Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Ultrasound image edge detection based on a novel multiplicative gradient and Canny operator.

PubMed

Zheng, Yinfei; Zhou, Yali; Zhou, Hao; Gong, Xiaohong

2015-07-01

To achieve the fast and accurate segmentation of ultrasound image, a novel edge detection method for speckle noised ultrasound images was proposed, which was based on the traditional Canny and a novel multiplicative gradient operator. The proposed technique combines a new multiplicative gradient operator of non-Newtonian type with the traditional Canny operator to generate the initial edge map, which is subsequently optimized by the following edge tracing step. To verify the proposed method, we compared it with several other edge detection methods that had good robustness to noise, with experiments on the simulated and in vivo medical ultrasound image. Experimental results showed that the proposed algorithm has higher speed for real-time processing, and the edge detection accuracy could be 75% or more. Thus, the proposed method is very suitable for fast and accurate edge detection of medical ultrasound images. © The Author(s) 2014.

Latent component-based gear tooth fault detection filter using advanced parametric modeling

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.; Sadeghi, M. H.; Rezaee, M.; Chitsaz, S.

2009-10-01

In this paper, a new parametric model-based filter is proposed for gear tooth fault detection. The designing of the filter consists of identifying the most proper latent component (LC) of the undamaged gearbox signal by analyzing the instant modules (IMs) and instant frequencies (IFs) and then using the component with lowest IM as the proposed filter output for detecting fault of the gearbox. The filter parameters are estimated by using the LC theory in which an advanced parametric modeling method has been implemented. The proposed method is applied on the signals, extracted from simulated gearbox for detection of the simulated gear faults. In addition, the method is used for quality inspection of the produced Nissan-Junior vehicle gearbox by gear profile error detection in an industrial test bed. For evaluation purpose, the proposed method is compared with the previous parametric TAR/AR-based filters in which the parametric model residual is considered as the filter output and also Yule-Walker and Kalman filter are implemented for estimating the parameters. The results confirm the high performance of the new proposed fault detection method.

Detection of alpha-fetoprotein in magnetic immunoassay of thin channels using biofunctional nanoparticles

NASA Astrophysics Data System (ADS)

Tsai, H. Y.; Gao, B. Z.; Yang, S. F.; Li, C. S.; Fuh, C. Bor

2014-01-01

This paper presents the use of fluorescent biofunctional nanoparticles (10-30 nm) to detect alpha-fetoprotein (AFP) in a thin-channel magnetic immunoassay. We used an AFP model biomarker and s-shaped deposition zones to test the proposed detection method. The results show that the detection using fluorescent biofunctional nanoparticle has a higher throughput than that of functional microparticle used in previous experiments on affinity reactions. The proposed method takes about 3 min (versus 150 min of previous method) to detect 100 samples. The proposed method is useful for screening biomarkers in clinical applications, and can reduce the run time for sandwich immunoassays to less than 20 min. The detection limits (0.06 pg/ml) and linear ranges (0.068 pg/ml-0.68 ng/ml) of AFP using fluorescent biofunctional nanoparticles are the same as those of using functional microparticles within experimental errors. This detection limit is substantially lower and the linear range is considerably wider than those of enzyme-linked immunosorbent assay (ELISA) and other methods in sandwich immunoassay methods. The differences between this method and an ELISA in AFP measurements of serum samples were less than 12 %. The proposed method provides simple, fast, and sensitive detection with a high throughput for biomarkers.

Airplane detection in remote sensing images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.

PubMed

Levman, Jacob E D; Gallego-Ortiz, Cristina; Warner, Ellen; Causer, Petrina; Martel, Anne L

2016-02-01

Magnetic resonance imaging (MRI)-enabled cancer screening has been shown to be a highly sensitive method for the early detection of breast cancer. Computer-aided detection systems have the potential to improve the screening process by standardizing radiologists to a high level of diagnostic accuracy. This retrospective study was approved by the institutional review board of Sunnybrook Health Sciences Centre. This study compares the performance of a proposed method for computer-aided detection (based on the second-order spatial derivative of the relative signal intensity) with the signal enhancement ratio (SER) on MRI-based breast screening examinations. Comparison is performed using receiver operating characteristic (ROC) curve analysis as well as free-response receiver operating characteristic (FROC) curve analysis. A modified computer-aided detection system combining the proposed approach with the SER method is also presented. The proposed method provides improvements in the rates of false positive markings over the SER method in the detection of breast cancer (as assessed by FROC analysis). The modified computer-aided detection system that incorporates both the proposed method and the SER method yields ROC results equal to that produced by SER while simultaneously providing improvements over the SER method in terms of false positives per noncancerous exam. The proposed method for identifying malignancies outperforms the SER method in terms of false positives on a challenging dataset containing many small lesions and may play a useful role in breast cancer screening by MRI as part of a computer-aided detection system.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Robust vehicle detection under various environmental conditions using an infrared thermal camera and its application to road traffic flow monitoring.

PubMed

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-06-17

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as "our previous method") using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as "our new method"). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal.

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade.

PubMed

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-08-14

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade.

Significance of parametric spectral ratio methods in detection and recognition of whispered speech

NASA Astrophysics Data System (ADS)

Mathur, Arpit; Reddy, Shankar M.; Hegde, Rajesh M.

2012-12-01

In this article the significance of a new parametric spectral ratio method that can be used to detect whispered speech segments within normally phonated speech is described. Adaptation methods based on the maximum likelihood linear regression (MLLR) are then used to realize a mismatched train-test style speech recognition system. This proposed parametric spectral ratio method computes a ratio spectrum of the linear prediction (LP) and the minimum variance distortion-less response (MVDR) methods. The smoothed ratio spectrum is then used to detect whispered segments of speech within neutral speech segments effectively. The proposed LP-MVDR ratio method exhibits robustness at different SNRs as indicated by the whisper diarization experiments conducted on the CHAINS and the cell phone whispered speech corpus. The proposed method also performs reasonably better than the conventional methods for whisper detection. In order to integrate the proposed whisper detection method into a conventional speech recognition engine with minimal changes, adaptation methods based on the MLLR are used herein. The hidden Markov models corresponding to neutral mode speech are adapted to the whispered mode speech data in the whispered regions as detected by the proposed ratio method. The performance of this method is first evaluated on whispered speech data from the CHAINS corpus. The second set of experiments are conducted on the cell phone corpus of whispered speech. This corpus is collected using a set up that is used commercially for handling public transactions. The proposed whisper speech recognition system exhibits reasonably better performance when compared to several conventional methods. The results shown indicate the possibility of a whispered speech recognition system for cell phone based transactions.

Design of Passive Power Filter for Hybrid Series Active Power Filter using Estimation, Detection and Classification Method

NASA Astrophysics Data System (ADS)

Swain, Sushree Diptimayee; Ray, Pravat Kumar; Mohanty, K. B.

2016-06-01

This research paper discover the design of a shunt Passive Power Filter (PPF) in Hybrid Series Active Power Filter (HSAPF) that employs a novel analytic methodology which is superior than FFT analysis. This novel approach consists of the estimation, detection and classification of the signals. The proposed method is applied to estimate, detect and classify the power quality (PQ) disturbance such as harmonics. This proposed work deals with three methods: the harmonic detection through wavelet transform method, the harmonic estimation by Kalman Filter algorithm and harmonic classification by decision tree method. From different type of mother wavelets in wavelet transform method, the db8 is selected as suitable mother wavelet because of its potency on transient response and crouched oscillation at frequency domain. In harmonic compensation process, the detected harmonic is compensated through Hybrid Series Active Power Filter (HSAPF) based on Instantaneous Reactive Power Theory (IRPT). The efficacy of the proposed method is verified in MATLAB/SIMULINK domain and as well as with an experimental set up. The obtained results confirm the superiority of the proposed methodology than FFT analysis. This newly proposed PPF is used to make the conventional HSAPF more robust and stable.

An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

NASA Astrophysics Data System (ADS)

Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

2018-01-01

In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

Detection of admittivity anomaly on high-contrast heterogeneous backgrounds using frequency difference EIT.

PubMed

Jang, J; Seo, J K

2015-06-01

This paper describes a multiple background subtraction method in frequency difference electrical impedance tomography (fdEIT) to detect an admittivity anomaly from a high-contrast background conductivity distribution. The proposed method expands the use of the conventional weighted frequency difference EIT method, which has been used limitedly to detect admittivity anomalies in a roughly homogeneous background. The proposed method can be viewed as multiple weighted difference imaging in fdEIT. Although the spatial resolutions of the output images by fdEIT are very low due to the inherent ill-posedness, numerical simulations and phantom experiments of the proposed method demonstrate its feasibility to detect anomalies. It has potential application in stroke detection in a head model, which is highly heterogeneous due to the skull.

Comprehensive Detection of Gas Plumes from Multibeam Water Column Images with Minimisation of Noise Interferences

PubMed Central

Zhao, Jianhu; Zhang, Hongmei; Wang, Shiqi

2017-01-01

Multibeam echosounder systems (MBES) can record backscatter strengths of gas plumes in the water column (WC) images that may be an indicator of possible occurrence of gas at certain depths. Manual or automatic detection is generally adopted in finding gas plumes, but frequently results in low efficiency and high false detection rates because of WC images that are polluted by noise. To improve the efficiency and reliability of the detection, a comprehensive detection method is proposed in this paper. In the proposed method, the characteristics of WC background noise are first analyzed and given. Then, the mean standard deviation threshold segmentations are respectively used for the denoising of time-angle and depth-angle images, an intersection operation is performed for the two segmented images to further weaken noise in the WC data, and the gas plumes in the WC data are detected from the intersection image by the morphological constraint. The proposed method was tested by conducting shallow-water and deepwater experiments. In these experiments, the detections were conducted automatically and higher correct detection rates than the traditional methods were achieved. The performance of the proposed method is analyzed and discussed. PMID:29186014

Bayesian methods for outliers detection in GNSS time series

NASA Astrophysics Data System (ADS)

Qianqian, Zhang; Qingming, Gui

2013-07-01

This article is concerned with the problem of detecting outliers in GNSS time series based on Bayesian statistical theory. Firstly, a new model is proposed to simultaneously detect different types of outliers based on the conception of introducing different types of classification variables corresponding to the different types of outliers; the problem of outlier detection is converted into the computation of the corresponding posterior probabilities, and the algorithm for computing the posterior probabilities based on standard Gibbs sampler is designed. Secondly, we analyze the reasons of masking and swamping about detecting patches of additive outliers intensively; an unmasking Bayesian method for detecting additive outlier patches is proposed based on an adaptive Gibbs sampler. Thirdly, the correctness of the theories and methods proposed above is illustrated by simulated data and then by analyzing real GNSS observations, such as cycle slips detection in carrier phase data. Examples illustrate that the Bayesian methods for outliers detection in GNSS time series proposed by this paper are not only capable of detecting isolated outliers but also capable of detecting additive outlier patches. Furthermore, it can be successfully used to process cycle slips in phase data, which solves the problem of small cycle slips.

Islanding detection technique using wavelet energy in grid-connected PV system

NASA Astrophysics Data System (ADS)

Kim, Il Song

2016-08-01

This paper proposes a new islanding detection method using wavelet energy in a grid-connected photovoltaic system. The method detects spectral changes in the higher-frequency components of the point of common coupling voltage and obtains wavelet coefficients by multilevel wavelet analysis. The autocorrelation of the wavelet coefficients can clearly identify islanding detection, even in the variations of the grid voltage harmonics during normal operating conditions. The advantage of the proposed method is that it can detect islanding condition the conventional under voltage/over voltage/under frequency/over frequency methods fail to detect. The theoretical method to obtain wavelet energies is evolved and verified by the experimental result.

Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

PubMed

Han, Jian; Zhang, Huaguang; Wang, Yingchun; Sun, Xun

2017-10-03

This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

Local region power spectrum-based unfocused ship detection method in synthetic aperture radar images

NASA Astrophysics Data System (ADS)

Wei, Xiangfei; Wang, Xiaoqing; Chong, Jinsong

2018-01-01

Ships on synthetic aperture radar (SAR) images will be severely defocused and their energy will disperse into numerous resolution cells under long SAR integration time. Therefore, the image intensity of ships is weak and sometimes even overwhelmed by sea clutter on SAR image. Consequently, it is hard to detect the ships from SAR intensity images. A ship detection method based on local region power spectrum of SAR complex image is proposed. Although the energies of the ships are dispersed on SAR intensity images, their spectral energies are rather concentrated or will cause the power spectra of local areas of SAR images to deviate from that of sea surface background. Therefore, the key idea of the proposed method is to detect ships via the power spectra distortion of local areas of SAR images. The local region power spectrum of a moving target on SAR image is analyzed and the way to obtain the detection threshold through the probability density function (pdf) of the power spectrum is illustrated. Numerical P- and L-band airborne SAR ocean data are utilized and the detection results are also illustrated. Results show that the proposed method can well detect the unfocused ships, with a detection rate of 93.6% and a false-alarm rate of 8.6%. Moreover, by comparing with some other algorithms, it indicates that the proposed method performs better under long SAR integration time. Finally, the applicability of the proposed method and the way of parameters selection are also discussed.

Multiratio fusion change detection with adaptive thresholding

NASA Astrophysics Data System (ADS)

Hytla, Patrick C.; Balster, Eric J.; Vasquez, Juan R.; Neuroth, Robert M.

2017-04-01

A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

Initial Results in Using a Self-Coherence Method for Detecting Sustained Oscillations

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

Zhou, Ning; Dagle, Jeffery E.

2015-01-01

This paper develops a self-coherence method for detecting sustained oscillations using phasor measurement unit (PMU) data. Sustained oscillations decrease system performance and introduce potential reliability issues. Timely detection of the oscillations at an early stage provides the opportunity for taking remedial reaction. Using high-speed time-synchronized PMU data, this paper details a self-coherence method for detecting sustained oscillation, even when the oscillation amplitude is lower than ambient noise. Simulation and field measurement data are used to evaluate the proposed method’s performance. It is shown that the proposed method can detect sustained oscillations and estimate oscillation frequencies with a low signal-to-noise ratio.more » Comparison with a power spectral density method also shows that the proposed self-coherence method performs better. Index Terms—coherence, power spectral density, phasor measurement unit (PMU), oscillations, power system dynamics« less

Smoke regions extraction based on two steps segmentation and motion detection in early fire

NASA Astrophysics Data System (ADS)

Jian, Wenlin; Wu, Kaizhi; Yu, Zirong; Chen, Lijuan

2018-03-01

Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. Then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. The Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. The proposed method was tested on 6 test videos with smoke. The experimental results show the effectiveness of our proposed method over visual observation.

A modified anomaly detection method for capsule endoscopy images using non-linear color conversion and Higher-order Local Auto-Correlation (HLAC).

PubMed

Hu, Erzhong; Nosato, Hirokazu; Sakanashi, Hidenori; Murakawa, Masahiro

2013-01-01

Capsule endoscopy is a patient-friendly endoscopy broadly utilized in gastrointestinal examination. However, the efficacy of diagnosis is restricted by the large quantity of images. This paper presents a modified anomaly detection method, by which both known and unknown anomalies in capsule endoscopy images of small intestine are expected to be detected. To achieve this goal, this paper introduces feature extraction using a non-linear color conversion and Higher-order Local Auto Correlation (HLAC) Features, and makes use of image partition and subspace method for anomaly detection. Experiments are implemented among several major anomalies with combinations of proposed techniques. As the result, the proposed method achieved 91.7% and 100% detection accuracy for swelling and bleeding respectively, so that the effectiveness of proposed method is demonstrated.

Remote logo detection using angle-distance histograms

NASA Astrophysics Data System (ADS)

Youn, Sungwook; Ok, Jiheon; Baek, Sangwook; Woo, Seongyoun; Lee, Chulhee

2016-05-01

Among all the various computer vision applications, automatic logo recognition has drawn great interest from industry as well as various academic institutions. In this paper, we propose an angle-distance map, which we used to develop a robust logo detection algorithm. The proposed angle-distance histogram is invariant against scale and rotation. The proposed method first used shape information and color characteristics to find the candidate regions and then applied the angle-distance histogram. Experiments show that the proposed method detected logos of various sizes and orientations.

Detecting ship targets in spaceborne infrared image based on modeling radiation anomalies

NASA Astrophysics Data System (ADS)

Wang, Haibo; Zou, Zhengxia; Shi, Zhenwei; Li, Bo

2017-09-01

Using infrared imaging sensors to detect ship target in the ocean environment has many advantages compared to other sensor modalities, such as better thermal sensitivity and all-weather detection capability. We propose a new ship detection method by modeling radiation anomalies for spaceborne infrared image. The proposed method can be decomposed into two stages, where in the first stage, a test infrared image is densely divided into a set of image patches and the radiation anomaly of each patch is estimated by a Gaussian Mixture Model (GMM), and thereby target candidates are obtained from anomaly image patches. In the second stage, target candidates are further checked by a more discriminative criterion to obtain the final detection result. The main innovation of the proposed method is inspired by the biological mechanism that human eyes are sensitive to the unusual and anomalous patches among complex background. The experimental result on short wavelength infrared band (1.560 - 2.300 μm) and long wavelength infrared band (10.30 - 12.50 μm) of Landsat-8 satellite shows the proposed method achieves a desired ship detection accuracy with higher recall than other classical ship detection methods.

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System.

PubMed

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-02-20

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequencydomain and achieves computational complexity reduction.

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade

PubMed Central

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-01-01

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade. PMID:26287200

Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis

NASA Astrophysics Data System (ADS)

Wang, Xiao; Gao, Feng; Dong, Junyu; Qi, Qiang

2018-04-01

Synthetic aperture radar (SAR) image is independent on atmospheric conditions, and it is the ideal image source for change detection. Existing methods directly analysis all the regions in the speckle noise contaminated difference image. The performance of these methods is easily affected by small noisy regions. In this paper, we proposed a novel change detection framework for saliency-guided change detection based on pattern and intensity distinctiveness analysis. The saliency analysis step can remove small noisy regions, and therefore makes the proposed method more robust to the speckle noise. In the proposed method, the log-ratio operator is first utilized to obtain a difference image (DI). Then, the saliency detection method based on pattern and intensity distinctiveness analysis is utilized to obtain the changed region candidates. Finally, principal component analysis and k-means clustering are employed to analysis pixels in the changed region candidates. Thus, the final change map can be obtained by classifying these pixels into changed or unchanged class. The experiment results on two real SAR images datasets have demonstrated the effectiveness of the proposed method.

Joint Dictionary Learning for Multispectral Change Detection.

PubMed

Lu, Xiaoqiang; Yuan, Yuan; Zheng, Xiangtao

2017-04-01

Change detection is one of the most important applications of remote sensing technology. It is a challenging task due to the obvious variations in the radiometric value of spectral signature and the limited capability of utilizing spectral information. In this paper, an improved sparse coding method for change detection is proposed. The intuition of the proposed method is that unchanged pixels in different images can be well reconstructed by the joint dictionary, which corresponds to knowledge of unchanged pixels, while changed pixels cannot. First, a query image pair is projected onto the joint dictionary to constitute the knowledge of unchanged pixels. Then reconstruction error is obtained to discriminate between the changed and unchanged pixels in the different images. To select the proper thresholds for determining changed regions, an automatic threshold selection strategy is presented by minimizing the reconstruction errors of the changed pixels. Adequate experiments on multispectral data have been tested, and the experimental results compared with the state-of-the-art methods prove the superiority of the proposed method. Contributions of the proposed method can be summarized as follows: 1) joint dictionary learning is proposed to explore the intrinsic information of different images for change detection. In this case, change detection can be transformed as a sparse representation problem. To the authors' knowledge, few publications utilize joint learning dictionary in change detection; 2) an automatic threshold selection strategy is presented, which minimizes the reconstruction errors of the changed pixels without the prior assumption of the spectral signature. As a result, the threshold value provided by the proposed method can adapt to different data due to the characteristic of joint dictionary learning; and 3) the proposed method makes no prior assumption of the modeling and the handling of the spectral signature, which can be adapted to different data.

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

PubMed

Liu, Wensong; Yang, Jie; Zhao, Jinqi; Shi, Hongtao; Yang, Le

2018-02-12

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by R j test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

Fabric defect detection based on faster R-CNN

NASA Astrophysics Data System (ADS)

Liu, Zhoufeng; Liu, Xianghui; Li, Chunlei; Li, Bicao; Wang, Baorui

2018-04-01

In order to effectively detect the defects for fabric image with complex texture, this paper proposed a novel detection algorithm based on an end-to-end convolutional neural network. First, the proposal regions are generated by RPN (regional proposal Network). Then, Fast Region-based Convolutional Network method (Fast R-CNN) is adopted to determine whether the proposal regions extracted by RPN is a defect or not. Finally, Soft-NMS (non-maximum suppression) and data augmentation strategies are utilized to improve the detection precision. Experimental results demonstrate that the proposed method can locate the fabric defect region with higher accuracy compared with the state-of- art, and has better adaptability to all kinds of the fabric image.

Robust Vehicle Detection under Various Environmental Conditions Using an Infrared Thermal Camera and Its Application to Road Traffic Flow Monitoring

PubMed Central

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-01-01

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as “our previous method”) using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as “our new method”). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal. PMID:23774988

Real-time anomaly detection for very short-term load forecasting

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

Luo, Jian; Hong, Tao; Yue, Meng

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Real-time anomaly detection for very short-term load forecasting

DOE PAGES

Luo, Jian; Hong, Tao; Yue, Meng

2018-01-06

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Object detection system based on multimodel saliency maps

NASA Astrophysics Data System (ADS)

Guo, Ya'nan; Luo, Chongfan; Ma, Yide

2017-03-01

Detection of visually salient image regions is extensively applied in computer vision and computer graphics, such as object detection, adaptive compression, and object recognition, but any single model always has its limitations to various images, so in our work, we establish a method based on multimodel saliency maps to detect the object, which intelligently absorbs the merits of various individual saliency detection models to achieve promising results. The method can be roughly divided into three steps: in the first step, we propose a decision-making system to evaluate saliency maps obtained by seven competitive methods and merely select the three most valuable saliency maps; in the second step, we introduce heterogeneous PCNN algorithm to obtain three prime foregrounds; and then a self-designed nonlinear fusion method is proposed to merge these saliency maps; at last, the adaptive improved and simplified PCNN model is used to detect the object. Our proposed method can constitute an object detection system for different occasions, which requires no training, is simple, and highly efficient. The proposed saliency fusion technique shows better performance over a broad range of images and enriches the applicability range by fusing different individual saliency models, this proposed system is worthy enough to be called a strong model. Moreover, the proposed adaptive improved SPCNN model is stemmed from the Eckhorn's neuron model, which is skilled in image segmentation because of its biological background, and in which all the parameters are adaptive to image information. We extensively appraise our algorithm on classical salient object detection database, and the experimental results demonstrate that the aggregation of saliency maps outperforms the best saliency model in all cases, yielding highest precision of 89.90%, better recall rates of 98.20%, greatest F-measure of 91.20%, and lowest mean absolute error value of 0.057, the value of proposed saliency evaluation EHA reaches to 215.287. We deem our method can be wielded to diverse applications in the future.

