dynamic facial muscle model.

PubMed

Asadpour, Vahid; Towhidkhah, Farzad; Homayounpour, Mohammad Mehdi

2006-10-01

Science of human identification using physiological characteristics or biometry has been of great concern in security systems. However, robust multimodal identification systems based on audio-visual information has not been thoroughly investigated yet. Therefore, the aim of this work to propose a model-based feature extraction method which employs physiological characteristics of facial muscles producing lip movements. This approach adopts the intrinsic properties of muscles such as viscosity, elasticity, and mass which are extracted from the dynamic lip model. These parameters are exclusively dependent on the neuro-muscular properties of speaker; consequently, imitation of valid speakers could be reduced to a large extent. These parameters are applied to a hidden Markov model (HMM) audio-visual identification system. In this work, a combination of audio and video features has been employed by adopting a multistream pseudo-synchronized HMM training method. Noise robust audio features such as Mel-frequency cepstral coefficients (MFCC), spectral subtraction (SS), and relative spectra perceptual linear prediction (J-RASTA-PLP) have been used to evaluate the performance of the multimodal system once efficient audio feature extraction methods have been utilized. The superior performance of the proposed system is demonstrated on a large multispeaker database of continuously spoken digits, along with a sentence that is phonetically rich. To evaluate the robustness of algorithms, some experiments were performed on genetically identical twins. Furthermore, changes in speaker voice were simulated with drug inhalation tests. In 3 dB signal to noise ratio (SNR), the dynamic muscle model improved the identification rate of the audio-visual system from 91 to 98%. Results on identical twins revealed that there was an apparent improvement on the performance for the dynamic muscle model-based system, in which the identification rate of the audio-visual system was enhanced from 87 to 96%.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Islam, Md. Shafiqul, E-mail: shafique@eng.ukm.my; Hannan, M.A., E-mail: hannan@eng.ukm.my; Basri, Hassan

Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensormore » intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.« less

Topography-Assisted Electromagnetic Platform for Blood-to-PCR in a Droplet

PubMed Central

Chiou, Chi-Han; Shin, Dong Jin; Zhang, Yi; Wang, Tza-Huei

2013-01-01

This paper presents an electromagnetically actuated platform for automated sample preparation and detection of nucleic acids. The proposed platform integrates nucleic acid extraction using silica-coated magnetic particles with real-time polymerase chain reaction (PCR) on a single cartridge. Extraction of genomic material was automated by manipulating magnetic particles in droplets using a series of planar coil electromagnets assisted by topographical features, enabling efficient fluidic processing over a variety of buffers and reagents. The functionality of the platform was demonstrated by performing nucleic acid extraction from whole blood, followed by real-time PCR detection of KRAS oncogene. Automated sample processing from whole blood to PCR-ready droplet was performed in 15 minutes. We took a modular approach of decoupling the modules of magnetic manipulation and optical detection from the device itself, enabling a low-complexity cartridge that operates in tandem with simple external instruments. PMID:23835223

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites.

PubMed

Maalek, Reza; Lichti, Derek D; Ruwanpura, Janaka Y

2018-03-08

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites

PubMed Central

Maalek, Reza; Lichti, Derek D; Ruwanpura, Janaka Y

2018-01-01

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites. PMID:29518062

[Research on spectra recognition method for cabbages and weeds based on PCA and SIMCA].

PubMed

Zu, Qin; Deng, Wei; Wang, Xiu; Zhao, Chun-Jiang

2013-10-01

In order to improve the accuracy and efficiency of weed identification, the difference of spectral reflectance was employed to distinguish between crops and weeds. Firstly, the different combinations of Savitzky-Golay (SG) convolutional derivation and multiplicative scattering correction (MSC) method were applied to preprocess the raw spectral data. Then the clustering analysis of various types of plants was completed by using principal component analysis (PCA) method, and the feature wavelengths which were sensitive for classifying various types of plants were extracted according to the corresponding loading plots of the optimal principal components in PCA results. Finally, setting the feature wavelengths as the input variables, the soft independent modeling of class analogy (SIMCA) classification method was used to identify the various types of plants. The experimental results of classifying cabbages and weeds showed that on the basis of the optimal pretreatment by a synthetic application of MSC and SG convolutional derivation with SG's parameters set as 1rd order derivation, 3th degree polynomial and 51 smoothing points, 23 feature wavelengths were extracted in accordance with the top three principal components in PCA results. When SIMCA method was used for classification while the previously selected 23 feature wavelengths were set as the input variables, the classification rates of the modeling set and the prediction set were respectively up to 98.6% and 100%.

Comparative analyses of universal extraction buffers for assay of stress related biochemical and physiological parameters.

PubMed

Han, Chunyu; Chan, Zhulong; Yang, Fan

2015-01-01

Comparative efficiency of three extraction solutions, including the universal sodium phosphate buffer (USPB), the Tris-HCl buffer (UTHB), and the specific buffers, were compared for assays of soluble protein, free proline, superoxide radical (O2∙-), hydrogen peroxide (H2O2), and the antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT), guaiacol peroxidase (POD), ascorbate peroxidase (APX), glutathione peroxidase (GPX), and glutathione reductase (GR) in Populus deltoide. Significant differences for protein extraction were detected via sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and two-dimensional electrophoresis (2-DE). Between the two universal extraction buffers, the USPB showed higher efficiency for extraction of soluble protein, CAT, GR, O2∙-, GPX, SOD, and free proline, while the UTHB had higher efficiency for extraction of APX, POD, and H2O2. When compared with the specific buffers, the USPB showed higher extraction efficiency for measurement of soluble protein, CAT, GR, and O2∙-, parallel extraction efficiency for GPX, SOD, free proline, and H2O2, and lower extraction efficiency for APX and POD, whereas the UTHB had higher extraction efficiency for measurement of POD and H2O2. Further comparisons proved that 100 mM USPB buffer showed the highest extraction efficiencies. These results indicated that USPB would be suitable and efficient for extraction of soluble protein, CAT, GR, GPX, SOD, H2O2, O2∙-, and free proline.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

Single-trial laser-evoked potentials feature extraction for prediction of pain perception.

PubMed

Huang, Gan; Xiao, Ping; Hu, Li; Hung, Yeung Sam; Zhang, Zhiguo

2013-01-01

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.

Recurrence time statistics: versatile tools for genomic DNA sequence analysis.

PubMed

Cao, Yinhe; Tung, Wen-Wen; Gao, J B

2004-01-01

With the completion of the human and a few model organisms' genomes, and the genomes of many other organisms waiting to be sequenced, it has become increasingly important to develop faster computational tools which are capable of easily identifying the structures and extracting features from DNA sequences. One of the more important structures in a DNA sequence is repeat-related. Often they have to be masked before protein coding regions along a DNA sequence are to be identified or redundant expressed sequence tags (ESTs) are to be sequenced. Here we report a novel recurrence time based method for sequence analysis. The method can conveniently study all kinds of periodicity and exhaustively find all repeat-related features from a genomic DNA sequence. An efficient codon index is also derived from the recurrence time statistics, which has the salient features of being largely species-independent and working well on very short sequences. Efficient codon indices are key elements of successful gene finding algorithms, and are particularly useful for determining whether a suspected EST belongs to a coding or non-coding region. We illustrate the power of the method by studying the genomes of E. coli, the yeast S. cervisivae, the nematode worm C. elegans, and the human, Homo sapiens. Computationally, our method is very efficient. It allows us to carry out analysis of genomes on the whole genomic scale by a PC.