Detection of Suspicious Persons using Internet Camera

NASA Astrophysics Data System (ADS)

Terada, Kenji; Kamogashira, Daisuke

Recently, many brutal crimes have shocked us. Therefore, the importance of security and self-defense have increased more and more. It is necessary to develop an automatic method of detecting suspicious persons. In this paper, we propose a method of detecting suspicious persons using the internet camera. An image sequence is obtained by the internet camera. By using these images, the recognition of suspicious persons is carried out. Our method classifies the condition of the target person into 3 postures: walking, staying and sitting. The system employs the subspace method which uses three features: the value of movement, the number of looking around restlessly, and the rate of stopping and going. Some experimental results using a simple experimental system are also reported, which indicate effectiveness of the proposed method. In most scenes, the suspicious persons are able to be detected by the proposed method.

A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

NASA Astrophysics Data System (ADS)

Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

2016-03-01

The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

Detecting subsurface fluid leaks in real-time using injection and production rates

NASA Astrophysics Data System (ADS)

Singh, Harpreet; Huerta, Nicolas J.

2017-12-01

CO2 injection into geologic formations for either enhanced oil recovery or carbon storage introduces a risk for undesired fluid leakage into overlying groundwater or to the surface. Despite decades of subsurface CO2 production and injection, the technologies and methods for detecting CO2 leaks are still costly and prone to large uncertainties. This is especially true for pressure-based monitoring methods, which require the use of simplified geological and reservoir flow models to simulate the pressure behavior as well as background noise affecting pressure measurements. In this study, we propose a method to detect the time and volume of fluid leakage based on real-time measurements of well injection and production rates. The approach utilizes analogies between fluid flow and capacitance-resistance modeling. Unlike other leak detection methods (e.g. pressure-based), the proposed method does not require geological and reservoir flow models to simulate the behavior that often carry significant sources of uncertainty; therefore, with our approach the leak can be detected with greater certainty. The method can be applied to detect when a leak begins by tracking a departure in fluid production rate from the expected pattern. The method has been tuned to detect the effect of boundary conditions and fluid compressibility on leakage. To highlight the utility of this approach we use our method to detect leaks for two scenarios. The first scenario simulates a fluid leak from the storage formation into an above-zone monitoring interval. The second scenario simulates intra-reservoir migration between two compartments. We illustrate this method to detect fluid leakage in three different reservoirs with varying levels of geological and structural complexity. The proposed leakage detection method has three novelties: i) requires only readily-available data (injection and production rates), ii) accounts for fluid compressibility and boundary effects, and iii) in addition to detecting the time when a leak is activated and the volume of that leakage, this method provides an insight about the leak location, and reservoir connectivity. We are proposing this as a complementary method that can be used with other, more expensive, methods early on in the injection process. This will allow an operator to conduct more expensive surveys less often because the proposed method can show if there are no leaks on a monthly basis that is cheap and fast.

Robust vehicle detection under various environments to realize road traffic flow surveillance using an infrared thermal camera.

PubMed

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2015-01-01

To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized.

Robust Vehicle Detection under Various Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera

PubMed Central

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2015-01-01

To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized. PMID:25763384

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System

PubMed Central

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-01-01

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequency-domain and achieves computational complexity reduction. PMID:28230763

Data based abnormality detection

NASA Astrophysics Data System (ADS)

Purwar, Yashasvi

Data based abnormality detection is a growing research field focussed on extracting information from feature rich data. They are considered to be non-intrusive and non-destructive in nature which gives them a clear advantage over conventional methods. In this study, we explore different streams of data based anomalies detection. We propose extension and revisions to existing valve stiction detection algorithm supported with industrial case study. We also explored the area of image analysis and proposed a complete solution for Malaria diagnosis. The proposed method is tested over images provided by pathology laboratory at Alberta Health Service. We also address the robustness and practicality of the solution proposed.

Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform.

PubMed

Chen, Shuo-Tsung; Wang, Tzung-Dau; Lee, Wen-Jeng; Huang, Tsai-Wei; Hung, Pei-Kai; Wei, Cheng-Yu; Chen, Chung-Ming; Kung, Woon-Man

2015-01-01

Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine.

PubMed

Imamverdiyev, Yadigar; Abdullayeva, Fargana

2018-06-01

In this article, the application of the deep learning method based on Gaussian-Bernoulli type restricted Boltzmann machine (RBM) to the detection of denial of service (DoS) attacks is considered. To increase the DoS attack detection accuracy, seven additional layers are added between the visible and the hidden layers of the RBM. Accurate results in DoS attack detection are obtained by optimization of the hyperparameters of the proposed deep RBM model. The form of the RBM that allows application of the continuous data is used. In this type of RBM, the probability distribution of the visible layer is replaced by a Gaussian distribution. Comparative analysis of the accuracy of the proposed method with Bernoulli-Bernoulli RBM, Gaussian-Bernoulli RBM, deep belief network type deep learning methods on DoS attack detection is provided. Detection accuracy of the methods is verified on the NSL-KDD data set. Higher accuracy from the proposed multilayer deep Gaussian-Bernoulli type RBM is obtained.

Infrared target tracking via weighted correlation filter

NASA Astrophysics Data System (ADS)

He, Yu-Jie; Li, Min; Zhang, JinLi; Yao, Jun-Ping

2015-11-01

Design of an effective target tracker is an important and challenging task for many applications due to multiple factors which can cause disturbance in infrared video sequences. In this paper, an infrared target tracking method under tracking by detection framework based on a weighted correlation filter is presented. This method consists of two parts: detection and filtering. For the detection stage, we propose a sequential detection method for the infrared target based on low-rank representation. For the filtering stage, a new multi-feature weighted function which fuses different target features is proposed, which takes the importance of the different regions into consideration. The weighted function is then incorporated into a correlation filter to compute a confidence map more accurately, in order to indicate the best target location based on the detection results obtained from the first stage. Extensive experimental results on different video sequences demonstrate that the proposed method performs favorably for detection and tracking compared with baseline methods in terms of efficiency and accuracy.

Recognition and defect detection of dot-matrix text via variation-model based learning

NASA Astrophysics Data System (ADS)

Ohyama, Wataru; Suzuki, Koushi; Wakabayashi, Tetsushi

2017-03-01

An algorithm for recognition and defect detection of dot-matrix text printed on products is proposed. Extraction and recognition of dot-matrix text contains several difficulties, which are not involved in standard camera-based OCR, that the appearance of dot-matrix characters is corrupted and broken by illumination, complex texture in the background and other standard characters printed on product packages. We propose a dot-matrix text extraction and recognition method which does not require any user interaction. The method employs detected location of corner points and classification score. The result of evaluation experiment using 250 images shows that recall and precision of extraction are 78.60% and 76.03%, respectively. Recognition accuracy of correctly extracted characters is 94.43%. Detecting printing defect of dot-matrix text is also important in the production scene to avoid illegal productions. We also propose a detection method for printing defect of dot-matrix characters. The method constructs a feature vector of which elements are classification scores of each character class and employs support vector machine to classify four types of printing defect. The detection accuracy of the proposed method is 96.68 %.

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

PubMed Central

Wang, Yuliang; Zhang, Zaicheng; Wang, Huimin; Bi, Shusheng

2015-01-01

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells. PMID:26066315

High-performance Chinese multiclass traffic sign detection via coarse-to-fine cascade and parallel support vector machine detectors

NASA Astrophysics Data System (ADS)

Chang, Faliang; Liu, Chunsheng

2017-09-01

The high variability of sign colors and shapes in uncontrolled environments has made the detection of traffic signs a challenging problem in computer vision. We propose a traffic sign detection (TSD) method based on coarse-to-fine cascade and parallel support vector machine (SVM) detectors to detect Chinese warning and danger traffic signs. First, a region of interest (ROI) extraction method is proposed to extract ROIs using color contrast features in local regions. The ROI extraction can reduce scanning regions and save detection time. For multiclass TSD, we propose a structure that combines a coarse-to-fine cascaded tree with a parallel structure of histogram of oriented gradients (HOG) + SVM detectors. The cascaded tree is designed to detect different types of traffic signs in a coarse-to-fine process. The parallel HOG + SVM detectors are designed to do fine detection of different types of traffic signs. The experiments demonstrate the proposed TSD method can rapidly detect multiclass traffic signs with different colors and shapes in high accuracy.

Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

NASA Astrophysics Data System (ADS)

Li, Miao; Lin, Zaiping; Long, Yunli; An, Wei; Zhou, Yiyu

2016-05-01

The high variability of target size makes small target detection in Infrared Search and Track (IRST) a challenging task. A joint detection and tracking method based on block-wise sparse decomposition is proposed to address this problem. For detection, the infrared image is divided into overlapped blocks, and each block is weighted on the local image complexity and target existence probabilities. Target-background decomposition is solved by block-wise inexact augmented Lagrange multipliers. For tracking, label multi-Bernoulli (LMB) tracker tracks multiple targets taking the result of single-frame detection as input, and provides corresponding target existence probabilities for detection. Unlike fixed-size methods, the proposed method can accommodate size-varying targets, due to no special assumption for the size and shape of small targets. Because of exact decomposition, classical target measurements are extended and additional direction information is provided to improve tracking performance. The experimental results show that the proposed method can effectively suppress background clutters, detect and track size-varying targets in infrared images.

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

PubMed

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

Missing RRI interpolation for HRV analysis using locally-weighted partial least squares regression.

PubMed

Kamata, Keisuke; Fujiwara, Koichi; Yamakawa, Toshiki; Kano, Manabu

2016-08-01

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV). Since HRV reflects autonomic nervous function, HRV-based health monitoring services, such as stress estimation, drowsy driving detection, and epileptic seizure prediction, have been proposed. In these HRV-based health monitoring services, precise R wave detection from ECG is required; however, R waves cannot always be detected due to ECG artifacts. Missing RRI data should be interpolated appropriately for HRV analysis. The present work proposes a missing RRI interpolation method by utilizing using just-in-time (JIT) modeling. The proposed method adopts locally weighted partial least squares (LW-PLS) for RRI interpolation, which is a well-known JIT modeling method used in the filed of process control. The usefulness of the proposed method was demonstrated through a case study of real RRI data collected from healthy persons. The proposed JIT-based interpolation method could improve the interpolation accuracy in comparison with a static interpolation method.

Automatic comic page image understanding based on edge segment analysis

NASA Astrophysics Data System (ADS)

Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

2013-12-01

Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

NASA Astrophysics Data System (ADS)

Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

2017-06-01

In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

Recurrent neural network based virtual detection line

NASA Astrophysics Data System (ADS)

Kadikis, Roberts

2018-04-01

The paper proposes an efficient method for detection of moving objects in the video. The objects are detected when they cross a virtual detection line. Only the pixels of the detection line are processed, which makes the method computationally efficient. A Recurrent Neural Network processes these pixels. The machine learning approach allows one to train a model that works in different and changing outdoor conditions. Also, the same network can be trained for various detection tasks, which is demonstrated by the tests on vehicle and people counting. In addition, the paper proposes a method for semi-automatic acquisition of labeled training data. The labeling method is used to create training and testing datasets, which in turn are used to train and evaluate the accuracy and efficiency of the detection method. The method shows similar accuracy as the alternative efficient methods but provides greater adaptability and usability for different tasks.

Detecting Signage and Doors for Blind Navigation and Wayfinding

PubMed Central

Wang, Shuihua; Yang, Xiaodong; Tian, Yingli

2013-01-01

Signage plays a very important role to find destinations in applications of navigation and wayfinding. In this paper, we propose a novel framework to detect doors and signage to help blind people accessing unfamiliar indoor environments. In order to eliminate the interference information and improve the accuracy of signage detection, we first extract the attended areas by using a saliency map. Then the signage is detected in the attended areas by using a bipartite graph matching. The proposed method can handle multiple signage detection. Furthermore, in order to provide more information for blind users to access the area associated with the detected signage, we develop a robust method to detect doors based on a geometric door frame model which is independent to door appearances. Experimental results on our collected datasets of indoor signage and doors demonstrate the effectiveness and efficiency of our proposed method. PMID:23914345

Detecting Signage and Doors for Blind Navigation and Wayfinding.

PubMed

Wang, Shuihua; Yang, Xiaodong; Tian, Yingli

2013-07-01

Signage plays a very important role to find destinations in applications of navigation and wayfinding. In this paper, we propose a novel framework to detect doors and signage to help blind people accessing unfamiliar indoor environments. In order to eliminate the interference information and improve the accuracy of signage detection, we first extract the attended areas by using a saliency map. Then the signage is detected in the attended areas by using a bipartite graph matching. The proposed method can handle multiple signage detection. Furthermore, in order to provide more information for blind users to access the area associated with the detected signage, we develop a robust method to detect doors based on a geometric door frame model which is independent to door appearances. Experimental results on our collected datasets of indoor signage and doors demonstrate the effectiveness and efficiency of our proposed method.

Light Weight MP3 Watermarking Method for Mobile Terminals

NASA Astrophysics Data System (ADS)

Takagi, Koichi; Sakazawa, Shigeyuki; Takishima, Yasuhiro

This paper proposes a novel MP3 watermarking method which is applicable to a mobile terminal with limited computational resources. Considering that in most cases the embedded information is copyright information or metadata, which should be extracted before playing back audio contents, the watermark detection process should be executed at high speed. However, when conventional methods are used with a mobile terminal, it takes a considerable amount of time to detect a digital watermark. This paper focuses on scalefactor manipulation to enable high speed watermark embedding/detection for MP3 audio and also proposes the manipulation method which minimizes audio quality degradation adaptively. Evaluation tests showed that the proposed method is capable of embedding 3 bits/frame information without degrading audio quality and detecting it at very high speed. Finally, this paper describes application examples for authentication with a digital signature.

Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

2017-11-01

A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

A novel infrared small moving target detection method based on tracking interest points under complicated background

NASA Astrophysics Data System (ADS)

Dong, Xiabin; Huang, Xinsheng; Zheng, Yongbin; Bai, Shengjian; Xu, Wanying

2014-07-01

Infrared moving target detection is an important part of infrared technology. We introduce a novel infrared small moving target detection method based on tracking interest points under complicated background. Firstly, Difference of Gaussians (DOG) filters are used to detect a group of interest points (including the moving targets). Secondly, a sort of small targets tracking method inspired by Human Visual System (HVS) is used to track these interest points for several frames, and then the correlations between interest points in the first frame and the last frame are obtained. Last, a new clustering method named as R-means is proposed to divide these interest points into two groups according to the correlations, one is target points and another is background points. In experimental results, the target-to-clutter ratio (TCR) and the receiver operating characteristics (ROC) curves are computed experimentally to compare the performances of the proposed method and other five sophisticated methods. From the results, the proposed method shows a better discrimination of targets and clutters and has a lower false alarm rate than the existing moving target detection methods.

Slip Ratio Estimation and Regenerative Brake Control for Decelerating Electric Vehicles without Detection of Vehicle Velocity and Acceleration

NASA Astrophysics Data System (ADS)

Suzuki, Toru; Fujimoto, Hiroshi

In slip ratio control systems, it is necessary to detect the vehicle velocity in order to obtain the slip ratio. However, it is very difficult to measure this velocity directly. We have proposed slip ratio estimation and control methods that do not require the vehicle velocity with acceleration. In this paper, the slip ratio estimation and control methods are proposed without detecting the vehicle velocity and acceleration when it is decelerating. We carried out simulations and experiments by using an electric vehicle to verify the effectiveness of the proposed method.

A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors

PubMed Central

Xu, Zhengyi; Wei, Jianming; Zhang, Bo; Yang, Weijun

2015-01-01

This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect. PMID:25831086

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram

PubMed Central

Chu, Catherine. J.; Chan, Arthur; Song, Dan; Staley, Kevin J.; Stufflebeam, Steven M.; Kramer, Mark A.

2017-01-01

Summary Background High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. New Method The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Results We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. Comparison with Existing Method The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Conclusions Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. PMID:27988323

Fault detection of Tennessee Eastman process based on topological features and SVM

NASA Astrophysics Data System (ADS)

Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

2018-03-01

Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

THE SCREENING AND RANKING ALGORITHM FOR CHANGE-POINTS DETECTION IN MULTIPLE SAMPLES

PubMed Central

Song, Chi; Min, Xiaoyi; Zhang, Heping

2016-01-01

The chromosome copy number variation (CNV) is the deviation of genomic regions from their normal copy number states, which may associate with many human diseases. Current genetic studies usually collect hundreds to thousands of samples to study the association between CNV and diseases. CNVs can be called by detecting the change-points in mean for sequences of array-based intensity measurements. Although multiple samples are of interest, the majority of the available CNV calling methods are single sample based. Only a few multiple sample methods have been proposed using scan statistics that are computationally intensive and designed toward either common or rare change-points detection. In this paper, we propose a novel multiple sample method by adaptively combining the scan statistic of the screening and ranking algorithm (SaRa), which is computationally efficient and is able to detect both common and rare change-points. We prove that asymptotically this method can find the true change-points with almost certainty and show in theory that multiple sample methods are superior to single sample methods when shared change-points are of interest. Additionally, we report extensive simulation studies to examine the performance of our proposed method. Finally, using our proposed method as well as two competing approaches, we attempt to detect CNVs in the data from the Primary Open-Angle Glaucoma Genes and Environment study, and conclude that our method is faster and requires less information while our ability to detect the CNVs is comparable or better. PMID:28090239

Thin Cloud Detection Method by Linear Combination Model of Cloud Image

NASA Astrophysics Data System (ADS)

Liu, L.; Li, J.; Wang, Y.; Xiao, Y.; Zhang, W.; Zhang, S.

2018-04-01

The existing cloud detection methods in photogrammetry often extract the image features from remote sensing images directly, and then use them to classify images into cloud or other things. But when the cloud is thin and small, these methods will be inaccurate. In this paper, a linear combination model of cloud images is proposed, by using this model, the underlying surface information of remote sensing images can be removed. So the cloud detection result can become more accurate. Firstly, the automatic cloud detection program in this paper uses the linear combination model to split the cloud information and surface information in the transparent cloud images, then uses different image features to recognize the cloud parts. In consideration of the computational efficiency, AdaBoost Classifier was introduced to combine the different features to establish a cloud classifier. AdaBoost Classifier can select the most effective features from many normal features, so the calculation time is largely reduced. Finally, we selected a cloud detection method based on tree structure and a multiple feature detection method using SVM classifier to compare with the proposed method, the experimental data shows that the proposed cloud detection program in this paper has high accuracy and fast calculation speed.

Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d GIS Model

NASA Astrophysics Data System (ADS)

Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Zhina

2016-06-01

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

Dangerous gas detection based on infrared video

NASA Astrophysics Data System (ADS)

Ding, Kang; Hong, Hanyu; Huang, Likun

2018-03-01

As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

PubMed

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-06-20

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

In this paper, an improved joint time-frequency (TF) analysis method based on a reassigned smoothed pseudo Wigner–Ville distribution (RSPWVD) has been proposed in interference detection for Global Navigation Satellite System (GNSS) receivers. In the RSPWVD, the two-dimensional low-pass filtering smoothing function is introduced to eliminate the cross-terms present in the quadratic TF distribution, and at the same time, the reassignment method is adopted to improve the TF concentration properties of the auto-terms of the signal components. This proposed interference detection method is evaluated by experiments on GPS L1 signals in the disturbing scenarios compared to the state-of-the-art interference detection approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-terms problem and also preserves good TF localization properties, which has been proven to be effective and valid to enhance the interference detection performance of the GNSS receivers, particularly in the jamming environments. PMID:25905704

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

PubMed Central

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-01-01

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. PMID:28773035

A Wavelet-based Fast Discrimination of Transformer Magnetizing Inrush Current

NASA Astrophysics Data System (ADS)

Kitayama, Masashi

Recently customers who need electricity of higher quality have been installing co-generation facilities. They can avoid voltage sags and other distribution system related disturbances by supplying electricity to important load from their generators. For another example, FRIENDS, highly reliable distribution system using semiconductor switches or storage devices based on power electronics technology, is proposed. These examples illustrates that the request for high reliability in distribution system is increasing. In order to realize these systems, fast relaying algorithms are indispensable. The author proposes a new method of detecting magnetizing inrush current using discrete wavelet transform (DWT). DWT provides the function of detecting discontinuity of current waveform. Inrush current occurs when transformer core becomes saturated. The proposed method detects spikes of DWT components derived from the discontinuity of the current waveform at both the beginning and the end of inrush current. Wavelet thresholding, one of the wavelet-based statistical modeling, was applied to detect the DWT component spikes. The proposed method is verified using experimental data using single-phase transformer and the proposed method is proved to be effective.

Spatial cluster detection for repeatedly measured outcomes while accounting for residential history.

PubMed

Cook, Andrea J; Gold, Diane R; Li, Yi

2009-10-01

Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations.

Machine Learning Methods for Attack Detection in the Smart Grid.

PubMed

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

Stepwise and stagewise approaches for spatial cluster detection

PubMed Central

Xu, Jiale

2016-01-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either hypothesis testing framework or Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic area. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power of detections. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. PMID:27246273

A model-guided symbolic execution approach for network protocol implementations and vulnerability detection.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM) model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations.

Automated detection of red lesions from digital colour fundus photographs.

PubMed

Jaafar, Hussain F; Nandi, Asoke K; Al-Nuaimy, Waleed

2011-01-01

Earliest signs of diabetic retinopathy, the major cause of vision loss, are damage to the blood vessels and the formation of lesions in the retina. Early detection of diabetic retinopathy is essential for the prevention of blindness. In this paper we present a computer-aided system to automatically identify red lesions from retinal fundus photographs. After pre-processing, a morphological technique was used to segment red lesion candidates from the background and other retinal structures. Then a rule-based classifier was used to discriminate actual red lesions from artifacts. A novel method for blood vessel detection is also proposed to refine the detection of red lesions. For a standarised test set of 219 images, the proposed method can detect red lesions with a sensitivity of 89.7% and a specificity of 98.6% (at lesion level). The performance of the proposed method shows considerable promise for detection of red lesions as well as other types of lesions.

Space debris detection in optical image sequences.

PubMed

Xi, Jiangbo; Wen, Desheng; Ersoy, Okan K; Yi, Hongwei; Yao, Dalei; Song, Zongxi; Xi, Shaobo

2016-10-01

We present a high-accuracy, low false-alarm rate, and low computational-cost methodology for removing stars and noise and detecting space debris with low signal-to-noise ratio (SNR) in optical image sequences. First, time-index filtering and bright star intensity enhancement are implemented to remove stars and noise effectively. Then, a multistage quasi-hypothesis-testing method is proposed to detect the pieces of space debris with continuous and discontinuous trajectories. For this purpose, a time-index image is defined and generated. Experimental results show that the proposed method can detect space debris effectively without any false alarms. When the SNR is higher than or equal to 1.5, the detection probability can reach 100%, and when the SNR is as low as 1.3, 1.2, and 1, it can still achieve 99%, 97%, and 85% detection probabilities, respectively. Additionally, two large sets of image sequences are tested to show that the proposed method performs stably and effectively.

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array.

PubMed

Li, Miao; Li, Jun; Zhou, Yiyu

2015-12-08

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts-MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing.

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array

PubMed Central

Li, Miao; Li, Jun; Zhou, Yiyu

2015-01-01

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts—MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing. PMID:26670234

High-Accuracy Ultrasound Contrast Agent Detection Method for Diagnostic Ultrasound Imaging Systems.

PubMed

Ito, Koichi; Noro, Kazumasa; Yanagisawa, Yukari; Sakamoto, Maya; Mori, Shiro; Shiga, Kiyoto; Kodama, Tetsuya; Aoki, Takafumi

2015-12-01

An accurate method for detecting contrast agents using diagnostic ultrasound imaging systems is proposed. Contrast agents, such as microbubbles, passing through a blood vessel during ultrasound imaging are detected as blinking signals in the temporal axis, because their intensity value is constantly in motion. Ultrasound contrast agents are detected by evaluating the intensity variation of a pixel in the temporal axis. Conventional methods are based on simple subtraction of ultrasound images to detect ultrasound contrast agents. Even if the subject moves only slightly, a conventional detection method will introduce significant error. In contrast, the proposed technique employs spatiotemporal analysis of the pixel intensity variation over several frames. Experiments visualizing blood vessels in the mouse tail illustrated that the proposed method performs efficiently compared with conventional approaches. We also report that the new technique is useful for observing temporal changes in microvessel density in subiliac lymph nodes containing tumors. The results are compared with those of contrast-enhanced computed tomography. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

NASA Astrophysics Data System (ADS)

Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

Detection of Copy-Rotate-Move Forgery Using Zernike Moments

NASA Astrophysics Data System (ADS)

Ryu, Seung-Jin; Lee, Min-Jeong; Lee, Heung-Kyu

As forgeries have become popular, the importance of forgery detection is much increased. Copy-move forgery, one of the most commonly used methods, copies a part of the image and pastes it into another part of the the image. In this paper, we propose a detection method of copy-move forgery that localizes duplicated regions using Zernike moments. Since the magnitude of Zernike moments is algebraically invariant against rotation, the proposed method can detect a forged region even though it is rotated. Our scheme is also resilient to the intentional distortions such as additive white Gaussian noise, JPEG compression, and blurring. Experimental results demonstrate that the proposed scheme is appropriate to identify the forged region by copy-rotate-move forgery.

An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images.

PubMed

Sidibé, Désiré; Sankar, Shrinivasan; Lemaître, Guillaume; Rastgoo, Mojdeh; Massich, Joan; Cheung, Carol Y; Tan, Gavin S W; Milea, Dan; Lamoureux, Ecosse; Wong, Tien Y; Mériaudeau, Fabrice

2017-02-01

This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Efficiently detecting outlying behavior in video-game players.

PubMed

Kim, Young Bin; Kang, Shin Jin; Lee, Sang Hyeok; Jung, Jang Young; Kam, Hyeong Ryeol; Lee, Jung; Kim, Young Sun; Lee, Joonsoo; Kim, Chang Hun

2015-01-01

In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players' characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse) was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.

Efficiently detecting outlying behavior in video-game players

PubMed Central

Kim, Young Bin; Kang, Shin Jin; Lee, Sang Hyeok; Jung, Jang Young; Kam, Hyeong Ryeol; Lee, Jung; Kim, Young Sun; Lee, Joonsoo

2015-01-01

In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players’ characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse) was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments. PMID:26713250

Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

NASA Astrophysics Data System (ADS)

Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

2017-12-01

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

An Implementation of Privacy Protection for a Surveillance Camera Using ROI Coding of JPEG2000 with Face Detection

NASA Astrophysics Data System (ADS)

Muneyasu, Mitsuji; Odani, Shuhei; Kitaura, Yoshihiro; Namba, Hitoshi

On the use of a surveillance camera, there is a case where privacy protection should be considered. This paper proposes a new privacy protection method by automatically degrading the face region in surveillance images. The proposed method consists of ROI coding of JPEG2000 and a face detection method based on template matching. The experimental result shows that the face region can be detected and hidden correctly.

Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms.

PubMed

Hurnanen, Tero; Lehtonen, Eero; Tadi, Mojtaba Jafari; Kuusela, Tom; Kiviniemi, Tuomas; Saraste, Antti; Vasankari, Tuija; Airaksinen, Juhani; Koivisto, Tero; Pankaala, Mikko

2017-09-01

In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of [Formula: see text] and an average true negative rate of [Formula: see text] for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.

Pelvic artery calcification detection on CT scans using convolutional neural networks

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Lu, Le; Yao, Jianhua; Bagheri, Mohammadhadi; Summers, Ronald M.

2017-03-01

Artery calcification is observed commonly in elderly patients, especially in patients with chronic kidney disease, and may affect coronary, carotid and peripheral arteries. Vascular calcification has been associated with many clinical outcomes. Manual identification of calcification in CT scans requires substantial expert interaction, which makes it time-consuming and infeasible for large-scale studies. Many works have been proposed for coronary artery calcification detection in cardiac CT scans. In these works, coronary artery extraction is commonly required for calcification detection. However, there are few works about abdominal or pelvic artery calcification detection. In this work, we present a method for automatic pelvic artery calcification detection on CT scan. This method uses the recent advanced faster region-based convolutional neural network (R-CNN) to directly identify artery calcification without a need for artery extraction since pelvic artery extraction itself is challenging. Our method first generates category-independent region proposals for each slice of the input CT scan using region proposal networks (RPN). Then, each region proposal is jointly classified and refined by softmax classifier and bounding box regressor. We applied the detection method to 500 images from 20 CT scans of patients for evaluation. The detection system achieved a 77.4% average precision and a 85% sensitivity at 1 false positive per image.

Edge-directed inference for microaneurysms detection in digital fundus images

NASA Astrophysics Data System (ADS)

Huang, Ke; Yan, Michelle; Aviyente, Selin

2007-03-01

Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of algorithms have been proposed for MAs detection in mass screening. Different methods have been proposed for MAs detection. The core technology for most of existing methods is based on a directional mathematical morphological operation called "Top-Hat" filter that requires multiple filtering operations at each pixel. Background structure, uneven illumination and noise often cause confusion between MAs and some non-MA structures and limits the applicability of the filter. In this paper, a novel detection framework based on edge directed inference is proposed for MAs detection. The candidate MA regions are first delineated from the edge map of a fundus image. Features measuring shape, brightness and contrast are extracted for each candidate MA region to better exclude false detection from true MAs. Algorithmic analysis and empirical evaluation reveal that the proposed edge directed inference outperforms the "Top-Hat" based algorithm in both detection accuracy and computational speed.

Research on infrared ship detection method in sea-sky background

NASA Astrophysics Data System (ADS)

Tang, Da; Sun, Gang; Wang, Ding-he; Niu, Zhao-dong; Chen, Zeng-ping

2013-09-01

An approach to infrared ship detection based on sea-sky-line(SSL) detection, ROI extraction and feature recognition is proposed in this paper. Firstly, considering that far ships are expected to be adjacent to the SSL, SSL is detected to find potential target areas. Radon transform is performed on gradient image to choose candidate SSLs, and detection result is given by fuzzy synthetic evaluation values. Secondly, in view of recognizable condition that there should be enough differences between target and background in infrared image, two gradient masks have been created and improved as practical guidelines in eliminating false alarm. Thirdly, extract ROI near the SSL by using multi-grade segmentation and fusion method after image sharpening, and unsuitable candidates are screened out according to the gradient masks and ROI shape. Finally, we segment the rest of ROIs by two-stage modified OTSU, and calculate target confidence as a standard measuring the facticity of target. Compared with other ship detection methods, proposed method is suitable for bipolar targets, which offers a good practicability and accuracy, and achieves a satisfying detection speed. Detection experiments with 200 thousand frames show that the proposed method is widely applicable, powerful in resistance to interferences and noises with a detection rate of above 95%, which satisfies the engineering needs commendably.

Detecting double compressed MPEG videos with the same quantization matrix and synchronized group of pictures structure

NASA Astrophysics Data System (ADS)

Aghamaleki, Javad Abbasi; Behrad, Alireza

2018-01-01

Double compression detection is a crucial stage in digital image and video forensics. However, the detection of double compressed videos is challenging when the video forger uses the same quantization matrix and synchronized group of pictures (GOP) structure during the recompression history to conceal tampering effects. A passive approach is proposed for detecting double compressed MPEG videos with the same quantization matrix and synchronized GOP structure. To devise the proposed algorithm, the effects of recompression on P frames are mathematically studied. Then, based on the obtained guidelines, a feature vector is proposed to detect double compressed frames on the GOP level. Subsequently, sparse representations of the feature vectors are used for dimensionality reduction and enrich the traces of recompression. Finally, a support vector machine classifier is employed to detect and localize double compression in temporal domain. The experimental results show that the proposed algorithm achieves the accuracy of more than 95%. In addition, the comparisons of the results of the proposed method with those of other methods reveal the efficiency of the proposed algorithm.

A deep learning approach for fetal QRS complex detection.

PubMed

Zhong, Wei; Liao, Lijuan; Guo, Xuemei; Wang, Guoli

2018-04-20

Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide more additional clinical information for detecting and diagnosing fetal diseases. We propose and demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals by using a convolutional neural network (CNN) model. The main objective is to investigate whether reliable fetal QRS complex detection performance can still be obtained from features of single-channel NI-FECG signals, without canceling maternal ECG (MECG) signals. A deep learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a of the PhysioNet/computing in Cardiology Challenge database. The sample entropy method is used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis. Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall, F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex detection. The proposed deep learning method can achieve relatively high precision (75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known pattern classification methods, namely KNN, naive Bayes and SVM. the proposed deep learning method can attain reliable fetal QRS complex detection performance from the raw NI-FECG signals without canceling MECG signals. In addition, the influence of different activation functions and signal quality assessment on classification performance are evaluated, and results show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification performance is obtained with the signal quality assessment step in this study.

Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy

PubMed Central

Huang, Yulin; Pei, Jifang; Zhang, Qian; Gu, Qin; Yang, Jianyu

2018-01-01

Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, due to the varying ocean waves and the strong echo of the sea surface, it is very difficult to detect ships from heterogeneous and strong clutter backgrounds. In this paper, an innovative ship detection method is proposed to effectively distinguish the vessels from complex backgrounds from a SAR image. First, the input SAR image is pre-screened by the maximally-stable extremal region (MSER) method, which can obtain the ship candidate regions with low computational complexity. Then, the proposed local contrast variance weighted information entropy (LCVWIE) is adopted to evaluate the complexity of those candidate regions and the dissimilarity between the candidate regions with their neighborhoods. Finally, the LCVWIE values of the candidate regions are compared with an adaptive threshold to obtain the final detection result. Experimental results based on measured ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods. PMID:29652863

Probabilistic double guarantee kidnapping detection in SLAM.

PubMed

Tian, Yang; Ma, Shugen

2016-01-01

For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features' positions and the robot's posture. Simulation results demonstrate the validity and accuracy of the proposed method.

Traffic Sign Detection Based on Biologically Visual Mechanism

NASA Astrophysics Data System (ADS)

Hu, X.; Zhu, X.; Li, D.

2012-07-01

TSR (Traffic sign recognition) is an important problem in ITS (intelligent traffic system), which is being paid more and more attention for realizing drivers assisting system and unmanned vehicle etc. TSR consists of two steps: detection and recognition, and this paper describe a new traffic sign detection method. The design principle of the traffic sign is comply with the visual attention mechanism of human, so we propose a method using visual attention mechanism to detect traffic sign ,which is reasonable. In our method, the whole scene will firstly be analyzed by visual attention model to acquire the area where traffic signs might be placed. And then, these candidate areas will be analyzed according to the shape characteristics of the traffic sign to detect traffic signs. In traffic sign detection experiments, the result shows the proposed method is effectively and robust than other existing saliency detection method.

A New Intrusion Detection Method Based on Antibody Concentration

NASA Astrophysics Data System (ADS)

Zeng, Jie; Li, Tao; Li, Guiyang; Li, Haibo

Antibody is one kind of protein that fights against the harmful antigen in human immune system. In modern medical examination, the health status of a human body can be diagnosed by detecting the intrusion intensity of a specific antigen and the concentration indicator of corresponding antibody from human body’s serum. In this paper, inspired by the principle of antigen-antibody reactions, we present a New Intrusion Detection Method Based on Antibody Concentration (NIDMBAC) to reduce false alarm rate without affecting detection rate. In our proposed method, the basic definitions of self, nonself, antigen and detector in the intrusion detection domain are given. Then, according to the antigen intrusion intensity, the change of antibody number is recorded from the process of clone proliferation for detectors based on the antigen classified recognition. Finally, building upon the above works, a probabilistic calculation method for the intrusion alarm production, which is based on the correlation between the antigen intrusion intensity and the antibody concen-tration, is proposed. Our theoretical analysis and experimental results show that our proposed method has a better performance than traditional methods.

Design of an Evolutionary Approach for Intrusion Detection

PubMed Central

2013-01-01

A novel evolutionary approach is proposed for effective intrusion detection based on benchmark datasets. The proposed approach can generate a pool of noninferior individual solutions and ensemble solutions thereof. The generated ensembles can be used to detect the intrusions accurately. For intrusion detection problem, the proposed approach could consider conflicting objectives simultaneously like detection rate of each attack class, error rate, accuracy, diversity, and so forth. The proposed approach can generate a pool of noninferior solutions and ensembles thereof having optimized trade-offs values of multiple conflicting objectives. In this paper, a three-phase, approach is proposed to generate solutions to a simple chromosome design in the first phase. In the first phase, a Pareto front of noninferior individual solutions is approximated. In the second phase of the proposed approach, the entire solution set is further refined to determine effective ensemble solutions considering solution interaction. In this phase, another improved Pareto front of ensemble solutions over that of individual solutions is approximated. The ensemble solutions in improved Pareto front reported improved detection results based on benchmark datasets for intrusion detection. In the third phase, a combination method like majority voting method is used to fuse the predictions of individual solutions for determining prediction of ensemble solution. Benchmark datasets, namely, KDD cup 1999 and ISCX 2012 dataset, are used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover individual solutions and ensemble solutions thereof with a good support and a detection rate from benchmark datasets (in comparison with well-known ensemble methods like bagging and boosting). In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives, and a dataset can be represented in the form of labelled instances in terms of its features. PMID:24376390

Thin layer chromatographic method for the detection of uric acid: collaborative study.

PubMed

Thrasher, J J; Abadie, A

1978-07-01

A collaborative study has been completed on an improved method for the detection and confirmation of uric acid from bird and insect excreta. The proposed method involves the lithium carbonate solubilization of the suspect excreta material, followed by butanol-methanol-water-acetic acid thin layer chromatography, and trisodium phosphate-phosphotungstic acid color development. The collaborative tests resulted in 100% detection of uric acid standard at the 50 ng level and 75% detection at the 20-25 ng level. No false positives were reported during tests of compounds similar to uric acid. The proposed method has been adopted official first action; the present official final action method, 44.161, will be retained for screening purposes.

Anomaly detection in reconstructed quantum states using a machine-learning technique

NASA Astrophysics Data System (ADS)

Hara, Satoshi; Ono, Takafumi; Okamoto, Ryo; Washio, Takashi; Takeuchi, Shigeki

2014-02-01

The accurate detection of small deviations in given density matrices is important for quantum information processing. Here we propose a method based on the concept of data mining. We demonstrate that the proposed method can more accurately detect small erroneous deviations in reconstructed density matrices, which contain intrinsic fluctuations due to the limited number of samples, than a naive method of checking the trace distance from the average of the given density matrices. This method has the potential to be a key tool in broad areas of physics where the detection of small deviations of quantum states reconstructed using a limited number of samples is essential.

Applications of 3D-EDGE Detection for ALS Point Cloud

NASA Astrophysics Data System (ADS)

Ni, H.; Lin, X. G.; Zhang, J. X.

2017-09-01

Edge detection has been one of the major issues in the field of remote sensing and photogrammetry. With the fast development of sensor technology of laser scanning system, dense point clouds have become increasingly common. Precious 3D-edges are able to be detected from these point clouds and a great deal of edge or feature line extraction methods have been proposed. Among these methods, an easy-to-use 3D-edge detection method, AGPN (Analyzing Geometric Properties of Neighborhoods), has been proposed. The AGPN method detects edges based on the analysis of geometric properties of a query point's neighbourhood. The AGPN method detects two kinds of 3D-edges, including boundary elements and fold edges, and it has many applications. This paper presents three applications of AGPN, i.e., 3D line segment extraction, ground points filtering, and ground breakline extraction. Experiments show that the utilization of AGPN method gives a straightforward solution to these applications.

Optic disc detection and boundary extraction in retinal images.

PubMed

Basit, A; Fraz, Muhammad Moazam

2015-04-10

With the development of digital image processing, analysis and modeling techniques, automatic retinal image analysis is emerging as an important screening tool for early detection of ophthalmologic disorders such as diabetic retinopathy and glaucoma. In this paper, a robust method for optic disc detection and extraction of the optic disc boundary is proposed to help in the development of computer-assisted diagnosis and treatment of such ophthalmic disease. The proposed method is based on morphological operations, smoothing filters, and the marker controlled watershed transform. Internal and external markers are used to first modify the gradient magnitude image and then the watershed transformation is applied on this modified gradient magnitude image for boundary extraction. This method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100%, 100%, 100% and 98.9% for the DRIVE, Shifa, CHASE_DB1, and DIARETDB1 databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 61.88%, 70.96%, 45.61%, and 54.69% for these databases, respectively, which are higher than currents methods.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

PubMed

Chu, Catherine J; Chan, Arthur; Song, Dan; Staley, Kevin J; Stufflebeam, Steven M; Kramer, Mark A

2017-02-01

High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of the Possibility a Right-Turn Driving Behavior at Intersection Leads to an Accident by Detecting Deviation of the Situation from Usual when the Behavior is Observed

NASA Astrophysics Data System (ADS)

Hayashi, Toshinori; Yamada, Keiichi

Deviation of driving behavior from usual could be a sign of human error that increases the risk of traffic accidents. This paper proposes a novel method for predicting the possibility a driving behavior leads to an accident from the information on the driving behavior and the situation. In a previous work, a method of predicting the possibility by detecting the deviation of driving behavior from usual one in that situation has been proposed. In contrast, the method proposed in this paper predicts the possibility by detecting the deviation of the situation from usual one when the behavior is observed. An advantage of the proposed method is the number of the required models is independent of the variety of the situations. The method was applied to a problem of predicting accidents by right-turn driving behavior at an intersection, and the performance of the method was evaluated by experiments on a driving simulator.

Chromatic dispersion estimation based on heterodyne detection for coherent optical communication systems

NASA Astrophysics Data System (ADS)

Li, Yong; Yang, Aiying; Guo, Peng; Qiao, Yaojun; Lu, Yueming

2018-01-01

We propose an accurate and nondata-aided chromatic dispersion (CD) estimation method involving the use of the cross-correlation function of two heterodyne detection signals for coherent optical communication systems. Simulations are implemented to verify the feasibility of the proposed method for 28-GBaud coherent systems with different modulation formats. The results show that the proposed method has high accuracy for measuring CD and has good robustness against laser phase noise, amplified spontaneous emission noise, and nonlinear impairments.

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique

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

Teramoto, Atsushi, E-mail: teramoto@fujita-hu.ac.jp; Fujita, Hiroshi; Yamamuro, Osamu

Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using anmore » active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors’ ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images.« less

Faint Debris Detection by Particle Based Track-Before-Detect Method

NASA Astrophysics Data System (ADS)

Uetsuhara, M.; Ikoma, N.

2014-09-01

This study proposes a particle method to detect faint debris, which is hardly seen in single frame, from an image sequence based on the concept of track-before-detect (TBD). The most widely used detection method is detect-before-track (DBT), which firstly detects signals of targets from single frame by distinguishing difference of intensity between foreground and background then associate the signals for each target between frames. DBT is capable of tracking bright targets but limited. DBT is necessary to consider presence of false signals and is difficult to recover from false association. On the other hand, TBD methods try to track targets without explicitly detecting the signals followed by evaluation of goodness of each track and obtaining detection results. TBD has an advantage over DBT in detecting weak signals around background level in single frame. However, conventional TBD methods for debris detection apply brute-force search over candidate tracks then manually select true one from the candidates. To reduce those significant drawbacks of brute-force search and not-fully automated process, this study proposes a faint debris detection algorithm by a particle based TBD method consisting of sequential update of target state and heuristic search of initial state. The state consists of position, velocity direction and magnitude, and size of debris over the image at a single frame. The sequential update process is implemented by a particle filter (PF). PF is an optimal filtering technique that requires initial distribution of target state as a prior knowledge. An evolutional algorithm (EA) is utilized to search the initial distribution. The EA iteratively applies propagation and likelihood evaluation of particles for the same image sequences and resulting set of particles is used as an initial distribution of PF. This paper describes the algorithm of the proposed faint debris detection method. The algorithm demonstrates performance on image sequences acquired during observation campaigns dedicated to GEO breakup fragments, which would contain a sufficient number of faint debris images. The results indicate the proposed method is capable of tracking faint debris with moderate computational costs at operational level.

Dim target detection method based on salient graph fusion

NASA Astrophysics Data System (ADS)

Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

2018-02-01

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

Remote sensing image ship target detection method based on visual attention model

NASA Astrophysics Data System (ADS)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

Quantum-state anomaly detection for arbitrary errors using a machine-learning technique

NASA Astrophysics Data System (ADS)

Hara, Satoshi; Ono, Takafumi; Okamoto, Ryo; Washio, Takashi; Takeuchi, Shigeki

2016-10-01

The accurate detection of small deviations in given density matrice is important for quantum information processing, which is a difficult task because of the intrinsic fluctuation in density matrices reconstructed using a limited number of experiments. We previously proposed a method for decoherence error detection using a machine-learning technique [S. Hara, T. Ono, R. Okamoto, T. Washio, and S. Takeuchi, Phys. Rev. A 89, 022104 (2014), 10.1103/PhysRevA.89.022104]. However, the previous method is not valid when the errors are just changes in phase. Here, we propose a method that is valid for arbitrary errors in density matrices. The performance of the proposed method is verified using both numerical simulation data and real experimental data.

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

PubMed Central

Chen, Fuming; Li, Sheng; Zhang, Yang; Wang, Jianqi

2017-01-01

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance. PMID:28282892

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

PubMed Central

2017-01-01

Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs. PMID:29065613

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

PubMed

Park, Jeong-Seon; Lee, Sang-Woong; Park, Unsang

2017-01-01

Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

Algorithm Summary and Evaluation: Automatic Implementation of Ringdown Analysis for Electromechanical Mode Identification from Phasor Measurements

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

Zhou, Ning; Huang, Zhenyu; Tuffner, Francis K.

2010-02-28

Small signal stability problems are one of the major threats to grid stability and reliability. Prony analysis has been successfully applied on ringdown data to monitor electromechanical modes of a power system using phasor measurement unit (PMU) data. To facilitate an on-line application of mode estimation, this paper develops a recursive algorithm for implementing Prony analysis and proposed an oscillation detection method to detect ringdown data in real time. By automatically detecting ringdown data, the proposed method helps guarantee that Prony analysis is applied properly and timely on the ringdown data. Thus, the mode estimation results can be performed reliablymore » and timely. The proposed method is tested using Monte Carlo simulations based on a 17-machine model and is shown to be able to properly identify the oscillation data for on-line application of Prony analysis. In addition, the proposed method is applied to field measurement data from WECC to show the performance of the proposed algorithm.« less

Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

NASA Astrophysics Data System (ADS)

Bagherzadeh, Seyed Amin; Asadi, Davood

2017-05-01

In search of a precise method for analyzing nonlinear and non-stationary flight data of an aircraft in the icing condition, an Empirical Mode Decomposition (EMD) algorithm enhanced by multi-objective optimization is introduced. In the proposed method, dissimilar IMF definitions are considered by the Genetic Algorithm (GA) in order to find the best decision parameters of the signal trend. To resolve disadvantages of the classical algorithm caused by the envelope concept, the signal trend is estimated directly in the proposed method. Furthermore, in order to simplify the performance and understanding of the EMD algorithm, the proposed method obviates the need for a repeated sifting process. The proposed enhanced EMD algorithm is verified by some benchmark signals. Afterwards, the enhanced algorithm is applied to simulated flight data in the icing condition in order to detect the ice assertion on the aircraft. The results demonstrate the effectiveness of the proposed EMD algorithm in aircraft ice detection by providing a figure of merit for the icing severity.