Automated global structure extraction for effective local building block processing in XCS.

PubMed

Butz, Martin V; Pelikan, Martin; Llorà, Xavier; Goldberg, David E

2006-01-01

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.

Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification.

PubMed

Liu, Wu; Zhang, Cheng; Ma, Huadong; Li, Shuangqun

2018-02-06

The integration of the latest breakthroughs in bioinformatics technology from one side and artificial intelligence from another side, enables remarkable advances in the fields of intelligent security guard computational biology, healthcare, and so on. Among them, biometrics based automatic human identification is one of the most fundamental and significant research topic. Human gait, which is a biometric features with the unique capability, has gained significant attentions as the remarkable characteristics of remote accessed, robust and security in the biometrics based human identification. However, the existed methods cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed an efficient spatial-temporal gait features with deep learning for human identification. First of all, we proposed a gait energy image (GEI) based Siamese neural network to automatically extract robust and discriminative spatial gait features for human identification. Furthermore, we exploit the deep 3-dimensional convolutional networks to learn the human gait convolutional 3D (C3D) as the temporal gait features. Finally, the GEI and C3D gait features are embedded into the null space by the Null Foley-Sammon Transform (NFST). In the new space, the spatial-temporal features are sufficiently combined with distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. Consequently, the experiments on the world's largest gait database show our framework impressively outperforms state-of-the-art methods.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

PubMed

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

PubMed

McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

2018-04-01

Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum

NASA Astrophysics Data System (ADS)

Wang, Ruofan; Wang, Jiang; Li, Shunan; Yu, Haitao; Deng, Bin; Wei, Xile

2015-01-01

In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.

Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum.

PubMed

Wang, Ruofan; Wang, Jiang; Li, Shunan; Yu, Haitao; Deng, Bin; Wei, Xile

2015-01-01

In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.

A hybrid feature selection and health indicator construction scheme for delay-time-based degradation modelling of rolling element bearings

NASA Astrophysics Data System (ADS)

Zhang, Bin; Deng, Congying; Zhang, Yi

2018-03-01

Rolling element bearings are mechanical components used frequently in most rotating machinery and they are also vulnerable links representing the main source of failures in such systems. Thus, health condition monitoring and fault diagnosis of rolling element bearings have long been studied to improve operational reliability and maintenance efficiency of rotatory machines. Over the past decade, prognosis that enables forewarning of failure and estimation of residual life attracted increasing attention. To accurately and efficiently predict failure of the rolling element bearing, the degradation requires to be well represented and modelled. For this purpose, degradation of the rolling element bearing is analysed with the delay-time-based model in this paper. Also, a hybrid feature selection and health indicator construction scheme is proposed for extraction of the bearing health relevant information from condition monitoring sensor data. Effectiveness of the presented approach is validated through case studies on rolling element bearing run-to-failure experiments.

A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

PubMed

Liu, Zhi; Xu, Shuqiong; Zhang, Yun; Chen, Chun Lung Philip

2014-11-01

This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.

Ordinal measures for iris recognition.

PubMed

Sun, Zhenan; Tan, Tieniu

2009-12-01

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.

Accuracy improvement in laser stripe extraction for large-scale triangulation scanning measurement system

NASA Astrophysics Data System (ADS)

Zhang, Yang; Liu, Wei; Li, Xiaodong; Yang, Fan; Gao, Peng; Jia, Zhenyuan

2015-10-01

Large-scale triangulation scanning measurement systems are widely used to measure the three-dimensional profile of large-scale components and parts. The accuracy and speed of the laser stripe center extraction are essential for guaranteeing the accuracy and efficiency of the measuring system. However, in the process of large-scale measurement, multiple factors can cause deviation of the laser stripe center, including the spatial light intensity distribution, material reflectivity characteristics, and spatial transmission characteristics. A center extraction method is proposed for improving the accuracy of the laser stripe center extraction based on image evaluation of Gaussian fitting structural similarity and analysis of the multiple source factors. First, according to the features of the gray distribution of the laser stripe, evaluation of the Gaussian fitting structural similarity is estimated to provide a threshold value for center compensation. Then using the relationships between the gray distribution of the laser stripe and the multiple source factors, a compensation method of center extraction is presented. Finally, measurement experiments for a large-scale aviation composite component are carried out. The experimental results for this specific implementation verify the feasibility of the proposed center extraction method and the improved accuracy for large-scale triangulation scanning measurements.

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Irregular Wave Energy Extraction Analysis for a Slider Crank WEC Power Take-Off System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sang, Yuanrui; Karayaka, H. Bora; Yan, Yanjun

2015-09-02

Slider crank Wave Energy Converter (WEC) is a novel energy conversion device. It converts wave energy into electricity at a relatively high efficiency, and it features a simple structure. Past analysis on this WEC has been done under regular sinusoidal wave conditions, and a suboptimal energy could be achieved. This paper presents the analysis of the system under irregular wave conditions; a time-domain hydrodynamics model is adopted and the control methodology is modified to better serve the irregular wave conditions. Results from the simulations show that the performance of the system under irregular wave conditions is different from that undermore » regular sinusoidal wave conditions, but still a reasonable amount of energy can be extracted.« less

Decomposition and extraction: a new framework for visual classification.

PubMed

Fang, Yuqiang; Chen, Qiang; Sun, Lin; Dai, Bin; Yan, Shuicheng

2014-08-01

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

Efficient processing of fluorescence images using directional multiscale representations.

PubMed

Labate, D; Laezza, F; Negi, P; Ozcan, B; Papadakis, M

2014-01-01

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data.

Rotation covariant image processing for biomedical applications.

PubMed

Skibbe, Henrik; Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

Accelerating image recognition on mobile devices using GPGPU

NASA Astrophysics Data System (ADS)

Bordallo López, Miguel; Nykänen, Henri; Hannuksela, Jari; Silvén, Olli; Vehviläinen, Markku

2011-01-01

The future multi-modal user interfaces of battery-powered mobile devices are expected to require computationally costly image analysis techniques. The use of Graphic Processing Units for computing is very well suited for parallel processing and the addition of programmable stages and high precision arithmetic provide for opportunities to implement energy-efficient complete algorithms. At the moment the first mobile graphics accelerators with programmable pipelines are available, enabling the GPGPU implementation of several image processing algorithms. In this context, we consider a face tracking approach that uses efficient gray-scale invariant texture features and boosting. The solution is based on the Local Binary Pattern (LBP) features and makes use of the GPU on the pre-processing and feature extraction phase. We have implemented a series of image processing techniques in the shader language of OpenGL ES 2.0, compiled them for a mobile graphics processing unit and performed tests on a mobile application processor platform (OMAP3530). In our contribution, we describe the challenges of designing on a mobile platform, present the performance achieved and provide measurement results for the actual power consumption in comparison to using the CPU (ARM) on the same platform.

Efficient processing of fluorescence images using directional multiscale representations

PubMed Central

Labate, D.; Laezza, F.; Negi, P.; Ozcan, B.; Papadakis, M.

2017-01-01

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data. PMID:28804225

Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.