Multiple targets detection method in detection of UWB through-wall radar

NASA Astrophysics Data System (ADS)

Yang, Xiuwei; Yang, Chuanfa; Zhao, Xingwen; Tian, Xianzhong

2017-11-01

In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.

A nonlinear quality-related fault detection approach based on modified kernel partial least squares.

PubMed

Jiao, Jianfang; Zhao, Ning; Wang, Guang; Yin, Shen

2017-01-01

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Staircase-scene-based nonuniformity correction in aerial point target detection systems.

PubMed

Huo, Lijun; Zhou, Dabiao; Wang, Dejiang; Liu, Rang; He, Bin

2016-09-01

Focal-plane arrays (FPAs) are often interfered by heavy fixed-pattern noise, which severely degrades the detection rate and increases the false alarms in airborne point target detection systems. Thus, high-precision nonuniformity correction is an essential preprocessing step. In this paper, a new nonuniformity correction method is proposed based on a staircase scene. This correction method can compensate for the nonlinear response of the detector and calibrate the entire optical system with computational efficiency and implementation simplicity. Then, a proof-of-concept point target detection system is established with a long-wave Sofradir FPA. Finally, the local standard deviation of the corrected image and the signal-to-clutter ratio of the Airy disk of a Boeing B738 are measured to evaluate the performance of the proposed nonuniformity correction method. Our experimental results demonstrate that the proposed correction method achieves high-quality corrections.

Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints

PubMed Central

Cheng, Jianjun; Leng, Mingwei; Li, Longjie; Zhou, Hanhai; Chen, Xiaoyun

2014-01-01

Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods. PMID:25329660

Violence detection based on histogram of optical flow orientation

NASA Astrophysics Data System (ADS)

Yang, Zhijie; Zhang, Tao; Yang, Jie; Wu, Qiang; Bai, Li; Yao, Lixiu

2013-12-01

In this paper, we propose a novel approach for violence detection and localization in a public scene. Currently, violence detection is considerably under-researched compared with the common action recognition. Although existing methods can detect the presence of violence in a video, they cannot precisely locate the regions in the scene where violence is happening. This paper will tackle the challenge and propose a novel method to locate the violence location in the scene, which is important for public surveillance. The Gaussian Mixed Model is extended into the optical flow domain in order to detect candidate violence regions. In each region, a new descriptor, Histogram of Optical Flow Orientation (HOFO), is proposed to measure the spatial-temporal features. A linear SVM is trained based on the descriptor. The performance of the method is demonstrated on the publicly available data sets, BEHAVE and CAVIAR.

Detection of Road Markings Recorded in In-Vehicle Camera Images by Using Position-Dependent Classifiers

NASA Astrophysics Data System (ADS)

Noda, Masafumi; Takahashi, Tomokazu; Deguchi, Daisuke; Ide, Ichiro; Murase, Hiroshi; Kojima, Yoshiko; Naito, Takashi

In this study, we propose a method for detecting road markings recorded in an image captured by an in-vehicle camera by using a position-dependent classifier. Road markings are symbols painted on the road surface that help in preventing traffic accidents and in ensuring traffic smooth. Therefore, driver support systems for detecting road markings, such as a system that provides warning in the case when traffic signs are overlooked, and supporting the stopping of a vehicle are required. It is difficult to detect road markings because their appearance changes with the actual traffic conditions, e. g. the shape and resolution change. The variation in these appearances depend on the positional relation between the vehicle and the road markings, and on the vehicle posture. Although these variations are quite large in an entire image, they are relatively small in a local area of the image. Therefore, we try to improve the detection performance by taking into account the local variations in these appearances. We propose a method in which a position-dependent classifier is used to detect road markings recorded in images captured by an in-vehicle camera. Further, to train the classifier efficiently, we propose a generative learning method that takes into consideration the positional relation between the vehicle and road markings, and also the vehicle posture. Experimental results showed that the detection performance when the proposed method was used was better than when a method involving a single classifier was used.

COMPARISON OF TWO METHODS FOR DETECTION OF GIARDIA CYSTS AND CRYTOSPORIDIUM OOCYSTS IN WATER

EPA Science Inventory

The steps of two immunofluorescent-antibody-based detection methods were evaluated for their efficiencies in detecting Giardia cysts and Cryptosporidium oocysts. The two methods evaluated were the American Society for Testing and Materials proposed test method for Giardia cysts a...

A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection.

PubMed

Jin, Hongsheng; Li, Zongyao; Tong, Ruofeng; Lin, Lanfen

2018-05-01

The automatic detection of pulmonary nodules using CT scans improves the efficiency of lung cancer diagnosis, and false-positive reduction plays a significant role in the detection. In this paper, we focus on the false-positive reduction task and propose an effective method for this task. We construct a deep 3D residual CNN (convolution neural network) to reduce false-positive nodules from candidate nodules. The proposed network is much deeper than the traditional 3D CNNs used in medical image processing. Specifically, in the network, we design a spatial pooling and cropping (SPC) layer to extract multilevel contextual information of CT data. Moreover, we employ an online hard sample selection strategy in the training process to make the network better fit hard samples (e.g., nodules with irregular shapes). Our method is evaluated on 888 CT scans from the dataset of the LUNA16 Challenge. The free-response receiver operating characteristic (FROC) curve shows that the proposed method achieves a high detection performance. Our experiments confirm that our method is robust and that the SPC layer helps increase the prediction accuracy. Additionally, the proposed method can easily be extended to other 3D object detection tasks in medical image processing. © 2018 American Association of Physicists in Medicine.

A novel rail defect detection method based on undecimated lifting wavelet packet transform and Shannon entropy-improved adaptive line enhancer

NASA Astrophysics Data System (ADS)

Hao, Qiushi; Zhang, Xin; Wang, Yan; Shen, Yi; Makis, Viliam

2018-07-01

Acoustic emission (AE) technology is sensitive to subliminal rail defects, however strong wheel-rail contact rolling noise under high-speed condition has gravely impeded detecting of rail defects using traditional denoising methods. In this context, the paper develops an adaptive detection method for rail cracks, which combines multiresolution analysis with an improved adaptive line enhancer (ALE). To obtain elaborate multiresolution information of transient crack signals with low computational cost, lifting scheme-based undecimated wavelet packet transform is adopted. In order to feature the impulsive property of crack signals, a Shannon entropy-improved ALE is proposed as a signal enhancing approach, where Shannon entropy is introduced to improve the cost function. Then a rail defect detection plan based on the proposed method for high-speed condition is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in enhancing the rail defect AE signal and reducing the strong background noise, offering an effective multiresolution approach for rail defect detection under high-speed and strong-noise condition.

Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching.

PubMed

Huh, Yong; Yu, Kiyun; Park, Woojin

2016-01-01

This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48.

Detection of artifacts from high energy bursts in neonatal EEG.

PubMed

Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well. © 2013 Elsevier Ltd. All rights reserved.

Microaneurysm detection using fully convolutional neural networks.

PubMed

Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew

2018-05-01

Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications. Copyright © 2018 Elsevier B.V. All rights reserved.

High-resolution remotely sensed small target detection by imitating fly visual perception mechanism.

PubMed

Huang, Fengchen; Xu, Lizhong; Li, Min; Tang, Min

2012-01-01

The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

Detection of Abnormal Events via Optical Flow Feature Analysis

PubMed Central

Wang, Tian; Snoussi, Hichem

2015-01-01

In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

Wind profiling for a coherent wind Doppler lidar by an auto-adaptive background subtraction approach.

PubMed

Wu, Yanwei; Guo, Pan; Chen, Siying; Chen, He; Zhang, Yinchao

2017-04-01

Auto-adaptive background subtraction (AABS) is proposed as a denoising method for data processing of the coherent Doppler lidar (CDL). The method is proposed specifically for a low-signal-to-noise-ratio regime, in which the drifting power spectral density of CDL data occurs. Unlike the periodogram maximum (PM) and adaptive iteratively reweighted penalized least squares (airPLS), the proposed method presents reliable peaks and is thus advantageous in identifying peak locations. According to the analysis results of simulated and actually measured data, the proposed method outperforms the airPLS method and the PM algorithm in the furthest detectable range. The proposed method improves the detection range approximately up to 16.7% and 40% when compared to the airPLS method and the PM method, respectively. It also has smaller mean wind velocity and standard error values than the airPLS and PM methods. The AABS approach improves the quality of Doppler shift estimates and can be applied to obtain the whole wind profiling by the CDL.

The review and results of different methods for facial recognition

NASA Astrophysics Data System (ADS)

Le, Yifan

2017-09-01

In recent years, facial recognition draws much attention due to its wide potential applications. As a unique technology in Biometric Identification, facial recognition represents a significant improvement since it could be operated without cooperation of people under detection. Hence, facial recognition will be taken into defense system, medical detection, human behavior understanding, etc. Several theories and methods have been established to make progress in facial recognition: (1) A novel two-stage facial landmark localization method is proposed which has more accurate facial localization effect under specific database; (2) A statistical face frontalization method is proposed which outperforms state-of-the-art methods for face landmark localization; (3) It proposes a general facial landmark detection algorithm to handle images with severe occlusion and images with large head poses; (4) There are three methods proposed on Face Alignment including shape augmented regression method, pose-indexed based multi-view method and a learning based method via regressing local binary features. The aim of this paper is to analyze previous work of different aspects in facial recognition, focusing on concrete method and performance under various databases. In addition, some improvement measures and suggestions in potential applications will be put forward.

A novel method for overlapping community detection using Multi-objective optimization

NASA Astrophysics Data System (ADS)

Ebrahimi, Morteza; Shahmoradi, Mohammad Reza; Heshmati, Zainabolhoda; Salehi, Mostafa

2018-09-01

The problem of community detection as one of the most important applications of network science can be addressed effectively by multi-objective optimization. In this paper, we aim to present a novel efficient method based on this approach. Also, in this study the idea of using all Pareto fronts to detect overlapping communities is introduced. The proposed method has two main advantages compared to other multi-objective optimization based approaches. The first advantage is scalability, and the second is the ability to find overlapping communities. Despite most of the works, the proposed method is able to find overlapping communities effectively. The new algorithm works by extracting appropriate communities from all the Pareto optimal solutions, instead of choosing the one optimal solution. Empirical experiments on different features of separated and overlapping communities, on both synthetic and real networks show that the proposed method performs better in comparison with other methods.

Multi person detection and tracking based on hierarchical level-set method

NASA Astrophysics Data System (ADS)

Khraief, Chadia; Benzarti, Faouzi; Amiri, Hamid

2018-04-01

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

Detection of Differential Item Functioning Using the Lasso Approach

ERIC Educational Resources Information Center

Magis, David; Tuerlinckx, Francis; De Boeck, Paul

2015-01-01

This article proposes a novel approach to detect differential item functioning (DIF) among dichotomously scored items. Unlike standard DIF methods that perform an item-by-item analysis, we propose the "LR lasso DIF method": logistic regression (LR) model is formulated for all item responses. The model contains item-specific intercepts,…

A Distributed Signature Detection Method for Detecting Intrusions in Sensor Systems

PubMed Central

Kim, Ilkyu; Oh, Doohwan; Yoon, Myung Kuk; Yi, Kyueun; Ro, Won Woo

2013-01-01

Sensor nodes in wireless sensor networks are easily exposed to open and unprotected regions. A security solution is strongly recommended to prevent networks against malicious attacks. Although many intrusion detection systems have been developed, most systems are difficult to implement for the sensor nodes owing to limited computation resources. To address this problem, we develop a novel distributed network intrusion detection system based on the Wu–Manber algorithm. In the proposed system, the algorithm is divided into two steps; the first step is dedicated to a sensor node, and the second step is assigned to a base station. In addition, the first step is modified to achieve efficient performance under limited computation resources. We conduct evaluations with random string sets and actual intrusion signatures to show the performance improvement of the proposed method. The proposed method achieves a speedup factor of 25.96 and reduces 43.94% of packet transmissions to the base station compared with the previously proposed method. The system achieves efficient utilization of the sensor nodes and provides a structural basis of cooperative systems among the sensors. PMID:23529146

A distributed signature detection method for detecting intrusions in sensor systems.

PubMed

Kim, Ilkyu; Oh, Doohwan; Yoon, Myung Kuk; Yi, Kyueun; Ro, Won Woo

2013-03-25

Sensor nodes in wireless sensor networks are easily exposed to open and unprotected regions. A security solution is strongly recommended to prevent networks against malicious attacks. Although many intrusion detection systems have been developed, most systems are difficult to implement for the sensor nodes owing to limited computation resources. To address this problem, we develop a novel distributed network intrusion detection system based on the Wu-Manber algorithm. In the proposed system, the algorithm is divided into two steps; the first step is dedicated to a sensor node, and the second step is assigned to a base station. In addition, the first step is modified to achieve efficient performance under limited computation resources. We conduct evaluations with random string sets and actual intrusion signatures to show the performance improvement of the proposed method. The proposed method achieves a speedup factor of 25.96 and reduces 43.94% of packet transmissions to the base station compared with the previously proposed method. The system achieves efficient utilization of the sensor nodes and provides a structural basis of cooperative systems among the sensors.

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

DOE PAGES

Azim, Riyasat; Li, Fangxing; Xue, Yaosuo; ...

2017-07-14

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only whenmore » the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.« less

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

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

Azim, Riyasat; Li, Fangxing; Xue, Yaosuo

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only whenmore » the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.« less

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

PubMed Central

Sivaraks, Haemwaan

2015-01-01

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

Vision-based method for detecting driver drowsiness and distraction in driver monitoring system

NASA Astrophysics Data System (ADS)

Jo, Jaeik; Lee, Sung Joo; Jung, Ho Gi; Park, Kang Ryoung; Kim, Jaihie

2011-12-01

Most driver-monitoring systems have attempted to detect either driver drowsiness or distraction, although both factors should be considered for accident prevention. Therefore, we propose a new driver-monitoring method considering both factors. We make the following contributions. First, if the driver is looking ahead, drowsiness detection is performed; otherwise, distraction detection is performed. Thus, the computational cost and eye-detection error can be reduced. Second, we propose a new eye-detection algorithm that combines adaptive boosting, adaptive template matching, and blob detection with eye validation, thereby reducing the eye-detection error and processing time significantly, which is hardly achievable using a single method. Third, to enhance eye-detection accuracy, eye validation is applied after initial eye detection, using a support vector machine based on appearance features obtained by principal component analysis (PCA) and linear discriminant analysis (LDA). Fourth, we propose a novel eye state-detection algorithm that combines appearance features obtained using PCA and LDA, with statistical features such as the sparseness and kurtosis of the histogram from the horizontal edge image of the eye. Experimental results showed that the detection accuracies of the eye region and eye states were 99 and 97%, respectively. Both driver drowsiness and distraction were detected with a success rate of 98%.

A multi-layer steganographic method based on audio time domain segmented and network steganography

NASA Astrophysics Data System (ADS)

Xue, Pengfei; Liu, Hanlin; Hu, Jingsong; Hu, Ronggui

2018-05-01

Both audio steganography and network steganography are belong to modern steganography. Audio steganography has a large capacity. Network steganography is difficult to detect or track. In this paper, a multi-layer steganographic method based on the collaboration of them (MLS-ATDSS&NS) is proposed. MLS-ATDSS&NS is realized in two covert layers (audio steganography layer and network steganography layer) by two steps. A new audio time domain segmented steganography (ATDSS) method is proposed in step 1, and the collaboration method of ATDSS and NS is proposed in step 2. The experimental results showed that the advantage of MLS-ATDSS&NS over others is better trade-off between capacity, anti-detectability and robustness, that means higher steganographic capacity, better anti-detectability and stronger robustness.

Robust obstacle detection for unmanned surface vehicles

NASA Astrophysics Data System (ADS)

Qin, Yueming; Zhang, Xiuzhi

2018-03-01

Obstacle detection is of essential importance for Unmanned Surface Vehicles (USV). Although some obstacles (e.g., ships, islands) can be detected by Radar, there are many other obstacles (e.g., floating pieces of woods, swimmers) which are difficult to be detected via Radar because these obstacles have low radar cross section. Therefore, detecting obstacle from images taken onboard is an effective supplement. In this paper, a robust vision-based obstacle detection method for USVs is developed. The proposed method employs the monocular image sequence captured by the camera on the USVs and detects obstacles on the sea surface from the image sequence. The experiment results show that the proposed scheme is efficient to fulfill the obstacle detection task.

Detection of dominant flow and abnormal events in surveillance video

NASA Astrophysics Data System (ADS)

Kwak, Sooyeong; Byun, Hyeran

2011-02-01

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

Defect detection of castings in radiography images using a robust statistical feature.

PubMed

Zhao, Xinyue; He, Zaixing; Zhang, Shuyou

2014-01-01

One of the most commonly used optical methods for defect detection is radiographic inspection. Compared with methods that extract defects directly from the radiography image, model-based methods deal with the case of an object with complex structure well. However, detection of small low-contrast defects in nonuniformly illuminated images is still a major challenge for them. In this paper, we present a new method based on the grayscale arranging pairs (GAP) feature to detect casting defects in radiography images automatically. First, a model is built using pixel pairs with a stable intensity relationship based on the GAP feature from previously acquired images. Second, defects can be extracted by comparing the difference of intensity-difference signs between the input image and the model statistically. The robustness of the proposed method to noise and illumination variations has been verified on casting radioscopic images with defects. The experimental results showed that the average computation time of the proposed method in the testing stage is 28 ms per image on a computer with a Pentium Core 2 Duo 3.00 GHz processor. For the comparison, we also evaluated the performance of the proposed method as well as that of the mixture-of-Gaussian-based and crossing line profile methods. The proposed method achieved 2.7% and 2.0% false negative rates in the noise and illumination variation experiments, respectively.

An improved PCA method with application to boiler leak detection.

PubMed

Sun, Xi; Marquez, Horacio J; Chen, Tongwen; Riaz, Muhammad

2005-07-01

Principal component analysis (PCA) is a popular fault detection technique. It has been widely used in process industries, especially in the chemical industry. In industrial applications, achieving a sensitive system capable of detecting incipient faults, which maintains the false alarm rate to a minimum, is a crucial issue. Although a lot of research has been focused on these issues for PCA-based fault detection and diagnosis methods, sensitivity of the fault detection scheme versus false alarm rate continues to be an important issue. In this paper, an improved PCA method is proposed to address this problem. In this method, a new data preprocessing scheme and a new fault detection scheme designed for Hotelling's T2 as well as the squared prediction error are developed. A dynamic PCA model is also developed for boiler leak detection. This new method is applied to boiler water/steam leak detection with real data from Syncrude Canada's utility plant in Fort McMurray, Canada. Our results demonstrate that the proposed method can effectively reduce false alarm rate, provide effective and correct leak alarms, and give early warning to operators.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

An effective hair detection algorithm for dermoscopic melanoma images of skin lesions

NASA Astrophysics Data System (ADS)

Chakraborti, Damayanti; Kaur, Ravneet; Umbaugh, Scott; LeAnder, Robert

2016-09-01

Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method's relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.

PubMed

Amudha, P; Karthik, S; Sivakumari, S

2015-01-01

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

PubMed Central

Amudha, P.; Karthik, S.; Sivakumari, S.

2015-01-01

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625

Underwater Turbulence Detection Using Gated Wavefront Sensing Technique

PubMed Central

Bi, Ying; Xu, Xiping; Chow, Eddy Mun Tik

2018-01-01

Laser sensing has been applied in various underwater applications, ranging from underwater detection to laser underwater communications. However, there are several great challenges when profiling underwater turbulence effects. Underwater detection is greatly affected by the turbulence effect, where the acquired image suffers excessive noise, blurring, and deformation. In this paper, we propose a novel underwater turbulence detection method based on a gated wavefront sensing technique. First, we elaborate on the operating principle of gated wavefront sensing and wavefront reconstruction. We then setup an experimental system in order to validate the feasibility of our proposed method. The effect of underwater turbulence on detection is examined at different distances, and under different turbulence levels. The experimental results obtained from our gated wavefront sensing system indicate that underwater turbulence can be detected and analyzed. The proposed gated wavefront sensing system has the advantage of a simple structure and high detection efficiency for underwater environments. PMID:29518889

Detection of shifted double JPEG compression by an adaptive DCT coefficient model

NASA Astrophysics Data System (ADS)

Wang, Shi-Lin; Liew, Alan Wee-Chung; Li, Sheng-Hong; Zhang, Yu-Jin; Li, Jian-Hua

2014-12-01

In many JPEG image splicing forgeries, the tampered image patch has been JPEG-compressed twice with different block alignments. Such phenomenon in JPEG image forgeries is called the shifted double JPEG (SDJPEG) compression effect. Detection of SDJPEG-compressed patches could help in detecting and locating the tampered region. However, the current SDJPEG detection methods do not provide satisfactory results especially when the tampered region is small. In this paper, we propose a new SDJPEG detection method based on an adaptive discrete cosine transform (DCT) coefficient model. DCT coefficient distributions for SDJPEG and non-SDJPEG patches have been analyzed and a discriminative feature has been proposed to perform the two-class classification. An adaptive approach is employed to select the most discriminative DCT modes for SDJPEG detection. The experimental results show that the proposed approach can achieve much better results compared with some existing approaches in SDJPEG patch detection especially when the patch size is small.

Automated feature extraction in color retinal images by a model based approach.

PubMed

Li, Huiqi; Chutatape, Opas

2004-02-01

Color retinal photography is an important tool to detect the evidence of various eye diseases. Novel methods to extract the main features in color retinal images have been developed in this paper. Principal component analysis is employed to locate optic disk; A modified active shape model is proposed in the shape detection of optic disk; A fundus coordinate system is established to provide a better description of the features in the retinal images; An approach to detect exudates by the combined region growing and edge detection is proposed. The success rates of disk localization, disk boundary detection, and fovea localization are 99%, 94%, and 100%, respectively. The sensitivity and specificity of exudate detection are 100% and 71%, correspondingly. The success of the proposed algorithms can be attributed to the utilization of the model-based methods. The detection and analysis could be applied to automatic mass screening and diagnosis of the retinal diseases.

Real-time x-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions

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

Ma Yingliang; Housden, R. James; Razavi, Reza

2013-07-15

Purpose: X-ray fluoroscopically guided cardiac electrophysiology (EP) procedures are commonly carried out to treat patients with arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of a three-dimensional (3D) roadmap derived from preprocedural volumetric images can be used to add anatomical information. It is useful to know the position of the catheter electrodes relative to the cardiac anatomy, for example, to record ablation therapy locations during atrial fibrillation therapy. Also, the electrode positions of the coronary sinus (CS) catheter or lasso catheter can be used for road map motion correction.Methods: In this paper, the authors presentmore » a novel unified computational framework for image-based catheter detection and tracking without any user interaction. The proposed framework includes fast blob detection, shape-constrained searching and model-based detection. In addition, catheter tracking methods were designed based on the customized catheter models input from the detection method. Three real-time detection and tracking methods are derived from the computational framework to detect or track the three most common types of catheters in EP procedures: the ablation catheter, the CS catheter, and the lasso catheter. Since the proposed methods use the same blob detection method to extract key information from x-ray images, the ablation, CS, and lasso catheters can be detected and tracked simultaneously in real-time.Results: The catheter detection methods were tested on 105 different clinical fluoroscopy sequences taken from 31 clinical procedures. Two-dimensional (2D) detection errors of 0.50 {+-} 0.29, 0.92 {+-} 0.61, and 0.63 {+-} 0.45 mm as well as success rates of 99.4%, 97.2%, and 88.9% were achieved for the CS catheter, ablation catheter, and lasso catheter, respectively. With the tracking method, accuracies were increased to 0.45 {+-} 0.28, 0.64 {+-} 0.37, and 0.53 {+-} 0.38 mm and success rates increased to 100%, 99.2%, and 96.5% for the CS, ablation, and lasso catheters, respectively. Subjective clinical evaluation by three experienced electrophysiologists showed that the detection and tracking results were clinically acceptable.Conclusions: The proposed detection and tracking methods are automatic and can detect and track CS, ablation, and lasso catheters simultaneously and in real-time. The accuracy of the proposed methods is sub-mm and the methods are robust toward low-dose x-ray fluoroscopic images, which are mainly used during EP procedures to maintain low radiation dose.« less

Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

PubMed

Ito, Eisuke; Sato, Takaaki; Sano, Daisuke; Utagawa, Etsuko; Kato, Tsuyoshi

2018-06-01

A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.

Bayesian Peptide Peak Detection for High Resolution TOF Mass Spectrometry.

PubMed

Zhang, Jianqiu; Zhou, Xiaobo; Wang, Honghui; Suffredini, Anthony; Zhang, Lin; Huang, Yufei; Wong, Stephen

2010-11-01

In this paper, we address the issue of peptide ion peak detection for high resolution time-of-flight (TOF) mass spectrometry (MS) data. A novel Bayesian peptide ion peak detection method is proposed for TOF data with resolution of 10 000-15 000 full width at half-maximum (FWHW). MS spectra exhibit distinct characteristics at this resolution, which are captured in a novel parametric model. Based on the proposed parametric model, a Bayesian peak detection algorithm based on Markov chain Monte Carlo (MCMC) sampling is developed. The proposed algorithm is tested on both simulated and real datasets. The results show a significant improvement in detection performance over a commonly employed method. The results also agree with expert's visual inspection. Moreover, better detection consistency is achieved across MS datasets from patients with identical pathological condition.

Bayesian Peptide Peak Detection for High Resolution TOF Mass Spectrometry

PubMed Central

Zhang, Jianqiu; Zhou, Xiaobo; Wang, Honghui; Suffredini, Anthony; Zhang, Lin; Huang, Yufei; Wong, Stephen

2011-01-01

In this paper, we address the issue of peptide ion peak detection for high resolution time-of-flight (TOF) mass spectrometry (MS) data. A novel Bayesian peptide ion peak detection method is proposed for TOF data with resolution of 10 000–15 000 full width at half-maximum (FWHW). MS spectra exhibit distinct characteristics at this resolution, which are captured in a novel parametric model. Based on the proposed parametric model, a Bayesian peak detection algorithm based on Markov chain Monte Carlo (MCMC) sampling is developed. The proposed algorithm is tested on both simulated and real datasets. The results show a significant improvement in detection performance over a commonly employed method. The results also agree with expert’s visual inspection. Moreover, better detection consistency is achieved across MS datasets from patients with identical pathological condition. PMID:21544266

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

Automatic background updating for video-based vehicle detection

NASA Astrophysics Data System (ADS)

Hu, Chunhai; Li, Dongmei; Liu, Jichuan

2008-03-01

Video-based vehicle detection is one of the most valuable techniques for the Intelligent Transportation System (ITS). The widely used video-based vehicle detection technique is the background subtraction method. The key problem of this method is how to subtract and update the background effectively. In this paper an efficient background updating scheme based on Zone-Distribution for vehicle detection is proposed to resolve the problems caused by sudden camera perturbation, sudden or gradual illumination change and the sleeping person problem. The proposed scheme is robust and fast enough to satisfy the real-time constraints of vehicle detection.

Detecting multiple outliers in linear functional relationship model for circular variables using clustering technique

NASA Astrophysics Data System (ADS)

Mokhtar, Nurkhairany Amyra; Zubairi, Yong Zulina; Hussin, Abdul Ghapor

2017-05-01

Outlier detection has been used extensively in data analysis to detect anomalous observation in data and has important application in fraud detection and robust analysis. In this paper, we propose a method in detecting multiple outliers for circular variables in linear functional relationship model. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering procedure. With the use of tree diagram, we illustrate the graphical approach of the detection of outlier. A simulation study is done to verify the accuracy of the proposed method. Also, an illustration to a real data set is given to show its practical applicability.

A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency.

PubMed

Wang, Bo; Su, Yumin; Wan, Lei

2016-04-15

Special features in real marine environments such as cloud clutter, sea glint and weather conditions always result in various kinds of interference in optical images, which make it very difficult for unmanned surface vehicles (USVs) to detect the sea-sky line (SSL) accurately. To solve this problem a saliency-based SSL detection method is proposed. Through the computation of gradient saliency the line features of SSL are enhanced effectively, while other interference factors are relatively suppressed, and line support regions are obtained by a region growing method on gradient orientation. The SSL identification is achieved according to region contrast, line segment length and orientation features, and optimal state estimation of SSL detection is implemented by introducing a cubature Kalman filter (CKF). In the end, the proposed method is tested on a benchmark dataset from the "XL" USV in a real marine environment, and the experimental results demonstrate that the proposed method is significantly superior to other state-of-the-art methods in terms of accuracy rate and real-time performance, and its accuracy and stability are effectively improved by the CKF.

Salient object detection: manifold-based similarity adaptation approach

NASA Astrophysics Data System (ADS)

Zhou, Jingbo; Ren, Yongfeng; Yan, Yunyang; Gao, Shangbing

2014-11-01

A saliency detection algorithm based on manifold-based similarity adaptation is proposed. The proposed algorithm is divided into three steps. First, we segment an input image into superpixels, which are represented as the nodes in a graph. Second, a new similarity measurement is used in the proposed algorithm. The weight matrix of the graph, which indicates the similarities between the nodes, uses a similarity-based method. It also captures the manifold structure of the image patches, in which the graph edges are determined in a data adaptive manner in terms of both similarity and manifold structure. Then, we use local reconstruction method as a diffusion method to obtain the saliency maps. The objective function in the proposed method is based on local reconstruction, with which estimated weights capture the manifold structure. Experiments on four bench-mark databases demonstrate the accuracy and robustness of the proposed method.

Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models.

PubMed

Chan, Ta-Chien; Teng, Yung-Chu; Hwang, Jing-Shiang

2015-02-21

Emerging novel influenza outbreaks have increasingly been a threat to the public and a major concern of public health departments. Real-time data in seamless surveillance systems such as health insurance claims data for influenza-like illnesses (ILI) are ready for analysis, making it highly desirable to develop practical techniques to analyze such readymade data for outbreak detection so that the public can receive timely influenza epidemic warnings. This study proposes a simple and effective approach to analyze area-based health insurance claims data including outpatient and emergency department (ED) visits for early detection of any aberrations of ILI. The health insurance claims data during 2004-2009 from a national health insurance research database were used for developing early detection methods. The proposed approach fitted the daily new ILI visits and monitored the Pearson residuals directly for aberration detection. First, negative binomial regression was used for both outpatient and ED visits to adjust for potentially influential factors such as holidays, weekends, seasons, temporal dependence and temperature. Second, if the Pearson residuals exceeded 1.96, aberration signals were issued. The empirical validation of the model was done in 2008 and 2009. In addition, we designed a simulation study to compare the time of outbreak detection, non-detection probability and false alarm rate between the proposed method and modified CUSUM. The model successfully detected the aberrations of 2009 pandemic (H1N1) influenza virus in northern, central and southern Taiwan. The proposed approach was more sensitive in identifying aberrations in ED visits than those in outpatient visits. Simulation studies demonstrated that the proposed approach could detect the aberrations earlier, and with lower non-detection probability and mean false alarm rate in detecting aberrations compared to modified CUSUM methods. The proposed simple approach was able to filter out temporal trends, adjust for temperature, and issue warning signals for the first wave of the influenza epidemic in a timely and accurate manner.

Semantic Information Extraction of Lanes Based on Onboard Camera Videos

NASA Astrophysics Data System (ADS)

Tang, L.; Deng, T.; Ren, C.

2018-04-01

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

Three plot correlation-based small infrared target detection in dense sun-glint environment for infrared search and track

NASA Astrophysics Data System (ADS)

Kim, Sungho; Choi, Byungin; Kim, Jieun; Kwon, Soon; Kim, Kyung-Tae

2012-05-01

This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based infrared search and track (IRST) problem in dense sun-glint environment. It is critical to detect small infrared targets such as sea-skimming missiles or asymmetric small ships for national defense. On the sea surface, sun-glint clutters degrade the detection performance. Furthermore, if we have to detect true targets using only three images with a low frame rate camera, then the problem is more difficult. We propose a novel three plot correlation filter and statistics based clutter reduction method to achieve robust small target detection rate in dense sun-glint environment. We validate the robust detection performance of the proposed method via real infrared test sequences including synthetic targets.

Stepwise and stagewise approaches for spatial cluster detection.

PubMed

Xu, Jiale; Gangnon, Ronald E

2016-05-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either a hypothesis testing framework or a Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with a tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic areas. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. Copyright © 2016 Elsevier Ltd. All rights reserved.

GPS/DR Error Estimation for Autonomous Vehicle Localization.

PubMed

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-08-21

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.

GPS/DR Error Estimation for Autonomous Vehicle Localization

PubMed Central

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-01-01

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level. PMID:26307997

A salient region detection model combining background distribution measure for indoor robots.

PubMed

Li, Na; Xu, Hui; Wang, Zhenhua; Sun, Lining; Chen, Guodong

2017-01-01

Vision system plays an important role in the field of indoor robot. Saliency detection methods, capturing regions that are perceived as important, are used to improve the performance of visual perception system. Most of state-of-the-art methods for saliency detection, performing outstandingly in natural images, cannot work in complicated indoor environment. Therefore, we propose a new method comprised of graph-based RGB-D segmentation, primary saliency measure, background distribution measure, and combination. Besides, region roundness is proposed to describe the compactness of a region to measure background distribution more robustly. To validate the proposed approach, eleven influential methods are compared on the DSD and ECSSD dataset. Moreover, we build a mobile robot platform for application in an actual environment, and design three different kinds of experimental constructions that are different viewpoints, illumination variations and partial occlusions. Experimental results demonstrate that our model outperforms existing methods and is useful for indoor mobile robots.

Criteria For Evaluation of Proposed Protozoan Detection Methods

EPA Science Inventory

Currently, the only EPA approved method for detection and quantitation of protozoan cysts and oöcysts in source and drinking water, is the “ICR Protozoan Method for Detecting Giardia Cysts and Cryptosporidium Oöcysts in Water by a Fluorescent Antibody Procedure (ICR Microbial La...

Weak scratch detection and defect classification methods for a large-aperture optical element

NASA Astrophysics Data System (ADS)

Tao, Xian; Xu, De; Zhang, Zheng-Tao; Zhang, Feng; Liu, Xi-Long; Zhang, Da-Peng

2017-03-01

Surface defects on optics cause optic failure and heavy loss to the optical system. Therefore, surface defects on optics must be carefully inspected. This paper proposes a coarse-to-fine detection strategy of weak scratches in complicated dark-field images. First, all possible scratches are detected based on bionic vision. Then, each possible scratch is precisely positioned and connected to a complete scratch by the LSD and a priori knowledge. Finally, multiple scratches with various types can be detected in dark-field images. To classify defects and pollutants, a classification method based on GIST features is proposed. This paper uses many real dark-field images as experimental images. The results show that this method can detect multiple types of weak scratches in complex images and that the defects can be correctly distinguished with interference. This method satisfies the real-time and accurate detection requirements of surface defects.

Application of a Subspace-Based Fault Detection Method to Industrial Structures

NASA Astrophysics Data System (ADS)

Mevel, L.; Hermans, L.; van der Auweraer, H.

1999-11-01

Early detection and localization of damage allow increased expectations of reliability, safety and reduction of the maintenance cost. This paper deals with the industrial validation of a technique to monitor the health of a structure in operating conditions (e.g. rotating machinery, civil constructions subject to ambient excitations, etc.) and to detect slight deviations in a modal model derived from in-operation measured data. In this paper, a statistical local approach based on covariance-driven stochastic subspace identification is proposed. The capabilities and limitations of the method with respect to health monitoring and damage detection are discussed and it is explained how the method can be practically used in industrial environments. After the successful validation of the proposed method on a few laboratory structures, its application to a sports car is discussed. The example illustrates that the method allows the early detection of a vibration-induced fatigue problem of a sports car.

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

PubMed Central

Alacid, Beatriz

2018-01-01

This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images. PMID:29316716

Real time algorithms for sharp wave ripple detection.

PubMed

Sethi, Ankit; Kemere, Caleb

2014-01-01

Neural activity during sharp wave ripples (SWR), short bursts of co-ordinated oscillatory activity in the CA1 region of the rodent hippocampus, is implicated in a variety of memory functions from consolidation to recall. Detection of these events in an algorithmic framework, has thus far relied on simple thresholding techniques with heuristically derived parameters. This study is an investigation into testing and improving the current methods for detection of SWR events in neural recordings. We propose and profile methods to reduce latency in ripple detection. Proposed algorithms are tested on simulated ripple data. The findings show that simple realtime algorithms can improve upon existing power thresholding methods and can detect ripple activity with latencies in the range of 10-20 ms.

A competitive chemiluminescence enzyme immunoassay method for β-defensin-2 detection in transgenic mice.

PubMed

Yang, Xi; Zhou, Tao; Yu, Lei; Tan, Wenwen; Zhou, Rui; Hu, Yonggang

2015-03-01

A competitive chemiluminescence enzyme immunoassay (CLEIA) method for porcine β-defensin-2 (pBD-2) detection in transgenic mice was established. Several factors that affect detection, including luminol, p-iodophenol and hydrogen peroxide concentrations, as well as pH, were studied and optimized. The linear range of the proposed method for pBD-2 detection under optimal conditions was 0.05-80 ng/mL with a correlation coefficient of 0.9960. Eleven detections of a 30 ng/mL pBD-2 standard sample were performed. Reproducible results were obtained with a relative standard deviation of 3.94%. The limit of detection of the method for pBD-2 was 3.5 pg/mL (3σ). The proposed method was applied to determine pBD-2 expression levels in the tissues of pBD-2 transgenic mice, and compared with LC-MS/MS and quantitative real-time reverse-transcriptase polymerase chain reaction. This suggests that the CLEIA can be used as a valuable method to detect and quantify pBD-2. Copyright © 2014 John Wiley & Sons, Ltd.

Stokes-Mueller matrix polarimetry technique for circular dichroism/birefringence sensing with scattering effects.

PubMed

Phan, Quoc-Hung; Lo, Yu-Lung

2017-04-01

A surface plasmon resonance (SPR)-enhanced method is proposed for measuring the circular dichroism (CD), circular birefringence (CB), and degree of polarization (DOP) of turbid media using a Stokes–Mueller matrix polarimetry technique. The validity of the analytical model is confirmed by means of numerical simulations. The simulation results show that the proposed detection method enables the CD and CB properties to be measured with a resolution of 10 ? 4 refractive index unit (RIU) and 10 ? 5 ?? RIU , respectively, for refractive indices in the range of 1.3 to 1.4. The practical feasibility of the proposed method is demonstrated by detecting the CB/CD/DOP properties of glucose–chlorophyllin compound samples containing polystyrene microspheres. It is shown that the extracted CB value decreases linearly with the glucose concentration, while the extracted CD value increases linearly with the chlorophyllin concentration. However, the DOP is insensitive to both the glucose concentration and the chlorophyllin concentration. Consequently, the potential of the proposed SPR-enhanced Stokes–Mueller matrix polarimetry method for high-resolution CB/CD/DOP detection is confirmed. Notably, in contrast to conventional SPR techniques designed to detect relative refractive index changes, the SPR technique proposed in the present study allows absolute measurements of the optical properties (CB/CD/DOP) to be obtained.

Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

PubMed

Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

2016-10-05

Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

Particle detection for patterned wafers of 100nm design rule by evanescent light illumination: analysis of evanescent light scattering using Finite-Difference Time-Domain (FDTD) method

NASA Astrophysics Data System (ADS)

Yoshioka, Toshie; Miyoshi, Takashi; Takaya, Yasuhiro

2005-12-01

To realize high productivity and reliability of the semiconductor, patterned wafers inspection technology to maintain high yield becomes essential in modern semiconductor manufacturing processes. As circuit feature is scaled below 100nm, the conventional imaging and light scattering methods are impossible to apply to the patterned wafers inspection technique, because of diffraction limit and lower S/N ratio. So, we propose a new particle detection method using annular evanescent light illumination. In this method, a converging annular light used as a light source is incident on a micro-hemispherical lens. When the converging angle is larger than critical angle, annular evanescent light is generated under the bottom surface of the hemispherical lens. Evanescent light is localized near by the bottom surface and decays exponentially away from the bottom surface. So, the evanescent light selectively illuminates the particles on the patterned wafer surface, because it can't illuminate the patterned wafer surface. The proposed method evaluates particles on a patterned wafer surface by detecting scattered evanescent light distribution from particles. To analyze the fundamental characteristics of the proposed method, the computer simulation was performed using FDTD method. The simulation results show that the proposed method is effective for detecting 100nm size particle on patterned wafer of 100nm lines and spaces, particularly under the condition that the evanescent light illumination with p-polarization and parallel incident to the line orientation. Finally, the experiment results suggest that 220nm size particle on patterned wafer of about 200nm lines and spaces can be detected.

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PubMed

Jahanian, Hesamoddin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

2005-09-01

To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features. (c) 2005 Wiley-Liss, Inc.

Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator.

PubMed

Xu, Changhang; Xie, Jing; Zhang, Wuyang; Kong, Qingzhao; Chen, Guoming; Song, Gangbing

2017-11-23

Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner.

Incorrect Match Detection Method for Arctic Sea-Ice Reconstruction Using Uav Images

NASA Astrophysics Data System (ADS)

Kim, J.-I.; Kim, H.-C.

2018-05-01

Shapes and surface roughness, which are considered as key indicators in understanding Arctic sea-ice, can be measured from the digital surface model (DSM) of the target area. Unmanned aerial vehicle (UAV) flying at low altitudes enables theoretically accurate DSM generation. However, the characteristics of sea-ice with textureless surface and incessant motion make image matching difficult for DSM generation. In this paper, we propose a method for effectively detecting incorrect matches before correcting a sea-ice DSM derived from UAV images. The proposed method variably adjusts the size of search window to analyze the matching results of DSM generated and distinguishes incorrect matches. Experimental results showed that the sea-ice DSM produced large errors along the textureless surfaces, and that the incorrect matches could be effectively detected by the proposed method.

Community detection enhancement using non-negative matrix factorization with graph regularization

NASA Astrophysics Data System (ADS)

Liu, Xiao; Wei, Yi-Ming; Wang, Jian; Wang, Wen-Jun; He, Dong-Xiao; Song, Zhan-Jie

2016-06-01

Community detection is a meaningful task in the analysis of complex networks, which has received great concern in various domains. A plethora of exhaustive studies has made great effort and proposed many methods on community detection. Particularly, a kind of attractive one is the two-step method which first makes a preprocessing for the network and then identifies its communities. However, not all types of methods can achieve satisfactory results by using such preprocessing strategy, such as the non-negative matrix factorization (NMF) methods. In this paper, rather than using the above two-step method as most works did, we propose a graph regularized-based model to improve, specialized, the NMF-based methods for the detection of communities, namely NMFGR. In NMFGR, we introduce the similarity metric which contains both the global and local information of networks, to reflect the relationships between two nodes, so as to improve the accuracy of community detection. Experimental results on both artificial and real-world networks demonstrate the superior performance of NMFGR to some competing methods.

A matched filter approach for blind joint detection of galaxy clusters in X-ray and SZ surveys

NASA Astrophysics Data System (ADS)

Tarrío, P.; Melin, J.-B.; Arnaud, M.

2018-06-01

The combination of X-ray and Sunyaev-Zeldovich (SZ) observations can potentially improve the cluster detection efficiency, when compared to using only one of these probes, since both probe the same medium, the hot ionized gas of the intra-cluster medium. We present a method based on matched multifrequency filters (MMF) for detecting galaxy clusters from SZ and X-ray surveys. This method builds on a previously proposed joint X-ray-SZ extraction method and allows the blind detection of clusters, that is finding new clusters without knowing their position, size, or redshift, by searching on SZ and X-ray maps simultaneously. The proposed method is tested using data from the ROSAT all-sky survey and from the Planck survey. The evaluation is done by comparison with existing cluster catalogues in the area of the sky covered by the deep SPT survey. Thanks to the addition of the X-ray information, the joint detection method is able to achieve simultaneously better purity, better detection efficiency, and better position accuracy than its predecessor Planck MMF, which is based on SZ maps alone. For a purity of 85%, the X-ray-SZ method detects 141 confirmed clusters in the SPT region; to detect the same number of confirmed clusters with Planck MMF, we would need to decrease its purity to 70%. We provide a catalogue of 225 sources selected by the proposed method in the SPT footprint, with masses ranging between 0.7 and 14.5 ×1014 M⊙ and redshifts between 0.01 and 1.2.

Novel Maximum-based Timing Acquisition for Spread-Spectrum Communications

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

Sibbetty, Taylor; Moradiz, Hussein; Farhang-Boroujeny, Behrouz

This paper proposes and analyzes a new packet detection and timing acquisition method for spread spectrum systems. The proposed method provides an enhancement over the typical thresholding techniques that have been proposed for direct sequence spread spectrum (DS-SS). The effective implementation of thresholding methods typically require accurate knowledge of the received signal-to-noise ratio (SNR), which is particularly difficult to estimate in spread spectrum systems. Instead, we propose a method which utilizes a consistency metric of the location of maximum samples at the output of a filter matched to the spread spectrum waveform to achieve acquisition, and does not require knowledgemore » of the received SNR. Through theoretical study, we show that the proposed method offers a low probability of missed detection over a large range of SNR with a corresponding probability of false alarm far lower than other methods. Computer simulations that corroborate our theoretical results are also presented. Although our work here has been motivated by our previous study of a filter bank multicarrier spread-spectrum (FB-MC-SS) system, the proposed method is applicable to DS-SS systems as well.« less

Research of infrared laser based pavement imaging and crack detection

NASA Astrophysics Data System (ADS)

Hong, Hanyu; Wang, Shu; Zhang, Xiuhua; Jing, Genqiang

2013-08-01

Road crack detection is seriously affected by many factors in actual applications, such as some shadows, road signs, oil stains, high frequency noise and so on. Due to these factors, the current crack detection methods can not distinguish the cracks in complex scenes. In order to solve this problem, a novel method based on infrared laser pavement imaging is proposed. Firstly, single sensor laser pavement imaging system is adopted to obtain pavement images, high power laser line projector is well used to resist various shadows. Secondly, the crack extraction algorithm which has merged multiple features intelligently is proposed to extract crack information. In this step, the non-negative feature and contrast feature are used to extract the basic crack information, and circular projection based on linearity feature is applied to enhance the crack area and eliminate noise. A series of experiments have been performed to test the proposed method, which shows that the proposed automatic extraction method is effective and advanced.