PubMed

Lim, Meng-Hui; Teoh, Andrew Beng Jin; Toh, Kar-Ann

2013-06-01

Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

PubMed

Guo, Jian; Qian, Kun; Zhang, Gongxuan; Xu, Huijie; Schuller, Björn

2017-12-01

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

Sieve-based relation extraction of gene regulatory networks from biological literature

PubMed Central

2015-01-01

Background Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. Results We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions. Conclusions Linear-chain conditional random fields, along with appropriate data transformations, can be efficiently used to extract relations. The sieve-based architecture simplifies the system as new sieves can be easily added or removed and each sieve can utilize the results of previous ones. Furthermore, sieves with conditional random fields can be trained on arbitrary text data and hence are applicable to broad range of relation extraction tasks and data domains. PMID:26551454

Sieve-based relation extraction of gene regulatory networks from biological literature.

PubMed

Žitnik, Slavko; Žitnik, Marinka; Zupan, Blaž; Bajec, Marko

2015-01-01

Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions. Linear-chain conditional random fields, along with appropriate data transformations, can be efficiently used to extract relations. The sieve-based architecture simplifies the system as new sieves can be easily added or removed and each sieve can utilize the results of previous ones. Furthermore, sieves with conditional random fields can be trained on arbitrary text data and hence are applicable to broad range of relation extraction tasks and data domains.

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Low-power coprocessor for Haar-like feature extraction with pixel-based pipelined architecture

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Fujita, Yuki; Zhang, Xiangyu; Chen, Lei; Jürgen Mattausch, Hans

2017-04-01

Intelligent analysis of image and video data requires image-feature extraction as an important processing capability for machine-vision realization. A coprocessor with pixel-based pipeline (CFEPP) architecture is developed for real-time Haar-like cell-based feature extraction. Synchronization with the image sensor’s pixel frequency and immediate usage of each input pixel for the feature-construction process avoids the dependence on memory-intensive conventional strategies like integral-image construction or frame buffers. One 180 nm CMOS prototype can extract the 1680-dimensional Haar-like feature vectors, applied in the speeded up robust features (SURF) scheme, using an on-chip memory of only 96 kb (kilobit). Additionally, a low power dissipation of only 43.45 mW at 1.8 V supply voltage is achieved during VGA video procession at 120 MHz frequency with more than 325 fps. The Haar-like feature-extraction coprocessor is further evaluated by the practical application of vehicle recognition, achieving the expected high accuracy which is comparable to previous work.

Acousto-Optic Technology for Topographic Feature Extraction and Image Analysis.

DTIC Science & Technology

1981-03-01

This report contains all findings of the acousto - optic technology study for feature extraction conducted by Deft Laboratories Inc. for the U.S. Army...topographic feature extraction and image analysis using acousto - optic (A-O) technology. A conclusion of this study was that A-O devices are potentially

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using computer-extracted mammographic features to predict occult invasive disease in patients with biopsy proven DCIS. We proposed a computer-vision algorithm based approach to extract mammographic features from magnification views of full field digital mammography (FFDM) for patients with DCIS. After an expert breast radiologist provided a region of interest (ROI) mask for the DCIS lesion, the proposed approach is able to segment individual microcalcifications (MCs), detect the boundary of the MC cluster (MCC), and extract 113 mammographic features from MCs and MCC within the ROI. In this study, we extracted mammographic features from 99 patients with DCIS (74 pure DCIS; 25 DCIS plus invasive disease). The predictive power of the mammographic features was demonstrated through binary classifications between pure DCIS and DCIS with invasive disease using linear discriminant analysis (LDA). Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features. The generalization performance was assessed using Leave-One-Out Cross-Validation and Receiver Operating Characteristic (ROC) curve analysis. Using the computer-extracted mammographic features, the proposed model was able to distinguish DCIS with invasive disease from pure DCIS, with an average classification performance of AUC = 0.61 +/- 0.05. Overall, the proposed computer-extracted mammographic features are promising for predicting occult invasive disease in DCIS.

Optical devices featuring textured semiconductor layers

DOEpatents

Moustakas, Theodore D [Dover, MA; Cabalu, Jasper S [Cary, NC

2011-10-11

A semiconductor sensor, solar cell or emitter, or a precursor therefor, has a substrate and one or more textured semiconductor layers deposited onto the substrate. The textured layers enhance light extraction or absorption. Texturing in the region of multiple quantum wells greatly enhances internal quantum efficiency if the semiconductor is polar and the quantum wells are grown along the polar direction. Electroluminescence of LEDs of the invention is dichromatic, and results in variable color LEDs, including white LEDs, without the use of phosphor.

Optical devices featuring textured semiconductor layers

DOEpatents

Moustakas, Theodore D [Dover, MA; Cabalu, Jasper S [Cary, NC

2012-08-07

A semiconductor sensor, solar cell or emitter, or a precursor therefor, has a substrate and one or more textured semiconductor layers deposited onto the substrate. The textured layers enhance light extraction or absorption. Texturing in the region of multiple quantum wells greatly enhances internal quantum efficiency if the semiconductor is polar and the quantum wells are grown along the polar direction. Electroluminescence of LEDs of the invention is dichromatic, and results in variable color LEDs, including white LEDs, without the use of phosphor.

Image Segmentation, Registration, Compression, and Matching

NASA Technical Reports Server (NTRS)

Yadegar, Jacob; Wei, Hai; Yadegar, Joseph; Ray, Nilanjan; Zabuawala, Sakina

2011-01-01

A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity/topology components of the generated models. The highly efficient triangular mesh compression compacts the connectivity information at the rate of 1.5-4 bits per vertex (on average for triangle meshes), while reducing the 3D geometry by 40-50 percent. Finally, taking into consideration the characteristics of 3D terrain data, and using the innovative, regularized binary decomposition mesh modeling, a multistage, pattern-drive modeling, and compression technique has been developed to provide an effective framework for compressing digital elevation model (DEM) surfaces, high-resolution aerial imagery, and other types of NASA data.

Facets : a Cloudcompare Plugin to Extract Geological Planes from Unstructured 3d Point Clouds

NASA Astrophysics Data System (ADS)

Dewez, T. J. B.; Girardeau-Montaut, D.; Allanic, C.; Rohmer, J.

2016-06-01

Geological planar facets (stratification, fault, joint…) are key features to unravel the tectonic history of rock outcrop or appreciate the stability of a hazardous rock cliff. Measuring their spatial attitude (dip and strike) is generally performed by hand with a compass/clinometer, which is time consuming, requires some degree of censoring (i.e. refusing to measure some features judged unimportant at the time), is not always possible for fractures higher up on the outcrop and is somewhat hazardous. 3D virtual geological outcrop hold the potential to alleviate these issues. Efficiently segmenting massive 3D point clouds into individual planar facets, inside a convenient software environment was lacking. FACETS is a dedicated plugin within CloudCompare v2.6.2 (http://cloudcompare.org/ ) implemented to perform planar facet extraction, calculate their dip and dip direction (i.e. azimuth of steepest decent) and report the extracted data in interactive stereograms. Two algorithms perform the segmentation: Kd-Tree and Fast Marching. Both divide the point cloud into sub-cells, then compute elementary planar objects and aggregate them progressively according to a planeity threshold into polygons. The boundaries of the polygons are adjusted around segmented points with a tension parameter, and the facet polygons can be exported as 3D polygon shapefiles towards third party GIS software or simply as ASCII comma separated files. One of the great features of FACETS is the capability to explore planar objects but also 3D points with normals with the stereogram tool. Poles can be readily displayed, queried and manually segmented interactively. The plugin blends seamlessly into CloudCompare to leverage all its other 3D point cloud manipulation features. A demonstration of the tool is presented to illustrate these different features. While designed for geological applications, FACETS could be more widely applied to any planar objects.