Novel Hyperspectral Anomaly Detection Methods Based on Unsupervised Nearest Regularized Subspace

NASA Astrophysics Data System (ADS)

Hou, Z.; Chen, Y.; Tan, K.; Du, P.

2018-04-01

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

Event Detection Using Mobile Phone Mass GPS Data and Their Reliavility Verification by Dmsp/ols Night Light Image

NASA Astrophysics Data System (ADS)

Yuki, Akiyama; Satoshi, Ueyama; Ryosuke, Shibasaki; Adachi, Ryuichiro

2016-06-01

In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an "Event," all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.

Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation.

PubMed

Bernhardt, Paul W; Wang, Huixia Judy; Zhang, Daowen

2014-01-01

Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multiple imputation methods work well while alternative methods lead to estimates that are either biased or more variable. The proposed methods are applied to analyze the dataset from a recently-conducted GenIMS study.

Vision-Based Real-Time Traversable Region Detection for Mobile Robot in the Outdoors.

PubMed

Deng, Fucheng; Zhu, Xiaorui; He, Chao

2017-09-13

Environment perception is essential for autonomous mobile robots in human-robot coexisting outdoor environments. One of the important tasks for such intelligent robots is to autonomously detect the traversable region in an unstructured 3D real world. The main drawback of most existing methods is that of high computational complexity. Hence, this paper proposes a binocular vision-based, real-time solution for detecting traversable region in the outdoors. In the proposed method, an appearance model based on multivariate Gaussian is quickly constructed from a sample region in the left image adaptively determined by the vanishing point and dominant borders. Then, a fast, self-supervised segmentation scheme is proposed to classify the traversable and non-traversable regions. The proposed method is evaluated on public datasets as well as a real mobile robot. Implementation on the mobile robot has shown its ability in the real-time navigation applications.

Automated grain extraction and classification by combining improved region growing segmentation and shape descriptors in electromagnetic mill classification system

NASA Astrophysics Data System (ADS)

Budzan, Sebastian

2018-04-01

In this paper, the automatic method of grain detection and classification has been presented. As input, it uses a single digital image obtained from milling process of the copper ore with an high-quality digital camera. The grinding process is an extremely energy and cost consuming process, thus granularity evaluation process should be performed with high efficiency and time consumption. The method proposed in this paper is based on the three-stage image processing. First, using Seeded Region Growing (SRG) segmentation with proposed adaptive thresholding based on the calculation of Relative Standard Deviation (RSD) all grains are detected. In the next step results of the detection are improved using information about the shape of the detected grains using distance map. Finally, each grain in the sample is classified into one of the predefined granularity class. The quality of the proposed method has been obtained by using nominal granularity samples, also with a comparison to the other methods.

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers.

PubMed

Shu, Ting; Zhang, Bob; Tang, Yuan Yan

2017-01-01

At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

Detecting and isolating abrupt changes in linear switching systems

NASA Astrophysics Data System (ADS)

Nazari, Sohail; Zhao, Qing; Huang, Biao

2015-04-01

In this paper, a novel fault detection and isolation (FDI) method for switching linear systems is developed. All input and output signals are assumed to be corrupted with measurement noises. In the proposed method, a 'lifted' linear model named as stochastic hybrid decoupling polynomial (SHDP) is introduced. The SHDP model governs the dynamics of the switching linear system with all different modes, and is independent of the switching sequence. The error-in-variable (EIV) representation of SHDP is derived, and is used for the fault residual generation and isolation following the well-adopted local approach. The proposed FDI method can detect and isolate the fault-induced abrupt changes in switching models' parameters without estimating the switching modes. Furthermore, in this paper, the analytical expressions of the gradient vector and Hessian matrix are obtained based on the EIV SHDP formulation, so that they can be used to implement the online fault detection scheme. The performance of the proposed method is then illustrated by simulation examples.

Corner detection and sorting method based on improved Harris algorithm in camera calibration

NASA Astrophysics Data System (ADS)

Xiao, Ying; Wang, Yonghong; Dan, Xizuo; Huang, Anqi; Hu, Yue; Yang, Lianxiang

2016-11-01

In traditional Harris corner detection algorithm, the appropriate threshold which is used to eliminate false corners is selected manually. In order to detect corners automatically, an improved algorithm which combines Harris and circular boundary theory of corners is proposed in this paper. After detecting accurate corner coordinates by using Harris algorithm and Forstner algorithm, false corners within chessboard pattern of the calibration plate can be eliminated automatically by using circular boundary theory. Moreover, a corner sorting method based on an improved calibration plate is proposed to eliminate false background corners and sort remaining corners in order. Experiment results show that the proposed algorithms can eliminate all false corners and sort remaining corners correctly and automatically.

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

PubMed

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Convolutional neural network features based change detection in satellite images

NASA Astrophysics Data System (ADS)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model.

PubMed

Li, Bin; Chen, Kan; Tian, Lianfang; Yeboah, Yao; Ou, Shanxing

2013-01-01

The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and "weak" local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method.

Modeling panel detection frequencies by queuing system theory: an application in gas chromatography olfactometry.

PubMed

Bult, Johannes H F; van Putten, Bram; Schifferstein, Hendrik N J; Roozen, Jacques P; Voragen, Alphons G J; Kroeze, Jan H A

2004-10-01

In continuous vigilance tasks, the number of coincident panel responses to stimuli provides an index of stimulus detectability. To determine whether this number is due to chance, panel noise levels have been approximated by the maximum coincidence level obtained in stimulus-free conditions. This study proposes an alternative method by which to assess noise levels, derived from queuing system theory (QST). Instead of critical coincidence levels, QST modeling estimates the duration of coinciding responses in the absence of stimuli. The proposed method has the advantage over previous approaches that it yields more reliable noise estimates and allows for statistical testing. The method was applied in an olfactory detection experiment using 16 panelists in stimulus-present and stimulus-free conditions. We propose that QST may be used as an alternative to signal detection theory for analyzing data from continuous vigilance tasks.

Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

PubMed

Ge, Jing; Zhang, Guoping

2015-01-01

Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

Cell tracking for cell image analysis

NASA Astrophysics Data System (ADS)

Bise, Ryoma; Sato, Yoichi

2017-04-01

Cell image analysis is important for research and discovery in biology and medicine. In this paper, we present our cell tracking methods, which is capable of obtaining fine-grain cell behavior metrics. In order to address difficulties under dense culture conditions, where cell detection cannot be done reliably since cell often touch with blurry intercellular boundaries, we proposed two methods which are global data association and jointly solving cell detection and association. We also show the effectiveness of the proposed methods by applying the method to the biological researches.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements.

PubMed

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K; Cai, Chang; Nagarajan, Srikantan S

2018-06-01

Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements

NASA Astrophysics Data System (ADS)

Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K.; Cai, Chang; Nagarajan, Srikantan S.

2018-06-01

Objective. Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. Approach. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Main results. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. Significance. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Pavement crack detection combining non-negative feature with fast LoG in complex scene

NASA Astrophysics Data System (ADS)

Wang, Wanli; Zhang, Xiuhua; Hong, Hanyu

2015-12-01

Pavement crack detection is affected by much interference in the realistic situation, such as the shadow, road sign, oil stain, salt and pepper noise etc. Due to these unfavorable factors, the exist crack detection methods are difficult to distinguish the crack from background correctly. How to extract crack information effectively is the key problem to the road crack detection system. To solve this problem, a novel method for pavement crack detection based on combining non-negative feature with fast LoG is proposed. The two key novelties and benefits of this new approach are that 1) using image pixel gray value compensation to acquisit uniform image, and 2) combining non-negative feature with fast LoG to extract crack information. The image preprocessing results demonstrate that the method is indeed able to homogenize the crack image with more accurately compared to existing methods. A large number of experimental results demonstrate the proposed approach can detect the crack regions more correctly compared with traditional methods.

A signal-based fault detection and classification method for heavy haul wagons

NASA Astrophysics Data System (ADS)

Li, Chunsheng; Luo, Shihui; Cole, Colin; Spiryagin, Maksym; Sun, Yanquan

2017-12-01

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.

Deep neural network-based bandwidth enhancement of photoacoustic data.

PubMed

Gutta, Sreedevi; Kadimesetty, Venkata Suryanarayana; Kalva, Sandeep Kumar; Pramanik, Manojit; Ganapathy, Sriram; Yalavarthy, Phaneendra K

2017-11-01

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data.

PubMed

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-05-15

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

PubMed Central

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-01-01

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. PMID:28505135

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

PubMed

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Total Count of Salmonella typhimurium Coupled on Water Soluble CdSe Quantum Dots by Fluorescence Detection

NASA Astrophysics Data System (ADS)

Feliciano Crespo, Raquel; Perales Perez, Oscar Juan; Ramirez, C.

2018-05-01

Health diseases due to the ingestion of water or food contaminated with pathogenic microorganisms are a main health problem around the world. The traditional methods for detecting foodborne pathogens are time-consuming (on the order of days). The development of methods that can help to detect and identify foodborne pathogens with high sensitivity and specificity have been proposed to overcome the limitations of traditional methods. Accordingly, this research is focused on the development of an experimental protocol for a high-sensitivity detection and quantification of bacterial pathogens with reduced detection times. This will lead to the development of a portable and low-cost technology with the opportunity to make onsite detection of pathogenic species. The proposed approach has modified the route reported in the literature; the method proposed is expected to be sensitive enough to detect a low limit of 102 CFU/mL counts of bacteria. The fluorescence-based method was tested in presence of Salmonella typhimurium (ATCC 14020) and Escherichia coli (ATCC 25922). CdSe water-soluble quantum dots (QDs) were synthesized in aqueous phase in presence of thioglycolic acid (TGA) as a capping agent. As-synthesized QDs were characterized by x-ray diffraction, near infrared and Fourier transform infrared spectroscopy, UV-Vis and photoluminescence techniques. Results of the CdSe/TGA-bacteria coupling and the determination of the corresponding quantification profiles (calibration curves) will be presented and discussed.

A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.

PubMed

Christodoulidis, Argyrios; Hurtut, Thomas; Tahar, Houssem Ben; Cheriet, Farida

2016-09-01

Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05). Copyright © 2016 Elsevier Ltd. All rights reserved.

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar-radar sensor fusion

NASA Astrophysics Data System (ADS)

Kwon, Seong Kyung; Hyun, Eugin; Lee, Jin-Hee; Lee, Jonghun; Son, Sang Hyuk

2017-11-01

Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar-radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.

Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

PubMed

Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

2017-07-01

Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

An integrative time-varying frequency detection and channel sounding method for dynamic plasma sheath

NASA Astrophysics Data System (ADS)

Shi, Lei; Yao, Bo; Zhao, Lei; Liu, Xiaotong; Yang, Min; Liu, Yanming

2018-01-01

The plasma sheath-surrounded hypersonic vehicle is a dynamic and time-varying medium and it is almost impossible to calculate time-varying physical parameters directly. The in-fight detection of the time-varying degree is important to understand the dynamic nature of the physical parameters and their effect on re-entry communication. In this paper, a constant envelope zero autocorrelation (CAZAC) sequence based on time-varying frequency detection and channel sounding method is proposed to detect the plasma sheath electronic density time-varying property and wireless channel characteristic. The proposed method utilizes the CAZAC sequence, which has excellent autocorrelation and spread gain characteristics, to realize dynamic time-varying detection/channel sounding under low signal-to-noise ratio in the plasma sheath environment. Theoretical simulation under a typical time-varying radio channel shows that the proposed method is capable of detecting time-variation frequency up to 200 kHz and can trace the channel amplitude and phase in the time domain well under -10 dB. Experimental results conducted in the RF modulation discharge plasma device verified the time variation detection ability in practical dynamic plasma sheath. Meanwhile, nonlinear phenomenon of dynamic plasma sheath on communication signal is observed thorough channel sounding result.

Regularized non-stationary morphological reconstruction algorithm for weak signal detection in microseismic monitoring: methodology

NASA Astrophysics Data System (ADS)

Huang, Weilin; Wang, Runqiu; Chen, Yangkang

2018-05-01

Microseismic signal is typically weak compared with the strong background noise. In order to effectively detect the weak signal in microseismic data, we propose a mathematical morphology based approach. We decompose the initial data into several morphological multiscale components. For detection of weak signal, a non-stationary weighting operator is proposed and introduced into the process of reconstruction of data by morphological multiscale components. The non-stationary weighting operator can be obtained by solving an inversion problem. The regularized non-stationary method can be understood as a non-stationary matching filtering method, where the matching filter has the same size as the data to be filtered. In this paper, we provide detailed algorithmic descriptions and analysis. The detailed algorithm framework, parameter selection and computational issue for the regularized non-stationary morphological reconstruction (RNMR) method are presented. We validate the presented method through a comprehensive analysis through different data examples. We first test the proposed technique using a synthetic data set. Then the proposed technique is applied to a field project, where the signals induced from hydraulic fracturing are recorded by 12 three-component geophones in a monitoring well. The result demonstrates that the RNMR can improve the detectability of the weak microseismic signals. Using the processed data, the short-term-average over long-term average picking algorithm and Geiger's method are applied to obtain new locations of microseismic events. In addition, we show that the proposed RNMR method can be used not only in microseismic data but also in reflection seismic data to detect the weak signal. We also discussed the extension of RNMR from 1-D to 2-D or a higher dimensional version.

Detection of small surface defects using DCT based enhancement approach in machine vision systems

NASA Astrophysics Data System (ADS)

He, Fuqiang; Wang, Wen; Chen, Zichen

2005-12-01

Utilizing DCT based enhancement approach, an improved small defect detection algorithm for real-time leather surface inspection was developed. A two-stage decomposition procedure was proposed to extract an odd-odd frequency matrix after a digital image has been transformed to DCT domain. Then, the reverse cumulative sum algorithm was proposed to detect the transition points of the gentle curves plotted from the odd-odd frequency matrix. The best radius of the cutting sector was computed in terms of the transition points and the high-pass filtering operation was implemented. The filtered image was then inversed and transformed back to the spatial domain. Finally, the restored image was segmented by an entropy method and some defect features are calculated. Experimental results show the proposed small defect detection method can reach the small defect detection rate by 94%.

Vision Sensor-Based Road Detection for Field Robot Navigation

PubMed Central

Lu, Keyu; Li, Jian; An, Xiangjing; He, Hangen

2015-01-01

Road detection is an essential component of field robot navigation systems. Vision sensors play an important role in road detection for their great potential in environmental perception. In this paper, we propose a hierarchical vision sensor-based method for robust road detection in challenging road scenes. More specifically, for a given road image captured by an on-board vision sensor, we introduce a multiple population genetic algorithm (MPGA)-based approach for efficient road vanishing point detection. Superpixel-level seeds are then selected in an unsupervised way using a clustering strategy. Then, according to the GrowCut framework, the seeds proliferate and iteratively try to occupy their neighbors. After convergence, the initial road segment is obtained. Finally, in order to achieve a globally-consistent road segment, the initial road segment is refined using the conditional random field (CRF) framework, which integrates high-level information into road detection. We perform several experiments to evaluate the common performance, scale sensitivity and noise sensitivity of the proposed method. The experimental results demonstrate that the proposed method exhibits high robustness compared to the state of the art. PMID:26610514

Novel vehicle detection system based on stacked DoG kernel and AdaBoost

PubMed Central

Kang, Hyun Ho; Lee, Seo Won; You, Sung Hyun

2018-01-01

This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible in the image due to the light conditions. A stacked Difference of Gaussian (DoG)–based feature extraction algorithm is proposed to address this issue by recognizing common characteristics, such as the shadow and rear wheels beneath vehicles—of vehicles under various conditions. The common characteristics of vehicles are extracted by applying the stacked DoG shaped kernel obtained from the 3D plot of an image through a convolution method and investigating only certain regions that have a similar patterns. A new vehicle detection system is constructed by combining the novel stacked DoG feature extraction algorithm with the AdaBoost method. Experiments are provided to demonstrate the effectiveness of the proposed vehicle detection system under different conditions. PMID:29513727

Road traffic sign detection and classification from mobile LiDAR point clouds

NASA Astrophysics Data System (ADS)

Weng, Shengxia; Li, Jonathan; Chen, Yiping; Wang, Cheng

2016-03-01

Traffic signs are important roadway assets that provide valuable information of the road for drivers to make safer and easier driving behaviors. Due to the development of mobile mapping systems that can efficiently acquire dense point clouds along the road, automated detection and recognition of road assets has been an important research issue. This paper deals with the detection and classification of traffic signs in outdoor environments using mobile light detection and ranging (Li- DAR) and inertial navigation technologies. The proposed method contains two main steps. It starts with an initial detection of traffic signs based on the intensity attributes of point clouds, as the traffic signs are always painted with highly reflective materials. Then, the classification of traffic signs is achieved based on the geometric shape and the pairwise 3D shape context. Some results and performance analyses are provided to show the effectiveness and limits of the proposed method. The experimental results demonstrate the feasibility and effectiveness of the proposed method in detecting and classifying traffic signs from mobile LiDAR point clouds.

Detect2Rank: Combining Object Detectors Using Learning to Rank.

PubMed

Karaoglu, Sezer; Yang Liu; Gevers, Theo

2016-01-01

Object detection is an important research area in the field of computer vision. Many detection algorithms have been proposed. However, each object detector relies on specific assumptions of the object appearance and imaging conditions. As a consequence, no algorithm can be considered universal. With the large variety of object detectors, the subsequent question is how to select and combine them. In this paper, we propose a framework to learn how to combine object detectors. The proposed method uses (single) detectors like Deformable Part Models, Color Names and Ensemble of Exemplar-SVMs, and exploits their correlation by high-level contextual features to yield a combined detection list. Experiments on the PASCAL VOC07 and VOC10 data sets show that the proposed method significantly outperforms single object detectors, DPM (8.4%), CN (6.8%) and EES (17.0%) on VOC07 and DPM (6.5%), CN (5.5%) and EES (16.2%) on VOC10. We show with an experiment that there are no constraints on the type of the detector. The proposed method outperforms (2.4%) the state-of-the-art object detector (RCNN) on VOC07 when Regions with Convolutional Neural Network is combined with other detectors used in this paper.

Trainable multiscript orientation detection

NASA Astrophysics Data System (ADS)

Van Beusekom, Joost; Rangoni, Yves; Breuel, Thomas M.

2010-01-01

Detecting the correct orientation of document images is an important step in large scale digitization processes, as most subsequent document analysis and optical character recognition methods assume upright position of the document page. Many methods have been proposed to solve the problem, most of which base on ascender to descender ratio computation. Unfortunately, this cannot be used for scripts having no descenders nor ascenders. Therefore, we present a trainable method using character similarity to compute the correct orientation. A connected component based distance measure is computed to compare the characters of the document image to characters whose orientation is known. This allows to detect the orientation for which the distance is lowest as the correct orientation. Training is easily achieved by exchanging the reference characters by characters of the script to be analyzed. Evaluation of the proposed approach showed accuracy of above 99% for Latin and Japanese script from the public UW-III and UW-II datasets. An accuracy of 98.9% was obtained for Fraktur on a non-public dataset. Comparison of the proposed method to two methods using ascender / descender ratio based orientation detection shows a significant improvement.

Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach.

PubMed

Abd El Aziz, Mohamed; Selim, I M; Xiong, Shengwu

2017-06-30

This paper presents a new approach for the automatic detection of galaxy morphology from datasets based on an image-retrieval approach. Currently, there are several classification methods proposed to detect galaxy types within an image. However, in some situations, the aim is not only to determine the type of galaxy within the queried image, but also to determine the most similar images for query image. Therefore, this paper proposes an image-retrieval method to detect the type of galaxies within an image and return with the most similar image. The proposed method consists of two stages, in the first stage, a set of features is extracted based on shape, color and texture descriptors, then a binary sine cosine algorithm selects the most relevant features. In the second stage, the similarity between the features of the queried galaxy image and the features of other galaxy images is computed. Our experiments were performed using the EFIGI catalogue, which contains about 5000 galaxies images with different types (edge-on spiral, spiral, elliptical and irregular). We demonstrate that our proposed approach has better performance compared with the particle swarm optimization (PSO) and genetic algorithm (GA) methods.

Ship detection in panchromatic images: a new method and its DSP implementation

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Wang, Mengfei; Meng, Gang

2016-03-01

In this paper, a new ship detection method is proposed after analyzing the characteristics of panchromatic remote sensing images and ship targets. Firstly, AdaBoost(Adaptive Boosting) classifiers trained by Haar features are utilized to make coarse detection of ship targets. Then LSD (Line Segment Detector) is adopted to extract the line features in target slices to make fine detection. Experimental results on a dataset of panchromatic remote sensing images with a spatial resolution of 2m show that the proposed algorithm can achieve high detection rate and low false alarm rate. Meanwhile, the algorithm can meet the needs of practical applications on DSP (Digital Signal Processor).

Robust skin color-based moving object detection for video surveillance

NASA Astrophysics Data System (ADS)

Kaliraj, Kalirajan; Manimaran, Sudha

2016-07-01

Robust skin color-based moving object detection for video surveillance is proposed. The objective of the proposed algorithm is to detect and track the target under complex situations. The proposed framework comprises four stages, which include preprocessing, skin color-based feature detection, feature classification, and target localization and tracking. In the preprocessing stage, the input image frame is smoothed using averaging filter and transformed into YCrCb color space. In skin color detection, skin color regions are detected using Otsu's method of global thresholding. In the feature classification, histograms of both skin and nonskin regions are constructed and the features are classified into foregrounds and backgrounds based on Bayesian skin color classifier. The foreground skin regions are localized by a connected component labeling process. Finally, the localized foreground skin regions are confirmed as a target by verifying the region properties, and nontarget regions are rejected using the Euler method. At last, the target is tracked by enclosing the bounding box around the target region in all video frames. The experiment was conducted on various publicly available data sets and the performance was evaluated with baseline methods. It evidently shows that the proposed algorithm works well against slowly varying illumination, target rotations, scaling, fast, and abrupt motion changes.

A spatial scan statistic for nonisotropic two-level risk cluster.

PubMed

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2012-01-30

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

Using State Estimation Residuals to Detect Abnormal SCADA Data

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

Ma, Jian; Chen, Yousu; Huang, Zhenyu

2010-04-30

Detection of abnormal supervisory control and data acquisition (SCADA) data is critically important for safe and secure operation of modern power systems. In this paper, a methodology of abnormal SCADA data detection based on state estimation residuals is presented. Preceded with a brief overview of outlier detection methods and bad SCADA data detection for state estimation, the framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection algorithm. The BACON algorithm ismore » applied to the outlier detection task. The IEEE 118-bus system is used as a test base to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.« less

A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

NASA Astrophysics Data System (ADS)

Barat, Christian; Phlypo, Ronald

2010-12-01

We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

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.

Particle swarm optimization method for small retinal vessels detection on multiresolution fundus images.

PubMed

Khomri, Bilal; Christodoulidis, Argyrios; Djerou, Leila; Babahenini, Mohamed Chaouki; Cheriet, Farida

2018-05-01

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high- and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two low-resolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Glue detection based on teaching points constraint and tracking model of pixel convolution

NASA Astrophysics Data System (ADS)

Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen

2018-01-01

On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.