The distinguishing signature of Magnetic Penrose Process

NASA Astrophysics Data System (ADS)

Dadhich, Naresh; Tursunov, Arman; Ahmedov, Bobomurat; Stuchlík, Zdeněk

2018-04-01

In this Letter, we wish to point out that the distinguishing feature of Magnetic Penrose process (MPP) is its super high efficiency exceeding 100% (which was established in mid 1980s for discrete particle accretion) of extraction of rotational energy of a rotating black hole electromagnetically for a magnetic field of milli Gauss order. Another similar process, which is also driven by electromagnetic field, is Blandford-Znajek mechanism (BZ), which could be envisaged as high magnetic field limit MPP as it requires threshold magnetic field of order 104G. Recent simulation studies of fully relativistic magnetohydrodynamic flows have borne out super high efficiency signature of the process for high magnetic field regime; viz BZ. We would like to make a clear prediction that similar simulation studies of MHD flows for low magnetic field regime, where BZ would be inoperative, would also have super efficiency.

Engagement Assessment Using EEG Signals

NASA Technical Reports Server (NTRS)

Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

2012-01-01

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

The optional selection of micro-motion feature based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Bo; Ren, Hongmei; Xiao, Zhi-he; Sheng, Jing

2017-11-01

Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.

PubMed

Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui

2017-08-17

It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.

Study of Burn Scar Extraction Automatically Based on Level Set Method using Remote Sensing Data

PubMed Central

Liu, Yang; Dai, Qin; Liu, JianBo; Liu, ShiBin; Yang, Jin

2014-01-01

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model. PMID:24503563

A judicious multiple hypothesis tracker with interacting feature extraction

NASA Astrophysics Data System (ADS)

McAnanama, James G.; Kirubarajan, T.

2009-05-01

The multiple hypotheses tracker (mht) is recognized as an optimal tracking method due to the enumeration of all possible measurement-to-track associations, which does not involve any approximation in its original formulation. However, its practical implementation is limited by the NP-hard nature of this enumeration. As a result, a number of maintenance techniques such as pruning and merging have been proposed to bound the computational complexity. It is possible to improve the performance of a tracker, mht or not, using feature information (e.g., signal strength, size, type) in addition to kinematic data. However, in most tracking systems, the extraction of features from the raw sensor data is typically independent of the subsequent association and filtering stages. In this paper, a new approach, called the Judicious Multi Hypotheses Tracker (jmht), whereby there is an interaction between feature extraction and the mht, is presented. The measure of the quality of feature extraction is input into measurement-to-track association while the prediction step feeds back the parameters to be used in the next round of feature extraction. The motivation for this forward and backward interaction between feature extraction and tracking is to improve the performance in both steps. This approach allows for a more rational partitioning of the feature space and removes unlikely features from the assignment problem. Simulation results demonstrate the benefits of the proposed approach.

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

PubMed

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection. The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features. Copyright © 2017 Elsevier B.V. All rights reserved.

Towards a More Efficient Detection of Earthquake Induced FAÇADE Damages Using Oblique Uav Imagery

NASA Astrophysics Data System (ADS)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2017-08-01

Urban search and rescue (USaR) teams require a fast and thorough building damage assessment, to focus their rescue efforts accordingly. Unmanned aerial vehicles (UAV) are able to capture relevant data in a short time frame and survey otherwise inaccessible areas after a disaster, and have thus been identified as useful when coupled with RGB cameras for façade damage detection. Existing literature focuses on the extraction of 3D and/or image features as cues for damage. However, little attention has been given to the efficiency of the proposed methods which hinders its use in an urban search and rescue context. The framework proposed in this paper aims at a more efficient façade damage detection using UAV multi-view imagery. This was achieved directing all damage classification computations only to the image regions containing the façades, hence discarding the irrelevant areas of the acquired images and consequently reducing the time needed for such task. To accomplish this, a three-step approach is proposed: i) building extraction from the sparse point cloud computed from the nadir images collected in an initial flight; ii) use of the latter as proxy for façade location in the oblique images captured in subsequent flights, and iii) selection of the façade image regions to be fed to a damage classification routine. The results show that the proposed framework successfully reduces the extracted façade image regions to be assessed for damage 6 fold, hence increasing the efficiency of subsequent damage detection routines. The framework was tested on a set of UAV multi-view images over a neighborhood of the city of L'Aquila, Italy, affected in 2009 by an earthquake.

Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

PubMed

Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

2017-08-20

Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93.86% for the Open Access Series of Imaging Studies (OASIS) database of MRI brain images, providing, compared to the best existing methods, a 3% lower error rate.

User-oriented summary extraction for soccer video based on multimodal analysis

NASA Astrophysics Data System (ADS)

Liu, Huayong; Jiang, Shanshan; He, Tingting

2011-11-01

An advanced user-oriented summary extraction method for soccer video is proposed in this work. Firstly, an algorithm of user-oriented summary extraction for soccer video is introduced. A novel approach that integrates multimodal analysis, such as extraction and analysis of the stadium features, moving object features, audio features and text features is introduced. By these features the semantic of the soccer video and the highlight mode are obtained. Then we can find the highlight position and put them together by highlight degrees to obtain the video summary. The experimental results for sports video of world cup soccer games indicate that multimodal analysis is effective for soccer video browsing and retrieval.

Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

NASA Astrophysics Data System (ADS)

Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

2017-01-01

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods

USGS Publications Warehouse

Germaine, Stephen S.; O'Donnell, Michael S.; Aldridge, Cameron L.; Baer, Lori; Fancher, Tammy; McBeth, Jamie; McDougal, Robert R.; Waltermire, Robert; Bowen, Zachary H.; Diffendorfer, James; Garman, Steven; Hanson, Leanne

2012-01-01

We evaluated how well three leading information-extraction software programs (eCognition, Feature Analyst, Feature Extraction) and manual hand digitization interpreted information from remotely sensed imagery of a visually complex gas field in Wyoming. Specifically, we compared how each mapped the area of and classified the disturbance features present on each of three remotely sensed images, including 30-meter-resolution Landsat, 10-meter-resolution SPOT (Satellite Pour l'Observation de la Terre), and 0.6-meter resolution pan-sharpened QuickBird scenes. Feature Extraction mapped the spatial area of disturbance features most accurately on the Landsat and QuickBird imagery, while hand digitization was most accurate on the SPOT imagery. Footprint non-overlap error was smallest on the Feature Analyst map of the Landsat imagery, the hand digitization map of the SPOT imagery, and the Feature Extraction map of the QuickBird imagery. When evaluating feature classification success against a set of ground-truthed control points, Feature Analyst, Feature Extraction, and hand digitization classified features with similar success on the QuickBird and SPOT imagery, while eCognition classified features poorly relative to the other methods. All maps derived from Landsat imagery classified disturbance features poorly. Using the hand digitized QuickBird data as a reference and making pixel-by-pixel comparisons, Feature Extraction classified features best overall on the QuickBird imagery, and Feature Analyst classified features best overall on the SPOT and Landsat imagery. Based on the entire suite of tasks we evaluated, Feature Extraction performed best overall on the Landsat and QuickBird imagery, while hand digitization performed best overall on the SPOT imagery, and eCognition performed worst overall on all three images. Error rates for both area measurements and feature classification were prohibitively high on Landsat imagery, while QuickBird was time and cost prohibitive for mapping large spatial extents. The SPOT imagery produced map products that were far more accurate than Landsat and did so at a far lower cost than QuickBird imagery. Consideration of degree of map accuracy required, costs associated with image acquisition, software, operator and computation time, and tradeoffs in the form of spatial extent versus resolution should all be considered when evaluating which combination of imagery and information-extraction method might best serve any given land use mapping project. When resources permit, attaining imagery that supports the highest classification and measurement accuracy possible is recommended.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

iFeature: a python package and web server for features extraction and selection from protein and peptide sequences.

PubMed

Chen, Zhen; Zhao, Pei; Li, Fuyi; Leier, André; Marquez-Lago, Tatiana T; Wang, Yanan; Webb, Geoffrey I; Smith, A Ian; Daly, Roger J; Chou, Kuo-Chen; Song, Jiangning

2018-03-08

Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection, and dimensionality reduction algorithms, greatly facilitating training, analysis, and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. jiangning.song@monash.edu; kcchou@gordonlifescience.org; roger.daly@monash.edu. Supplementary data are available at Bioinformatics online.

CNN universal machine as classificaton platform: an art-like clustering algorithm.

PubMed

Bálya, David

2003-12-01

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

NASA Astrophysics Data System (ADS)

Hannel, Mark D.; Abdulali, Aidan; O'Brien, Michael; Grier, David G.

2018-06-01

Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive indexes. Extracting this wealth of information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques. We demonstrate that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN), can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques. Our CNN implementation localizes holographic features precisely enough to bootstrap more detailed analyses based on the Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade proves to be less precise, but is so computationally efficient that it creates new opportunities for applications that emphasize speed and low cost. We demonstrate its use as a real-time targeting system for holographic optical trapping.

A new technique for solving puzzles.

PubMed

Makridis, Michael; Papamarkos, Nikos

2010-06-01

This paper proposes a new technique for solving jigsaw puzzles. The novelty of the proposed technique is that it provides an automatic jigsaw puzzle solution without any initial restriction about the shape of pieces, the number of neighbor pieces, etc. The proposed technique uses both curve- and color-matching similarity features. A recurrent procedure is applied, which compares and merges puzzle pieces in pairs, until the original puzzle image is reformed. Geometrical and color features are extracted on the characteristic points (CPs) of the puzzle pieces. CPs, which can be considered as high curvature points, are detected by a rotationally invariant corner detection algorithm. The features which are associated with color are provided by applying a color reduction technique using the Kohonen self-organized feature map. Finally, a postprocessing stage checks and corrects the relative position between puzzle pieces to improve the quality of the resulting image. Experimental results prove the efficiency of the proposed technique, which can be further extended to deal with even more complex jigsaw puzzle problems.

Method and system for data clustering for very large databases

NASA Technical Reports Server (NTRS)

Livny, Miron (Inventor); Zhang, Tian (Inventor); Ramakrishnan, Raghu (Inventor)

1998-01-01

Multi-dimensional data contained in very large databases is efficiently and accurately clustered to determine patterns therein and extract useful information from such patterns. Conventional computer processors may be used which have limited memory capacity and conventional operating speed, allowing massive data sets to be processed in a reasonable time and with reasonable computer resources. The clustering process is organized using a clustering feature tree structure wherein each clustering feature comprises the number of data points in the cluster, the linear sum of the data points in the cluster, and the square sum of the data points in the cluster. A dense region of data points is treated collectively as a single cluster, and points in sparsely occupied regions can be treated as outliers and removed from the clustering feature tree. The clustering can be carried out continuously with new data points being received and processed, and with the clustering feature tree being restructured as necessary to accommodate the information from the newly received data points.

Linear feature detection algorithm for astronomical surveys - I. Algorithm description

NASA Astrophysics Data System (ADS)

Bektešević, Dino; Vinković, Dejan

2017-11-01

Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars and galaxies, ignoring completely the detection of existing linear features. With the emergence of wide-field sky surveys, linear features attract scientific interest as possible trails of fast flybys of near-Earth asteroids and meteors. In this work, we describe a new linear feature detection algorithm designed specifically for implementation in big data astronomy. The algorithm combines a series of algorithmic steps that first remove other objects (stars and galaxies) from the image and then enhance the line to enable more efficient line detection with the Hough algorithm. The rate of false positives is greatly reduced thanks to a step that replaces possible line segments with rectangles and then compares lines fitted to the rectangles with the lines obtained directly from the image. The speed of the algorithm and its applicability in astronomical surveys are also discussed.

Cross-Domain Shoe Retrieval with a Semantic Hierarchy of Attribute Classification Network.

PubMed

Zhan, Huijing; Shi, Boxin; Kot, Alex C

2017-08-04

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe dataset composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.

Design of a lighting system with high-power LEDs, large area electronics, and light management structure in the LUMENTILE European project

NASA Astrophysics Data System (ADS)

Carraro, L.; Simonetta, M.; Benetti, G.; Tramonte, A.; Capelli, G.; Benedetti, M.; Randone, E. M.; Ylisaukko-oja, A.; Keränen, K.; Facchinetti, T.; Giuliani, G.

2017-02-01

LUMENTILE (LUMinous ElectroNic TILE) is a project funded by the European Commission with the goal of developing a luminous tile with novel functionalities, capable of changing its color and interact with the user. Applications include interior/exterior tile for walls and floors covering, high-efficiency luminaries, and advertising under the form of giant video screens. High overall electrical efficiency of the tile is mandatory, as several millions of square meters are foreseen to be installed each year. Demand is for high uniformity of the illumination of the top tile surface, and for high optical extraction efficiency. These features are achieved by smart light management, using a new approach based on light guiding slab and spatially selective light extraction obtained using both diffusion and/or reflection from the top and bottom interfaces of the optical layer. Planar and edge configurations for the RGB LEDs are considered and compared. A square shape with side length from 20cm to 60cm is considered for the tiles. The electronic circuit layout must optimize the electrical efficiency, and be compatible with low-cost roll-to-roll production on flexible substrates. LED heat management is tackled by using dedicated solutions that allow operation in thermally harsh environment. An approach based on OLEDs has also been considered, still needing improvement on emitted power and ruggedness.

An alternative respiratory sounds classification system utilizing artificial neural networks.

PubMed

Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen

2015-01-01

Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

Biologically-inspired data decorrelation for hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Picon, Artzai; Ghita, Ovidiu; Rodriguez-Vaamonde, Sergio; Iriondo, Pedro Ma; Whelan, Paul F.

2011-12-01

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

Fake currency detection using image processing

NASA Astrophysics Data System (ADS)

Agasti, Tushar; Burand, Gajanan; Wade, Pratik; Chitra, P.