Kernel parameter variation-based selective ensemble support vector data description for oil spill detection on the ocean via hyperspectral imaging

NASA Astrophysics Data System (ADS)

Uslu, Faruk Sukru

2017-07-01

Oil spills on the ocean surface cause serious environmental, political, and economic problems. Therefore, these catastrophic threats to marine ecosystems require detection and monitoring. Hyperspectral sensors are powerful optical sensors used for oil spill detection with the help of detailed spectral information of materials. However, huge amounts of data in hyperspectral imaging (HSI) require fast and accurate computation methods for detection problems. Support vector data description (SVDD) is one of the most suitable methods for detection, especially for large data sets. Nevertheless, the selection of kernel parameters is one of the main problems in SVDD. This paper presents a method, inspired by ensemble learning, for improving performance of SVDD without tuning its kernel parameters. Additionally, a classifier selection technique is proposed to get more gain. The proposed approach also aims to solve the small sample size problem, which is very important for processing high-dimensional data in HSI. The algorithm is applied to two HSI data sets for detection problems. In the first HSI data set, various targets are detected; in the second HSI data set, oil spill detection in situ is realized. The experimental results demonstrate the feasibility and performance improvement of the proposed algorithm for oil spill detection problems.

A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers.

PubMed

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-09-02

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner-Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference Sensors 2015, 15 22168 detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments.

A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner–Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments. PMID:26364637

Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Ishida, Kyoko; Sawada, Akira; Hatanaka, Yuji; Yamamoto, Tetsuya; Fujita, Hiroshi

2016-03-01

Early detection of glaucoma is important to slow down or cease progression of the disease and for preventing total blindness. We have previously proposed an automated scheme for detection of retinal nerve fiber layer defect (NFLD), which is one of the early signs of glaucoma observed on retinal fundus images. In this study, a new multi-step detection scheme was included to improve detection of subtle and narrow NFLDs. In addition, new features were added to distinguish between NFLDs and blood vessels, which are frequent sites of false positives (FPs). The result was evaluated with a new test dataset consisted of 261 cases, including 130 cases with NFLDs. Using the proposed method, the initial detection rate was improved from 82% to 98%. At the sensitivity of 80%, the number of FPs per image was reduced from 4.25 to 1.36. The result indicates the potential usefulness of the proposed method for early detection of glaucoma.

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

NASA Astrophysics Data System (ADS)

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

Low-complexity object detection with deep convolutional neural network for embedded systems

NASA Astrophysics Data System (ADS)

Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

2017-09-01

We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection.

PubMed

Lang, Congyan; Feng, Jiashi; Feng, Songhe; Wang, Jingdong; Yan, Shuicheng

2016-06-01

Saliency detection is an important procedure for machines to understand visual world as humans do. In this paper, we consider a specific saliency detection problem of predicting human eye fixations when they freely view natural images, and propose a novel dual low-rank pursuit (DLRP) method. DLRP learns saliency-aware feature transformations by utilizing available supervision information and constructs discriminative bases for effectively detecting human fixation points under the popular low-rank and sparsity-pursuit framework. Benefiting from the embedded high-level information in the supervised learning process, DLRP is able to predict fixations accurately without performing the expensive object segmentation as in the previous works. Comprehensive experiments clearly show the superiority of the proposed DLRP method over the established state-of-the-art methods. We also empirically demonstrate that DLRP provides stronger generalization performance across different data sets and inherits the advantages of both the bottom-up- and top-down-based saliency detection methods.

Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization

PubMed Central

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called “relative ratio symptom parameters” are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks. PMID:22163833

Intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization.

PubMed

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called "relative ratio symptom parameters" are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks.

Varying face occlusion detection and iterative recovery for face recognition

NASA Astrophysics Data System (ADS)

Wang, Meng; Hu, Zhengping; Sun, Zhe; Zhao, Shuhuan; Sun, Mei

2017-05-01

In most sparse representation methods for face recognition (FR), occlusion problems were usually solved via removing the occlusion part of both query samples and training samples to perform the recognition process. This practice ignores the global feature of facial image and may lead to unsatisfactory results due to the limitation of local features. Considering the aforementioned drawback, we propose a method called varying occlusion detection and iterative recovery for FR. The main contributions of our method are as follows: (1) to detect an accurate occlusion area of facial images, an image processing and intersection-based clustering combination method is used for occlusion FR; (2) according to an accurate occlusion map, the new integrated facial images are recovered iteratively and put into a recognition process; and (3) the effectiveness on recognition accuracy of our method is verified by comparing it with three typical occlusion map detection methods. Experiments show that the proposed method has a highly accurate detection and recovery performance and that it outperforms several similar state-of-the-art methods against partial contiguous occlusion.

Statistical methods to detect novel genetic variants using publicly available GWAS summary data.

PubMed

Guo, Bin; Wu, Baolin

2018-03-01

We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study. Copyright © 2018 Elsevier Ltd. All rights reserved.

Efficient airport detection using region-based fully convolutional neural networks

NASA Astrophysics Data System (ADS)

Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

2018-04-01

This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity.

PubMed

Napoletano, Paolo; Piccoli, Flavio; Schettini, Raimondo

2018-01-12

Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.

Frequency domain phase-shifted confocal microscopy (FDPCM) with array detection

NASA Astrophysics Data System (ADS)

Ge, Baoliang; Huang, Yujia; Fang, Yue; Kuang, Cuifang; Xiu, Peng; Liu, Xu

2017-09-01

We proposed a novel method to reconstruct images taken by array detected confocal microscopy without prior knowledge about its detector distribution. The proposed frequency domain phase-shifted confocal microscopy (FDPCM) shifts the image from each detection channel to its corresponding place by substituting the phase information in Fourier domain. Theoretical analysis shows that our method could approach the resolution nearly twofold of wide-field microscopy. Simulation and experiment results are also shown to verify the applicability and effectiveness of our method. Compared to Airyscan, our method holds the advantage of simplicity and convenience to be applied to array detectors with different structure, which makes FDPCM have great potential in the application of biomedical observation in the future.

Near-Infrared Spectrum Detection of Wheat Gluten Protein Content Based on a Combined Filtering Method.

PubMed

Cai, Jian-Hua

2017-09-01

To eliminate the random error of the derivative near-IR (NIR) spectrum and to improve model stability and the prediction accuracy of the gluten protein content, a combined method is proposed for pretreatment of the NIR spectrum based on both empirical mode decomposition and the wavelet soft-threshold method. The principle and the steps of the method are introduced and the denoising effect is evaluated. The wheat gluten protein content is calculated based on the denoised spectrum, and the results are compared with those of the nine-point smoothing method and the wavelet soft-threshold method. Experimental results show that the proposed combined method is effective in completing pretreatment of the NIR spectrum, and the proposed method improves the accuracy of detection of wheat gluten protein content from the NIR spectrum.

Location detection and tracking of moving targets by a 2D IR-UWB radar system.

PubMed

Nguyen, Van-Han; Pyun, Jae-Young

2015-03-19

In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.

A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles.

PubMed

Homaeinezhad, M R; Erfanianmoshiri-Nejad, M; Naseri, H

2014-01-01

The goal of this study is to introduce a simple, standard and safe procedure to detect and to delineate P and T waves of the electrocardiogram (ECG) signal in real conditions. The proposed method consists of four major steps: (1) a secure QRS detection and delineation algorithm, (2) a pattern recognition algorithm designed for distinguishing various ECG clusters which take place between consecutive R-waves, (3) extracting template of the dominant events of each cluster waveform and (4) application of the correlation analysis in order to delineate automatically the P- and T-waves in noisy conditions. The performance characteristics of the proposed P and T detection-delineation algorithm are evaluated versus various ECG signals whose qualities are altered from the best to the worst cases based on the random-walk noise theory. Also, the method is applied to the MIT-BIH Arrhythmia and the QT databases for comparing some parts of its performance characteristics with a number of P and T detection-delineation algorithms. The conducted evaluations indicate that in a signal with low quality value of about 0.6, the proposed method detects the P and T events with sensitivity Se=85% and positive predictive value of P+=89%, respectively. In addition, at the same quality, the average delineation errors associated with those ECG events are 45 and 63ms, respectively. Stable delineation error, high detection accuracy and high noise tolerance were the most important aspects considered during development of the proposed method. © 2013 Elsevier Ltd. All rights reserved.

A Method of Detections' Fusion for GNSS Anti-Spoofing.

PubMed

Tao, Huiqi; Li, Hong; Lu, Mingquan

2016-12-19

The spoofing attack is one of the security threats of systems depending on the Global Navigation Satellite System (GNSS). There have been many GNSS spoofing detection methods, and each of them focuses on a characteristic of the GNSS signal or a measurement that the receiver has obtained. The method based on a single detector is insufficient against spoofing attacks in some scenarios. How to fuse multiple detections together is a problem that concerns the performance of GNSS anti-spoofing. Scholars have put forward a model to fuse different detection results based on the Dempster-Shafer theory (DST) of evidence combination. However, there are some problems in the application. The main challenge is the valuation of the belief function, which is a key issue in DST. This paper proposes a practical method of detections' fusion based on an approach to assign the belief function for spoofing detections. The frame of discernment is simplified, and the hard decision of hypothesis testing is replaced by the soft decision; then, the belief functions for some detections can be evaluated. The method is discussed in detail, and a performance evaluation is provided, as well. Detections' fusion reduces false alarms of detection and makes the result more reliable. Experimental results based on public test datasets demonstrate the performance of the proposed method.

Landslide Inventory Mapping from Bitemporal 10 m SENTINEL-2 Images Using Change Detection Based Markov Random Field

NASA Astrophysics Data System (ADS)

Qin, Y.; Lu, P.; Li, Z.

2018-04-01

Landslide inventory mapping is essential for hazard assessment and mitigation. In most previous studies, landslide mapping was achieved by visual interpretation of aerial photos and remote sensing images. However, such method is labor-intensive and time-consuming, especially over large areas. Although a number of semi-automatic landslide mapping methods have been proposed over the past few years, limitations remain in terms of their applicability over different study areas and data, and there is large room for improvement in terms of the accuracy and automation degree. For these reasons, we developed a change detection-based Markov Random Field (CDMRF) method for landslide inventory mapping. The proposed method mainly includes two steps: 1) change detection-based multi-threshold for training samples generation and 2) MRF for landslide inventory mapping. Compared with the previous methods, the proposed method in this study has three advantages: 1) it combines multiple image difference techniques with multi-threshold method to generate reliable training samples; 2) it takes the spectral characteristics of landslides into account; and 3) it is highly automatic with little parameter tuning. The proposed method was applied for regional landslides mapping from 10 m Sentinel-2 images in Western China. Results corroborated the effectiveness and applicability of the proposed method especially the capability of rapid landslide mapping. Some directions for future research are offered. This study to our knowledge is the first attempt to map landslides from free and medium resolution satellite (i.e., Sentinel-2) images in China.

A consensus algorithm for approximate string matching and its application to QRS complex detection

NASA Astrophysics Data System (ADS)

Alba, Alfonso; Mendez, Martin O.; Rubio-Rincon, Miguel E.; Arce-Santana, Edgar R.

2016-08-01

In this paper, a novel algorithm for approximate string matching (ASM) is proposed. The novelty resides in the fact that, unlike most other methods, the proposed algorithm is not based on the Hamming or Levenshtein distances, but instead computes a score for each symbol in the search text based on a consensus measure. Those symbols with sufficiently high scores will likely correspond to approximate instances of the pattern string. To demonstrate the usefulness of the proposed method, it has been applied to the detection of QRS complexes in electrocardiographic signals with competitive results when compared against the classic Pan-Tompkins (PT) algorithm. The proposed method outperformed PT in 72% of the test cases, with no extra computational cost.

Detection method of visible and invisible nipples on digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Chae, Seung-Hoon; Jeong, Ji-Wook; Lee, Sooyeul; Chae, Eun Young; Kim, Hak Hee; Choi, Young-Wook

2015-03-01

Digital Breast Tomosynthesis(DBT) with 3D breast image can improve detection sensitivity of breast cancer more than 2D mammogram on dense breast. The nipple location information is needed to analyze DBT. The nipple location is invaluable information in registration and as a reference point for classifying mass or micro-calcification clusters. Since there are visible nipple and invisible nipple in 2D mammogram or DBT, the nipple detection of breast must be possible to detect visible and invisible nipple of breast. The detection method of visible nipple using shape information of nipple is simple and highly efficient. However, it is difficult to detect invisible nipple because it doesn't have prominent shape. Mammary glands in breast connect nipple, anatomically. The nipple location is detected through analyzing location of mammary glands in breast. In this paper, therefore, we propose a method to detect the nipple on a breast, which has a visible or invisible nipple using changes of breast area and mammary glands, respectively. The result shows that our proposed method has average error of 2.54+/-1.47mm.

A visual model for object detection based on active contours and level-set method.

PubMed

Satoh, Shunji

2006-09-01

A visual model for object detection is proposed. In order to make the detection ability comparable with existing technical methods for object detection, an evolution equation of neurons in the model is derived from the computational principle of active contours. The hierarchical structure of the model emerges naturally from the evolution equation. One drawback involved with initial values of active contours is alleviated by introducing and formulating convexity, which is a visual property. Numerical experiments show that the proposed model detects objects with complex topologies and that it is tolerant of noise. A visual attention model is introduced into the proposed model. Other simulations show that the visual properties of the model are consistent with the results of psychological experiments that disclose the relation between figure-ground reversal and visual attention. We also demonstrate that the model tends to perceive smaller regions as figures, which is a characteristic observed in human visual perception.

Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator

PubMed Central

Xu, Changhang; Xie, Jing; Zhang, Wuyang; Kong, Qingzhao; Chen, Guoming; Song, Gangbing

2017-01-01

Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner. PMID:29168759

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video.

PubMed

Ghosh, Tonmoy; Fattah, Shaikh Anowarul; Wahid, Khan A

2018-01-01

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

CRITERIA FOR EVALUATION OF PROPOSED PROTOZOAN DETECTION METHODS

EPA Science Inventory

There has been a proliferation of techniques and methods reported for analysis of water samples to determine the presence of the protozoan pathogens Cryptosporidium parvum and Giardia lamblia. Many of the proposed methods are presented as complete procedures, which include sampli...

CRITERIA FOR EVALUATION OF PROPOSED PROTOZOAN DETECTION METHODS.

EPA Science Inventory

There has been a proliferation of techniques and methods reported for analysis of water samples to determine the presence of the protozoan pathogens Cryptosporidium parvum and Giardia lamblia. Many of the proposed methods are presented as complete procedures, which include sampli...

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Detecting glaucomatous change in visual fields: Analysis with an optimization framework.

PubMed

Yousefi, Siamak; Goldbaum, Michael H; Varnousfaderani, Ehsan S; Belghith, Akram; Jung, Tzyy-Ping; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2015-12-01

Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants. Copyright © 2015 Elsevier Inc. All rights reserved.

A novel approach to describing and detecting performance anti-patterns

NASA Astrophysics Data System (ADS)

Sheng, Jinfang; Wang, Yihan; Hu, Peipei; Wang, Bin

2017-08-01

Anti-pattern, as an extension to pattern, describes a widely used poor solution which can bring negative influence to application systems. Aiming at the shortcomings of the existing anti-pattern descriptions, an anti-pattern description method based on first order predicate is proposed. This method synthesizes anti-pattern forms and symptoms, which makes the description more accurate and has good scalability and versatility as well. In order to improve the accuracy of anti-pattern detection, a Bayesian classification method is applied in validation for detection results, which can reduce false negatives and false positives of anti-pattern detection. Finally, the proposed approach in this paper is applied to a small e-commerce system, the feasibility and effectiveness of the approach is demonstrated further through experiments.

Face liveness detection for face recognition based on cardiac features of skin color image

NASA Astrophysics Data System (ADS)

Suh, Kun Ha; Lee, Eui Chul

2016-07-01

With the growth of biometric technology, spoofing attacks have been emerged a threat to the security of the system. Main spoofing scenarios in the face recognition system include the printing attack, replay attack, and 3D mask attack. To prevent such attacks, techniques that evaluating liveness of the biometric data can be considered as a solution. In this paper, a novel face liveness detection method based on cardiac signal extracted from face is presented. The key point of proposed method is that the cardiac characteristic is detected in live faces but not detected in non-live faces. Experimental results showed that the proposed method can be effective way for determining printing attack or 3D mask attack.

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT.

PubMed

Lopez-Martin, Manuel; Carro, Belen; Sanchez-Esguevillas, Antonio; Lloret, Jaime

2017-08-26

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

PubMed Central

Carro, Belen; Sanchez-Esguevillas, Antonio

2017-01-01

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. PMID:28846608

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits.

PubMed

Fu, Liya; Wang, You-Gan

2011-02-15

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which clearly demonstrates the advantages of the rank regression models.

Remote pedestrians detection at night time in FIR Image using contrast filtering and locally projected region based CNN

NASA Astrophysics Data System (ADS)

Kim, Taehwan; Kim, Sungho

2017-02-01

This paper presents a novel method to detect the remote pedestrians. After producing the human temperature based brightness enhancement image using the temperature data input, we generates the regions of interest (ROIs) by the multiscale contrast filtering based approach including the biased hysteresis threshold and clustering, remote pedestrian's height, pixel area and central position information. Afterwards, we conduct local vertical and horizontal projection based ROI refinement and weak aspect ratio based ROI limitation to solve the problem of region expansion in the contrast filtering stage. Finally, we detect the remote pedestrians by validating the final ROIs using transfer learning with convolutional neural network (CNN) feature, following non-maximal suppression (NMS) with strong aspect ratio limitation to improve the detection performance. In the experimental results, we confirmed that the proposed contrast filtering and locally projected region based CNN (CFLP-CNN) outperforms the baseline method by 8% in term of logaveraged miss rate. Also, the proposed method is more effective than the baseline approach and the proposed method provides the better regions that are suitably adjusted to the shape and appearance of remote pedestrians, which makes it detect the pedestrian that didn't find in the baseline approach and are able to help detect pedestrians by splitting the people group into a person.

Modeling and Detecting Feature Interactions among Integrated Services of Home Network Systems

NASA Astrophysics Data System (ADS)

Igaki, Hiroshi; Nakamura, Masahide

This paper presents a framework for formalizing and detecting feature interactions (FIs) in the emerging smart home domain. We first establish a model of home network system (HNS), where every networked appliance (or the HNS environment) is characterized as an object consisting of properties and methods. Then, every HNS service is defined as a sequence of method invocations of the appliances. Within the model, we next formalize two kinds of FIs: (a) appliance interactions and (b) environment interactions. An appliance interaction occurs when two method invocations conflict on the same appliance, whereas an environment interaction arises when two method invocations conflict indirectly via the environment. Finally, we propose offline and online methods that detect FIs before service deployment and during execution, respectively. Through a case study with seven practical services, it is shown that the proposed framework is generic enough to capture feature interactions in HNS integrated services. We also discuss several FI resolution schemes within the proposed framework.

SWT voting-based color reduction for text detection in natural scene images

NASA Astrophysics Data System (ADS)

Ikica, Andrej; Peer, Peter

2013-12-01

In this article, we propose a novel stroke width transform (SWT) voting-based color reduction method for detecting text in natural scene images. Unlike other text detection approaches that mostly rely on either text structure or color, the proposed method combines both by supervising text-oriented color reduction process with additional SWT information. SWT pixels mapped to color space vote in favor of the color they correspond to. Colors receiving high SWT vote most likely belong to text areas and are blocked from being mean-shifted away. Literature does not explicitly address SWT search direction issue; thus, we propose an adaptive sub-block method for determining correct SWT direction. Both SWT voting-based color reduction and SWT direction determination methods are evaluated on binary (text/non-text) images obtained from a challenging Computer Vision Lab optical character recognition database. SWT voting-based color reduction method outperforms the state-of-the-art text-oriented color reduction approach.

Sensing Methods for Detecting Analog Television Signals

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Azizur; Song, Chunyi; Harada, Hiroshi

This paper introduces a unified method of spectrum sensing for all existing analog television (TV) signals including NTSC, PAL and SECAM. We propose a correlation based method (CBM) with a single reference signal for sensing any analog TV signals. In addition we also propose an improved energy detection method. The CBM approach has been implemented in a hardware prototype specially designed for participating in Singapore TV white space (WS) test trial conducted by Infocomm Development Authority (IDA) of the Singapore government. Analytical and simulation results of the CBM method will be presented in the paper, as well as hardware testing results for sensing various analog TV signals. Both AWGN and fading channels will be considered. It is shown that the theoretical results closely match with those from simulations. Sensing performance of the hardware prototype will also be presented in fading environment by using a fading simulator. We present performance of the proposed techniques in terms of probability of false alarm, probability of detection, sensing time etc. We also present a comparative study of the various techniques.

A pdf-Free Change Detection Test Based on Density Difference Estimation.

PubMed

Bu, Li; Alippi, Cesare; Zhao, Dongbin

2018-02-01

The ability to detect online changes in stationarity or time variance in a data stream is a hot research topic with striking implications. In this paper, we propose a novel probability density function-free change detection test, which is based on the least squares density-difference estimation method and operates online on multidimensional inputs. The test does not require any assumption about the underlying data distribution, and is able to operate immediately after having been configured by adopting a reservoir sampling mechanism. Thresholds requested to detect a change are automatically derived once a false positive rate is set by the application designer. Comprehensive experiments validate the effectiveness in detection of the proposed method both in terms of detection promptness and accuracy.

Automatic small target detection in synthetic infrared images

NASA Astrophysics Data System (ADS)

Yardımcı, Ozan; Ulusoy, Ä.°lkay

2017-05-01

Automatic detection of targets from far distances is a very challenging problem. Background clutter and small target size are the main difficulties which should be solved while reaching a high detection performance as well as a low computational load. The pre-processing, detection and post-processing approaches are very effective on the final results. In this study, first of all, various methods in the literature were evaluated separately for each of these stages using the simulated test scenarios. Then, a full system of detection was constructed among available solutions which resulted in the best performance in terms of detection. However, although a precision rate as 100% was reached, the recall values stayed low around 25-45%. Finally, a post-processing method was proposed which increased the recall value while keeping the precision at 100%. The proposed post-processing method, which is based on local operations, increased the recall value to 65-95% in all test scenarios.

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.

A novel, colorimetric method for biogenic amine detection based on arylalkylamine N-acetyltransferase.

PubMed

Leng, Pei-Qiang; Zhao, Feng-Lan; Yin, Bin-Cheng; Ye, Bang-Ce

2015-05-21

We developed a novel colorimetric method for rapid detection of biogenic amines based on arylalkylamine N-acetyltransferase (aaNAT). The proposed method offers distinct advantages including simple handling, high speed, low cost, good sensitivity and selectivity.

Automatic detection of Martian dark slope streaks by machine learning using HiRISE images

NASA Astrophysics Data System (ADS)

Wang, Yexin; Di, Kaichang; Xin, Xin; Wan, Wenhui

2017-07-01

Dark slope streaks (DSSs) on the Martian surface are one of the active geologic features that can be observed on Mars nowadays. The detection of DSS is a prerequisite for studying its appearance, morphology, and distribution to reveal its underlying geological mechanisms. In addition, increasingly massive amounts of Mars high resolution data are now available. Hence, an automatic detection method for locating DSSs is highly desirable. In this research, we present an automatic DSS detection method by combining interest region extraction and machine learning techniques. The interest region extraction combines gradient and regional grayscale information. Moreover, a novel recognition strategy is proposed that takes the normalized minimum bounding rectangles (MBRs) of the extracted regions to calculate the Local Binary Pattern (LBP) feature and train a DSS classifier using the Adaboost machine learning algorithm. Comparative experiments using five different feature descriptors and three different machine learning algorithms show the superiority of the proposed method. Experimental results utilizing 888 extracted region samples from 28 HiRISE images show that the overall detection accuracy of our proposed method is 92.4%, with a true positive rate of 79.1% and false positive rate of 3.7%, which in particular indicates great performance of the method at eliminating non-DSS regions.