2017-11-01

The advancement of color printing technology has increased the rate of fake currency note printing and duplicating the notes on a very large scale. Few years back, the printing could be done in a print house, but now anyone can print a currency note with maximum accuracy using a simple laser printer. As a result the issue of fake notes instead of the genuine ones has been increased very largely. India has been unfortunately cursed with the problems like corruption and black money. And counterfeit of currency notes is also a big problem to it. This leads to design of a system that detects the fake currency note in a less time and in a more efficient manner. The proposed system gives an approach to verify the Indian currency notes. Verification of currency note is done by the concepts of image processing. This article describes extraction of various features of Indian currency notes. MATLAB software is used to extract the features of the note. The proposed system has got advantages like simplicity and high performance speed. The result will predict whether the currency note is fake or not.

A Bio Medical Waste Identification and Classification Algorithm Using Mltrp and Rvm.

PubMed

Achuthan, Aravindan; Ayyallu Madangopal, Vasumathi

2016-10-01

We aimed to extract the histogram features for text analysis and, to classify the types of Bio Medical Waste (BMW) for garbage disposal and management. The given BMW was preprocessed by using the median filtering technique that efficiently reduced the noise in the image. After that, the histogram features of the filtered image were extracted with the help of proposed Modified Local Tetra Pattern (MLTrP) technique. Finally, the Relevance Vector Machine (RVM) was used to classify the BMW into human body parts, plastics, cotton and liquids. The BMW image was collected from the garbage image dataset for analysis. The performance of the proposed BMW identification and classification system was evaluated in terms of sensitivity, specificity, classification rate and accuracy with the help of MATLAB. When compared to the existing techniques, the proposed techniques provided the better results. This work proposes a new texture analysis and classification technique for BMW management and disposal. It can be used in many real time applications such as hospital and healthcare management systems for proper BMW disposal.

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

PubMed

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

Pyramid image codes

NASA Technical Reports Server (NTRS)

Watson, Andrew B.

1990-01-01

All vision systems, both human and machine, transform the spatial image into a coded representation. Particular codes may be optimized for efficiency or to extract useful image features. Researchers explored image codes based on primary visual cortex in man and other primates. Understanding these codes will advance the art in image coding, autonomous vision, and computational human factors. In cortex, imagery is coded by features that vary in size, orientation, and position. Researchers have devised a mathematical model of this transformation, called the Hexagonal oriented Orthogonal quadrature Pyramid (HOP). In a pyramid code, features are segregated by size into layers, with fewer features in the layers devoted to large features. Pyramid schemes provide scale invariance, and are useful for coarse-to-fine searching and for progressive transmission of images. The HOP Pyramid is novel in three respects: (1) it uses a hexagonal pixel lattice, (2) it uses oriented features, and (3) it accurately models most of the prominent aspects of primary visual cortex. The transform uses seven basic features (kernels), which may be regarded as three oriented edges, three oriented bars, and one non-oriented blob. Application of these kernels to non-overlapping seven-pixel neighborhoods yields six oriented, high-pass pyramid layers, and one low-pass (blob) layer.

Robust digital image watermarking using distortion-compensated dither modulation

NASA Astrophysics Data System (ADS)

Li, Mianjie; Yuan, Xiaochen

2018-04-01

In this paper, we propose a robust feature extraction based digital image watermarking method using Distortion- Compensated Dither Modulation (DC-DM). Our proposed local watermarking method provides stronger robustness and better flexibility than traditional global watermarking methods. We improve robustness by introducing feature extraction and DC-DM method. To extract the robust feature points, we propose a DAISY-based Robust Feature Extraction (DRFE) method by employing the DAISY descriptor and applying the entropy calculation based filtering. The experimental results show that the proposed method achieves satisfactory robustness under the premise of ensuring watermark imperceptibility quality compared to other existing methods.

[Study of cuttings identification using laser-induced breakdown spectroscopy].

PubMed

Tian, Ye; Wang, Zhen-nan; Hou, Hua-ming; Zhai, Xiao-wei; Ci, Xing-hua; Zheng, Rong-er

2012-08-01

Cutting identification is one of the most important links in the course of cutting logging which is very significant in the process of oil drilling. In the present paper, LIBS was used for identification of four kinds of cutting samples coming from logging field, and then multivariate analysis was used in data processing. The whole spectra model and the feature model were built for cuttings identification using PLS-DA method. The accuracy of the whole spectra model was 88.3%, a little more than the feature model with an accuracy of 86.7%. While in the aspect of data size, the variables were decreased from 24,041 to 27 by feature extraction, which increased the efficiency of data processing observably. The obtained results demonstrate that LIBS combined with chemometrics method could be developed as a rapid and valid approach to cutting identification and has great potential to be used in logging field.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Comparison of three methods for long-term monitoring of boreal lake area using Landsat TM and ETM+ imagery

USGS Publications Warehouse

Roach, Jennifer K.; Griffith, Brad; Verbyla, David

2012-01-01

Programs to monitor lake area change are becoming increasingly important in high latitude regions, and their development often requires evaluating tradeoffs among different approaches in terms of accuracy of measurement, consistency across multiple users over long time periods, and efficiency. We compared three supervised methods for lake classification from Landsat imagery (density slicing, classification trees, and feature extraction). The accuracy of lake area and number estimates was evaluated relative to high-resolution aerial photography acquired within two days of satellite overpasses. The shortwave infrared band 5 was better at separating surface water from nonwater when used alone than when combined with other spectral bands. The simplest of the three methods, density slicing, performed best overall. The classification tree method resulted in the most omission errors (approx. 2x), feature extraction resulted in the most commission errors (approx. 4x), and density slicing had the least directional bias (approx. half of the lakes with overestimated area and half of the lakes with underestimated area). Feature extraction was the least consistent across training sets (i.e., large standard error among different training sets). Density slicing was the best of the three at classifying small lakes as evidenced by its lower optimal minimum lake size criterion of 5850 m2 compared with the other methods (8550 m2). Contrary to conventional wisdom, the use of additional spectral bands and a more sophisticated method not only required additional processing effort but also had a cost in terms of the accuracy and consistency of lake classifications.

An Investigation of Aggregation in Synergistic Solvent Extraction Systems

NASA Astrophysics Data System (ADS)

Jackson, Andy Steven

With an increasing focus on anthropogenic climate change, nuclear reactors present an attractive option for base load power generation with regard to air pollution and carbon emissions, especially when compared with traditional fossil fuel based options. However, used nuclear fuel (UNF) is highly radiotoxic and contains minor actinides (americium and curium) which remain more radiotoxic than natural uranium ore for hundreds of thousands of years, presenting a challenge for long-term storage . Advanced nuclear fuel recycling can reduce this required storage time to thousands of years by removing the highly radiotoxic minor actinides. Many advanced separation schemes have been proposed to achieve this separation but none have been implemented to date. A key feature among many proposed schemes is the use of more than one extraction reagent in a single extraction phase, which can lead to the phenomenon known as "synergism" in which the extraction efficiency for a combination of the reagents is greater than that of the individual extractants alone. This feature is not well understood for many systems and a comprehensive picture of the mechanism behind synergism does not exist. There are several proposed mechanisms for synergism though none have been used to model multiple extraction systems. This work examines several proposed advanced extractant combinations which exhibit synergism: 2-bromodecanoic acid (BDA) with 2,2':6',2"-terpyridine (TERPY), tri-n-butylphosphine oxide (TPBO) with 2-thenoyltrifluoro acetone (HTTA), and dinonylnaphthalene sulfonic acid (HDNNS) with 5,8-diethyl-7-hydroxy-dodecan-6-oxime (LIX). We examine two proposed synergistic mechanisms involving and attempt to verify the ability of these mechanisms to predict the extraction behavior of the chosen systems. These are a reverse micellar catalyzed extraction model and a mixed complex formation model. Neither was able to effectively predict the synergistic behavior of the systems. We further examine these systems for the presence of large reverse micellar aggregates and thermodynamic signatures of aggregation. Behaviors differed widely from system to system, suggesting the possibility of more than one mechanism being responsible for similar observed extraction trends.

Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

NASA Astrophysics Data System (ADS)

Li, Quanbao; Wei, Fajie; Zhou, Shenghan

2017-05-01

The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

PubMed Central

Wen, Tingxi; Zhang, Zhongnan

2017-01-01

Abstract In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy. PMID:28489789

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification.

PubMed

Wen, Tingxi; Zhang, Zhongnan

2017-05-01

In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach.

PubMed

Nasir, Muhammad; Attique Khan, Muhammad; Sharif, Muhammad; Lali, Ikram Ullah; Saba, Tanzila; Iqbal, Tassawar

2018-02-21

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods. © 2018 Wiley Periodicals, Inc.

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

PubMed Central

Tsai, Yu Hsin; Stow, Douglas; Weeks, John

2013-01-01

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. PMID:24415810

Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.

PubMed

Sabooh, M Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H F

2018-05-01

This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (m 5 C) plays a vital role in a number of biological processes. For better comprehension of the biological functions and mechanism it is necessary to recognize m 5 C sites in RNA precisely. The laboratory techniques and procedures are available to identify m 5 C sites in RNA, but these procedures require a lot of time and resources. This study develops a new computational method for extracting the features of RNA sequence. In this method, first the RNA sequence is encoded via composite feature vector, then, for the selection of discriminate features, the minimum-redundancy-maximum-relevance algorithm was used. Secondly, the classification method used has been based on a support vector machine by using jackknife cross validation test. The suggested method efficiently identifies m 5 C sites from non- m 5 C sites and the outcome of the suggested algorithm is 93.33% with sensitivity of 90.0 and specificity of 96.66 on bench mark datasets. The result exhibits that proposed algorithm shown significant identification performance compared to the existing computational techniques. This study extends the knowledge about the occurrence sites of RNA modification which paves the way for better comprehension of the biological uses and mechanism. Copyright © 2018 Elsevier Ltd. All rights reserved.

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

Extraction of ECG signal with adaptive filter for hearth abnormalities detection

NASA Astrophysics Data System (ADS)

Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti

2018-04-01

This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

Recursive Feature Extraction in Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-08-14

ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

PubMed Central

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

PubMed Central

Deng, Ning

2014-01-01

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.

PubMed

Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning

2014-01-01

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering.

PubMed

Rodríguez-Sotelo, J L; Peluffo-Ordoñez, D; Cuesta-Frau, D; Castellanos-Domínguez, G

2012-10-01

The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Robust image features: concentric contrasting circles and their image extraction

NASA Astrophysics Data System (ADS)

Gatrell, Lance B.; Hoff, William A.; Sklair, Cheryl W.

1992-03-01

Many computer vision tasks can be simplified if special image features are placed on the objects to be recognized. A review of special image features that have been used in the past is given and then a new image feature, the concentric contrasting circle, is presented. The concentric contrasting circle image feature has the advantages of being easily manufactured, easily extracted from the image, robust extraction (true targets are found, while few false targets are found), it is a passive feature, and its centroid is completely invariant to the three translational and one rotational degrees of freedom and nearly invariant to the remaining two rotational degrees of freedom. There are several examples of existing parallel implementations which perform most of the extraction work. Extraction robustness was measured by recording the probability of correct detection and the false alarm rate in a set of images of scenes containing mockups of satellites, fluid couplings, and electrical components. A typical application of concentric contrasting circle features is to place them on modeled objects for monocular pose estimation or object identification. This feature is demonstrated on a visually challenging background of a specular but wrinkled surface similar to a multilayered insulation spacecraft thermal blanket.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

Evaluation of polyethersulfone performance for the microextraction of polar chlorinated herbicides from environmental water samples.

PubMed

Prieto, Ailette; Rodil, Rosario; Quintana, José Benito; Cela, Rafael; Möder, Monika; Rodríguez, Isaac

2014-05-01

In this work, the suitability of bulk polyethersulfone (PES) for sorptive microextraction of eight polar, chlorinated phenoxy acids and dicamba from environmental water samples is assessed and the analytical features of the optimized method are compared to those reported for other microextraction techniques. Under optimized conditions, extractions were performed with samples (18 mL) adjusted at pH 2 and containing a 30% (w/v) of sodium chloride, using a tubular PES sorbent (1 cm length × 0.7 mm o.d., sorbent volume 8 µL). Equilibrium conditions were achieved after 3h of direct sampling, with absolute extraction efficiencies ranging from 39 to 66%, depending on the compound. Analytes were recovered soaking the polymer with 0.1 mL of ethyl acetate, derivatized and determined by gas chromatography-mass spectrometry (GC-MS). Achieved quantification limits (LOQs) varied between 0.005 and 0.073 ng mL(-1). After normalization with the internal surrogate (IS), the efficiency of the extraction was only moderately affected by the particular characteristics of different water samples (surface and sewage water); thus, pseudo-external calibration, using spiked ultrapure water solutions, can be used as quantification technique. The reduced cost of the PES polymer allowed considering it as a disposable sorbent, avoiding variations in the performance of the extraction due to cross-contamination problems and/or surface modification with usage. Copyright © 2014 Elsevier B.V. All rights reserved.

Wire bonding quality monitoring via refining process of electrical signal from ultrasonic generator

NASA Astrophysics Data System (ADS)

Feng, Wuwei; Meng, Qingfeng; Xie, Youbo; Fan, Hong

2011-04-01

In this paper, a technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis (PCA) method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

A Hybrid Neural Network and Feature Extraction Technique for Target Recognition.

DTIC Science & Technology

target features are extracted, the extracted data being evaluated in an artificial neural network to identify a target at a location within the image scene from which the different viewing angles extend.

Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

NASA Astrophysics Data System (ADS)

Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

2017-09-01

Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

Decision rules for unbiased inventory estimates

NASA Technical Reports Server (NTRS)

Argentiero, P. D.; Koch, D.

1979-01-01

An efficient and accurate procedure for estimating inventories from remote sensing scenes is presented. In place of the conventional and expensive full dimensional Bayes decision rule, a one-dimensional feature extraction and classification technique was employed. It is shown that this efficient decision rule can be used to develop unbiased inventory estimates and that for large sample sizes typical of satellite derived remote sensing scenes, resulting accuracies are comparable or superior to more expensive alternative procedures. Mathematical details of the procedure are provided in the body of the report and in the appendix. Results of a numerical simulation of the technique using statistics obtained from an observed LANDSAT scene are included. The simulation demonstrates the effectiveness of the technique in computing accurate inventory estimates.

Content Based Image Retrieval based on Wavelet Transform coefficients distribution

PubMed Central

Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

2007-01-01

In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013

Micropropagation, antinociceptive and antioxidant activities of extracts of Verbena litoralis Kunth (Verbenaceae).

PubMed

Braga, Virgínia F; Mendes, Giselle C; Oliveira, Raphael T R; Soares, Carla Q G; Resende, Cristiano F; Pinto, Leandro C; Santana, Reinaldo de; Viccini, Lyderson F; Raposo, Nádia R B; Peixoto, Paulo H P

2012-03-01

This work describes an efficient micropropagation protocol for Verbena litoralis and the study of the antinociceptive and antioxidant activities in extracts of this species. For the establishment in vitro, surface-sterilization procedures and PVPP showed high efficiency in fungal-bacterial contamination and phenol oxidation controls. Nodal segments cultivation in MS medium supplemented with 6-benzyladenine (7.5 µM)/α-naphthaleneacetic acid (NAA; 0.005 µM) induced multiple shoots. Elongated shoots were rooted with IAA (0.2 µM). Acclimatization rates were elevated and the plants showed the typical features of this species. The hexanic fraction (HF) of powdered leaves presented a radical scavenging activity with IC(50) = 169.3 µg mL(-1). HF showed a non-dose dependent analgesic activity in the writhing test; its antinociceptive activity in the hot plate test was restricted to 500 mg kg(-1), which is the highest dose. The results of this study showed the potential of tissue culture on conservation and large scale multiplication and confirmed the traditional folk medicine use of V. litoralis.

Optimal design of a bank of spatio-temporal filters for EEG signal classification.

PubMed

Higashi, Hiroshi; Tanaka, Toshihisa

2011-01-01

The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interfaces (MI-BCI). To achieve accurate classification in CSP, the frequency filter should be properly designed. To this end, several methods for designing the filter have been proposed. However, the existing methods cannot consider plural brain activities described with different frequency bands and different spatial patterns such as activities of mu and beta rhythms. In order to efficiently extract these brain activities, we propose a method to design plural filters and spatial weights which extract desired brain activity. The proposed method designs finite impulse response (FIR) filters and the associated spatial weights by optimization of an objective function which is a natural extension of CSP. Moreover, we show by a classification experiment that the bank of FIR filters which are designed by introducing an orthogonality into the objective function can extract good discriminative features. Moreover, the experiment result suggests that the proposed method can automatically detect and extract brain activities related to motor imagery.

Evidence for a Global Sampling Process in Extraction of Summary Statistics of Item Sizes in a Set.

PubMed

Tokita, Midori; Ueda, Sachiyo; Ishiguchi, Akira

2016-01-01

Several studies have shown that our visual system may construct a "summary statistical representation" over groups of visual objects. Although there is a general understanding that human observers can accurately represent sets of a variety of features, many questions on how summary statistics, such as an average, are computed remain unanswered. This study investigated sampling properties of visual information used by human observers to extract two types of summary statistics of item sets, average and variance. We presented three models of ideal observers to extract the summary statistics: a global sampling model without sampling noise, global sampling model with sampling noise, and limited sampling model. We compared the performance of an ideal observer of each model with that of human observers using statistical efficiency analysis. Results suggest that summary statistics of items in a set may be computed without representing individual items, which makes it possible to discard the limited sampling account. Moreover, the extraction of summary statistics may not necessarily require the representation of individual objects with focused attention when the sets of items are larger than 4.

Energy Extraction from a Slider-Crank Wave Energy under Irregular Wave Conditions: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sang, Yuanrui; Karayaka, H. Bora; Yan, Yanjun

2015-08-24

A slider-crank wave energy converter (WEC) is a novel energy conversion device. It converts wave energy into electricity at a relatively high efficiency, and it features a simple structure. Past analysis on this particular WEC has been done under regular sinusoidal wave conditions, and suboptimal energy could be achieved. This paper presents the analysis of the system under irregular wave conditions; a time-domain hydrodynamics model is adopted and a rule-based control methodology is introduced to better serve the irregular wave conditions. Results from the simulations show that the performance of the system under irregular wave conditions is different from thatmore » under regular sinusoidal wave conditions, but a reasonable amount of energy can still be extracted.« less

Energy Extraction from a Slider-Crank Wave Energy Converter under Irregular Wave Conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sang, Yuanrui; Karayaka, H. Bora; Yan, Yanjun

2015-10-19

A slider-crank wave energy converter (WEC) is a novel energy conversion device. It converts wave energy into electricity at a relatively high efficiency, and it features a simple structure. Past analysis on this particular WEC has been done under regular sinusoidal wave conditions, and suboptimal energy could be achieved. This paper presents the analysis of the system under irregular wave conditions; a time-domain hydrodynamics model is adopted and a rule-based control methodology is introduced to better serve the irregular wave conditions. Results from the simulations show that the performance of the system under irregular wave conditions is different from thatmore » under regular sinusoidal wave conditions, but a reasonable amount of energy can still be extracted.« less

Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection

NASA Astrophysics Data System (ADS)

Chen, Jingbo; Yue, Anzhi; Wang, Chengyi; Huang, Qingqing; Chen, Jiansheng; Meng, Yu; He, Dongxu

2018-01-01

The wind turbine is a device that converts the wind's kinetic energy into electrical power. Accurate and automatic extraction of wind turbine is instructive for government departments to plan wind power plant projects. A hybrid and practical framework based on saliency detection for wind turbine extraction, using Google Earth image at spatial resolution of 1 m, is proposed. It can be viewed as a two-phase procedure: coarsely detection and fine extraction. In the first stage, we introduced a frequency-tuned saliency detection approach for initially detecting the area of interest of the wind turbines. This method exploited features of color and luminance, was simple to implement, and was computationally efficient. Taking into account the complexity of remote sensing images, in the second stage, we proposed a fast method for fine-tuning results in frequency domain and then extracted wind turbines from these salient objects by removing the irrelevant salient areas according to the special properties of the wind turbines. Experiments demonstrated that our approach consistently obtains higher precision and better recall rates. Our method was also compared with other techniques from the literature and proves that it is more applicable and robust.

A High-Speed Vision-Based Sensor for Dynamic Vibration Analysis Using Fast Motion Extraction Algorithms.

PubMed

Zhang, Dashan; Guo, Jie; Lei, Xiujun; Zhu, Changan

2016-04-22

The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.

Stroke-model-based character extraction from gray-level document images.

PubMed

Ye, X; Cheriet, M; Suen, C Y

2001-01-01

Global gray-level thresholding techniques such as Otsu's method, and local gray-level thresholding techniques such as edge-based segmentation or the adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. A stroke-model is proposed to depict the local features of character objects as double-edges in a predefined size. This model enables us to detect thin connected components selectively, while ignoring relatively large backgrounds that appear complex. Meanwhile, since the stroke width restriction is fully factored in, the proposed technique can be used to extract characters in predefined font sizes. To process large volumes of documents efficiently, a hybrid method is proposed for character extraction from various backgrounds. Using the measurement of class separability to differentiate images with simple backgrounds from those with complex backgrounds, the hybrid method can process documents with different backgrounds by applying the appropriate methods. Experiments on extracting handwriting from a check image, as well as machine-printed characters from scene images demonstrate the effectiveness of the proposed model.