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

PubMed Central

Guvensan, M. Amac; Kansiz, A. Oguz; Camgoz, N. Cihan; Turkmen, H. Irem; Yavuz, A. Gokhan; Karsligil, M. Elif

2017-01-01

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions. PMID:28644378

Quench Detection and Protection for High Temperature Superconducting Transformers by Using the Active Power Method

NASA Astrophysics Data System (ADS)

Nanato, N.; Kobayashi, Y.

AC high temperature superconducting (HTS) coils have been developed for transformers, motors and so on. Quench detection and protection system are essential for safety operations of the AC HTS facilities. The balance voltage method is universally used for the quench detection and protection, however especially for AC operations, the method has risks in terms of high voltage sparks. Because the method needs a voltage tap soldered to a midpoint of the coil winding and the AC HTS facilities generally operate at high voltages and therefore high voltage sparks may occur at the midpoint with no insulation. We have proposed the active power method for the quench detection and protection. The method requires no voltage tap on the midpoint of the coil winding and therefore it has in-built effectiveness for the AC HTS facilities. In this paper, we show that the method can detect the quench in an HTS transformer and moreover our proposed quench protection circuits which consist of thyristors are simple and useful for the AC HTS facilities.

Integrated software for the detection of epileptogenic zones in refractory epilepsy.

PubMed

Mottini, Alejandro; Miceli, Franco; Albin, Germán; Nuñez, Margarita; Ferrándo, Rodolfo; Aguerrebere, Cecilia; Fernandez, Alicia

2010-01-01

In this paper we present an integrated software designed to help nuclear medicine physicians in the detection of epileptogenic zones (EZ) by means of ictal-interictal SPECT and MR images. This tool was designed to be flexible, friendly and efficient. A novel detection method was included (A-contrario) along with the classical detection method (Subtraction analysis). The software's performance was evaluated with two separate sets of validation studies: visual interpretation of 12 patient images by an experimented observer and objective analysis of virtual brain phantom experiments by proposed numerical observers. Our results support the potential use of the proposed software to help nuclear medicine physicians in the detection of EZ in clinical practice.

Displacement Vector Measurement Using 2D Modulation by Virtual Hyperbolic Beam Forming

NASA Astrophysics Data System (ADS)

Kondo, Kengo; Yamakawa, Makoto; Shiina, Tsuyoshi

For the purpose of diagnosing ischemic heart disease by detection of malfunction area and cancer tumor by detection of hard area, 3-D tissue motion must be correctly evaluated. So far various methods of measuring multidimensional displacement have been developed. Most of present techniques are restricted to one-dimensional measurement of tissue displacement such as myocardial stain-rate imaging. Although lateral modulation method enables us to attain high-accuracy measurement of lateral displacement as well as axial direction by generating lateral oscillating RF signals, the method causes distorted RF far from center of aperture. As a result, the method is not suited to sector scan which is used for myocardial examination. We propose a method to solve the problem by using 2-D modulation with the virtual hyperbolic beam forming and detection of 2-D displacement vector. The feasibilities of the proposed method were evaluated by numerically simulating the left ventricle short-axis imaging of cylindrical myocardial model. The volume strain image obtained by the proposed method clearly depicted the hard infarction area where conventional multi-beam Doppler imaging could not.

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

PubMed

He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

2012-06-25

Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

Influence analysis in quantitative trait loci detection.

PubMed

Dou, Xiaoling; Kuriki, Satoshi; Maeno, Akiteru; Takada, Toyoyuki; Shiroishi, Toshihiko

2014-07-01

This paper presents systematic methods for the detection of influential individuals that affect the log odds (LOD) score curve. We derive general formulas of influence functions for profile likelihoods and introduce them into two standard quantitative trait locus detection methods-the interval mapping method and single marker analysis. Besides influence analysis on specific LOD scores, we also develop influence analysis methods on the shape of the LOD score curves. A simulation-based method is proposed to assess the significance of the influence of the individuals. These methods are shown useful in the influence analysis of a real dataset of an experimental population from an F2 mouse cross. By receiver operating characteristic analysis, we confirm that the proposed methods show better performance than existing diagnostics. © 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Detection of glucose in the human brain with 1 H MRS at 7 Tesla.

PubMed

Kaiser, Lana G; Hirokazu, Kawaguchi; Fukunaga, Masaki; B Matson, Gerald

2016-12-01

A new method is proposed for noninvasive detection of glucose in vivo using proton MR spectroscopy at 7 Tesla. The proposed method utilizes J-difference editing to uncover the resonance of beta-glucose (β-glc) at 3.23 ppm, which is strongly overlapped with choline. Calculations using the density matrix formalism are used to maximize the signal-to-noise ratio of the β-glc resonance at 3.23 ppm. The calculations are verified using phantom and in vivo data collected at 7 Tesla. The proposed method allows observation of the glucose signal at 3.23 ppm in the human brain spectrum. Additional co-edited resonances of N-acetylaspartylglutamatate and glutathione are also detected in the same experiment. The proposed method does not require carbon ( 13 C)- labeled glucose injections and 13 C hardware; as such, it has a potential to provide valuable information on intrinsic glucose concentration in the human brain in vivo. Magn Reson Med 76:1653-1660, 2016. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Real-time biscuit tile image segmentation method based on edge detection.

PubMed

Matić, Tomislav; Aleksi, Ivan; Hocenski, Željko; Kraus, Dieter

2018-05-01

In this paper we propose a novel real-time Biscuit Tile Segmentation (BTS) method for images from ceramic tile production line. BTS method is based on signal change detection and contour tracing with a main goal of separating tile pixels from background in images captured on the production line. Usually, human operators are visually inspecting and classifying produced ceramic tiles. Computer vision and image processing techniques can automate visual inspection process if they fulfill real-time requirements. Important step in this process is a real-time tile pixels segmentation. BTS method is implemented for parallel execution on a GPU device to satisfy the real-time constraints of tile production line. BTS method outperforms 2D threshold-based methods, 1D edge detection methods and contour-based methods. Proposed BTS method is in use in the biscuit tile production line. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

PubMed

Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

An Optimized Method to Detect BDS Satellites' Orbit Maneuvering and Anomalies in Real-Time.

PubMed

Huang, Guanwen; Qin, Zhiwei; Zhang, Qin; Wang, Le; Yan, Xingyuan; Wang, Xiaolei

2018-02-28

The orbital maneuvers of Global Navigation Satellite System (GNSS) Constellations will decrease the performance and accuracy of positioning, navigation, and timing (PNT). Because satellites in the Chinese BeiDou Navigation Satellite System (BDS) are in Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO), maneuvers occur more frequently. Also, the precise start moment of the BDS satellites' orbit maneuvering cannot be obtained by common users. This paper presented an improved real-time detecting method for BDS satellites' orbit maneuvering and anomalies with higher timeliness and higher accuracy. The main contributions to this improvement are as follows: (1) instead of the previous two-steps method, a new one-step method with higher accuracy is proposed to determine the start moment and the pseudo random noise code (PRN) of the satellite orbit maneuvering in that time; (2) BDS Medium Earth Orbit (MEO) orbital maneuvers are firstly detected according to the proposed selection strategy for the stations; and (3) the classified non-maneuvering anomalies are detected by a new median robust method using the weak anomaly detection factor and the strong anomaly detection factor. The data from the Multi-GNSS Experiment (MGEX) in 2017 was used for experimental analysis. The experimental results and analysis showed that the start moment of orbital maneuvers and the period of non-maneuver anomalies can be determined more accurately in real-time. When orbital maneuvers and anomalies occur, the proposed method improved the data utilization for 91 and 95 min in 2017.

An Optimized Method to Detect BDS Satellites’ Orbit Maneuvering and Anomalies in Real-Time

PubMed Central

Huang, Guanwen; Qin, Zhiwei; Zhang, Qin; Wang, Le; Yan, Xingyuan; Wang, Xiaolei

2018-01-01

The orbital maneuvers of Global Navigation Satellite System (GNSS) Constellations will decrease the performance and accuracy of positioning, navigation, and timing (PNT). Because satellites in the Chinese BeiDou Navigation Satellite System (BDS) are in Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO), maneuvers occur more frequently. Also, the precise start moment of the BDS satellites’ orbit maneuvering cannot be obtained by common users. This paper presented an improved real-time detecting method for BDS satellites’ orbit maneuvering and anomalies with higher timeliness and higher accuracy. The main contributions to this improvement are as follows: (1) instead of the previous two-steps method, a new one-step method with higher accuracy is proposed to determine the start moment and the pseudo random noise code (PRN) of the satellite orbit maneuvering in that time; (2) BDS Medium Earth Orbit (MEO) orbital maneuvers are firstly detected according to the proposed selection strategy for the stations; and (3) the classified non-maneuvering anomalies are detected by a new median robust method using the weak anomaly detection factor and the strong anomaly detection factor. The data from the Multi-GNSS Experiment (MGEX) in 2017 was used for experimental analysis. The experimental results and analysis showed that the start moment of orbital maneuvers and the period of non-maneuver anomalies can be determined more accurately in real-time. When orbital maneuvers and anomalies occur, the proposed method improved the data utilization for 91 and 95 min in 2017. PMID:29495638

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

PubMed

Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

2014-08-01

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature discrepancies and prove advantageous not only at the candidate detection level, but also at subsequent steps of a CAD system.

Region-based automatic building and forest change detection on Cartosat-1 stereo imagery

NASA Astrophysics Data System (ADS)

Tian, J.; Reinartz, P.; d'Angelo, P.; Ehlers, M.

2013-05-01

In this paper a novel region-based method is proposed for change detection using space borne panchromatic Cartosat-1 stereo imagery. In the first step, Digital Surface Models (DSMs) from two dates are generated by semi-global matching. The geometric lateral resolution of the DSMs is 5 m × 5 m and the height accuracy is in the range of approximately 3 m (RMSE). In the second step, mean-shift segmentation is applied on the orthorectified images of two dates to obtain initial regions. A region intersection following a merging strategy is proposed to get minimum change regions and multi-level change vectors are extracted for these regions. Finally change detection is achieved by combining these features with weighted change vector analysis. The result evaluations demonstrate that the applied DSM generation method is well suited for Cartosat-1 imagery, and the extracted height values can largely improve the change detection accuracy, moreover it is shown that the proposed change detection method can be used robustly for both forest and industrial areas.

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.

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

PubMed Central

Wang, Zhenzhu; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm. PMID:28421125

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning.

PubMed

Zhou, Wei; Wu, Chengdong; Chen, Dali; Wang, Zhenzhu; Yi, Yugen; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

PubMed

Ganapathy, S; Yogesh, P; Kannan, A

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

PubMed Central

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

NASA Astrophysics Data System (ADS)

Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

2015-04-01

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

Walsh-Hadamard transform kernel-based feature vector for shot boundary detection.

PubMed

Lakshmi, Priya G G; Domnic, S

2014-12-01

Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method.

Meal Detection in Patients With Type 1 Diabetes: A New Module for the Multivariable Adaptive Artificial Pancreas Control System.

PubMed

Turksoy, Kamuran; Samadi, Sediqeh; Feng, Jianyuan; Littlejohn, Elizabeth; Quinn, Laurie; Cinar, Ali

2016-01-01

A novel meal-detection algorithm is developed based on continuous glucose measurements. Bergman's minimal model is modified and used in an unscented Kalman filter for state estimations. The estimated rate of appearance of glucose is used for meal detection. Data from nine subjects are used to assess the performance of the algorithm. The results indicate that the proposed algorithm works successfully with high accuracy. The average change in glucose levels between the meals and the detection points is 16(±9.42) [mg/dl] for 61 successfully detected meals and snacks. The algorithm is developed as a new module of an integrated multivariable adaptive artificial pancreas control system. Meal detection with the proposed method is used to administer insulin boluses and prevent most of postprandial hyperglycemia without any manual meal announcements. A novel meal bolus calculation method is proposed and tested with the UVA/Padova simulator. The results indicate significant reduction in hyperglycemia.

Pathological brain detection based on wavelet entropy and Hu moment invariants.

PubMed

Zhang, Yudong; Wang, Shuihua; Sun, Ping; Phillips, Preetha

2015-01-01

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

Improvement of retinal blood vessel detection by spur removal and Gaussian matched filtering compensation

NASA Astrophysics Data System (ADS)

Xiao, Di; Vignarajan, Janardhan; An, Dong; Tay-Kearney, Mei-Ling; Kanagasingam, Yogi

2016-03-01

Retinal photography is a non-invasive and well-accepted clinical diagnosis of ocular diseases. Qualitative and quantitative assessment of retinal images is crucial in ocular diseases related clinical application. In this paper, we proposed approaches for improving the quality of blood vessel detection based on our initial blood vessel detection methods. A blood vessel spur pruning method has been developed for removing the blood vessel spurs both on vessel medial lines and binary vessel masks, which are caused by artifacts and side-effect of Gaussian matched vessel enhancement. A Gaussian matched filtering compensation method has been developed for removing incorrect vessel branches in the areas of low illumination. The proposed approaches were applied and tested on the color fundus images from one publicly available database and our diabetic retinopathy screening dataset. A preliminary result has demonstrated the robustness and good performance of the proposed approaches and their potential application for improving retinal blood vessel detection.

Urban Area Detection in Very High Resolution Remote Sensing Images Using Deep Convolutional Neural Networks.

PubMed

Tian, Tian; Li, Chang; Xu, Jinkang; Ma, Jiayi

2018-03-18

Detecting urban areas from very high resolution (VHR) remote sensing images plays an important role in the field of Earth observation. The recently-developed deep convolutional neural networks (DCNNs), which can extract rich features from training data automatically, have achieved outstanding performance on many image classification databases. Motivated by this fact, we propose a new urban area detection method based on DCNNs in this paper. The proposed method mainly includes three steps: (i) a visual dictionary is obtained based on the deep features extracted by pre-trained DCNNs; (ii) urban words are learned from labeled images; (iii) the urban regions are detected in a new image based on the nearest dictionary word criterion. The qualitative and quantitative experiments on different datasets demonstrate that the proposed method can obtain a remarkable overall accuracy (OA) and kappa coefficient. Moreover, it can also strike a good balance between the true positive rate (TPR) and false positive rate (FPR).

Detecting Payload Attacks on Programmable Logic Controllers (PLCs)

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

Yang, Huan

Programmable logic controllers (PLCs) play critical roles in industrial control systems (ICS). Providing hardware peripherals and firmware support for control programs (i.e., a PLC’s “payload”) written in languages such as ladder logic, PLCs directly receive sensor readings and control ICS physical processes. An attacker with access to PLC development software (e.g., by compromising an engineering workstation) can modify the payload program and cause severe physical damages to the ICS. To protect critical ICS infrastructure, we propose to model runtime behaviors of legitimate PLC payload program and use runtime behavior monitoring in PLC firmware to detect payload attacks. By monitoring themore » I/O access patterns, network access patterns, as well as payload program timing characteristics, our proposed firmware-level detection mechanism can detect abnormal runtime behaviors of malicious PLC payload. Using our proof-of-concept implementation, we evaluate the memory and execution time overhead of implementing our proposed method and find that it is feasible to incorporate our method into existing PLC firmware. In addition, our evaluation results show that a wide variety of payload attacks can be effectively detected by our proposed approach. The proposed firmware-level payload attack detection scheme complements existing bumpin- the-wire solutions (e.g., external temporal-logic-based model checkers) in that it can detect payload attacks that violate realtime requirements of ICS operations and does not require any additional apparatus.« less

Detecting event-related changes in organizational networks using optimized neural network models.

PubMed

Li, Ze; Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques.

Detecting event-related changes in organizational networks using optimized neural network models

PubMed Central

Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques. PMID:29190799

Optical detection of metastatic cancer cells using a scanned laser pico-projection system

NASA Astrophysics Data System (ADS)

Huang, Chih-Ling; Chiu, Wen-Tai; Lo, Yu-Lung; Chuang, Chin-Ho; Chen, Yu-Bin; Chang, Shu-Jing; Ke, Tung-Ting; Cheng, Hung-Chi; Wu, Hua-Lin

2015-03-01

Metastasis is responsible for 90% of all cancer-related deaths in humans. As a result, reliable techniques for detecting metastatic cells are urgently required. Although various techniques have been proposed for metastasis detection, they are generally capable of detecting metastatic cells only once migration has already occurred. Accordingly, the present study proposes an optical method for physical characterization of metastatic cancer cells using a scanned laser pico-projection system (SLPP). The validity of the proposed method is demonstrated using five pairs of cancer cell lines and two pairs of non-cancer cell lines treated by IPTG induction in order to mimic normal cells with an overexpression of oncogene. The results show that for all of the considered cell lines, the SLPP speckle contrast of the high-metastatic cells is significantly higher than that of the low-metastatic cells. As a result, the speckle contrast measurement provides a reliable means of distinguishing quantitatively between low- and high-metastatic cells of the same origin. Compared to existing metastasis detection methods, the proposed SLPP approach has many advantages, including a higher throughput, a lower cost, a larger sample size and a more reliable diagnostic performance. As a result, it provides a highly promising solution for physical characterization of metastatic cancer cells in vitro.

Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

NASA Astrophysics Data System (ADS)

Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang

2017-10-01

This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

A new method for spatial structure detection of complex inner cavities based on 3D γ-photon imaging

NASA Astrophysics Data System (ADS)

Xiao, Hui; Zhao, Min; Liu, Jiantang; Liu, Jiao; Chen, Hao

2018-05-01

This paper presents a new three-dimensional (3D) imaging method for detecting the spatial structure of a complex inner cavity based on positron annihilation and γ-photon detection. This method first marks carrier solution by a certain radionuclide and injects it into the inner cavity where positrons are generated. Subsequently, γ-photons are released from positron annihilation, and the γ-photon detector ring is used for recording the γ-photons. Finally, the two-dimensional (2D) image slices of the inner cavity are constructed by the ordered-subset expectation maximization scheme and the 2D image slices are merged to the 3D image of the inner cavity. To eliminate the artifact in the reconstructed image due to the scattered γ-photons, a novel angle-traversal model is proposed for γ-photon single-scattering correction, in which the path of the single scattered γ-photon is analyzed from a spatial geometry perspective. Two experiments are conducted to verify the effectiveness of the proposed correction model and the advantage of the proposed testing method in detecting the spatial structure of the inner cavity, including the distribution of gas-liquid multi-phase mixture inside the inner cavity. The above two experiments indicate the potential of the proposed method as a new tool for accurately delineating the inner structures of industrial complex parts.

Robust curb detection with fusion of 3D-Lidar and camera data.

PubMed

Tan, Jun; Li, Jian; An, Xiangjing; He, Hangen

2014-05-21

Curb detection is an essential component of Autonomous Land Vehicles (ALV), especially important for safe driving in urban environments. In this paper, we propose a fusion-based curb detection method through exploiting 3D-Lidar and camera data. More specifically, we first fuse the sparse 3D-Lidar points and high-resolution camera images together to recover a dense depth image of the captured scene. Based on the recovered dense depth image, we propose a filter-based method to estimate the normal direction within the image. Then, by using the multi-scale normal patterns based on the curb's geometric property, curb point features fitting the patterns are detected in the normal image row by row. After that, we construct a Markov Chain to model the consistency of curb points which utilizes the continuous property of the curb, and thus the optimal curb path which links the curb points together can be efficiently estimated by dynamic programming. Finally, we perform post-processing operations to filter the outliers, parameterize the curbs and give the confidence scores on the detected curbs. Extensive evaluations clearly show that our proposed method can detect curbs with strong robustness at real-time speed for both static and dynamic scenes.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

PubMed Central

Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

2015-01-01

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858

High accuracy position method based on computer vision and error analysis

NASA Astrophysics Data System (ADS)

Chen, Shihao; Shi, Zhongke

2003-09-01

The study of high accuracy position system is becoming the hotspot in the field of autocontrol. And positioning is one of the most researched tasks in vision system. So we decide to solve the object locating by using the image processing method. This paper describes a new method of high accuracy positioning method through vision system. In the proposed method, an edge-detection filter is designed for a certain running condition. Here, the filter contains two mainly parts: one is image-processing module, this module is to implement edge detection, it contains of multi-level threshold self-adapting segmentation, edge-detection and edge filter; the other one is object-locating module, it is to point out the location of each object in high accurate, and it is made up of medium-filtering and curve-fitting. This paper gives some analysis error for the method to prove the feasibility of vision in position detecting. Finally, to verify the availability of the method, an example of positioning worktable, which is using the proposed method, is given at the end of the paper. Results show that the method can accurately detect the position of measured object and identify object attitude.

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.

Modulation-format-free and automatic bias control for optical IQ modulators based on dither-correlation detection.

PubMed

Li, Xiaolei; Deng, Lei; Chen, Xiaoman; Cheng, Mengfan; Fu, Songnian; Tang, Ming; Liu, Deming

2017-04-17

A novel automatic bias control (ABC) method for optical in-phase and quadrature (IQ) modulator is proposed and experimentally demonstrated. In the proposed method, two different low frequency sine wave dither signals are generated and added on to the I/Q bias signal respectively. Instead of power monitoring of the harmonics of the dither signal, dither-correlation detection is proposed and used to adjust the bias voltages of the optical IQ modulator. By this way, not only frequency spectral analysis isn't required but also the directional bias adjustment could be realized, resulting in the decrease of algorithm complexity and the growth of convergence rate of ABC algorithm. The results show that the sensitivity of the proposed ABC method outperforms that of the traditional dither frequency monitoring method. Moreover, the proposed ABC method is proved to be modulation-format-free, and the transmission penalty caused by this method for both 10 Gb/s optical QPSK and 17.9 Gb/s optical 16QAM-OFDM signal transmission are negligible in our experiment.

Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection

PubMed Central

Su, Hai; Xing, Fuyong; Yang, Lin

2016-01-01

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96. PMID:26812706

Research on vehicle detection based on background feature analysis in SAR images

NASA Astrophysics Data System (ADS)

Zhang, Bochuan; Tang, Bo; Zhang, Cong; Hu, Ruiguang; Yun, Hongquan; Xiao, Liping

2017-10-01

Aiming at vehicle detection on the ground through low resolution SAR images, a method is proposed for determining the region of the vehicles first and then detecting the target in the specific region. The experimental results show that this method not only reduces the target detection area, but also reduces the influence of terrain clutter on the detection, which greatly improves the reliability of the target detection.

Fast microcalcification detection in ultrasound images using image enhancement and threshold adjacency statistics

NASA Astrophysics Data System (ADS)

Cho, Baek Hwan; Chang, Chuho; Lee, Jong-Ha; Ko, Eun Young; Seong, Yeong Kyeong; Woo, Kyoung-Gu

2013-02-01

The existence of microcalcifications (MCs) is an important marker of malignancy in breast cancer. In spite of the benefits in mass detection for dense breasts, ultrasonography is believed that it might not reliably detect MCs. For computer aided diagnosis systems, however, accurate detection of MCs has the possibility of improving the performance in both Breast Imaging-Reporting and Data System (BI-RADS) lexicon description for calcifications and malignancy classification. We propose a new efficient and effective method for MC detection using image enhancement and threshold adjacency statistics (TAS). The main idea of TAS is to threshold an image and to count the number of white pixels with a given number of adjacent white pixels. Our contribution is to adopt TAS features and apply image enhancement to facilitate MC detection in ultrasound images. We employed fuzzy logic, tophat filter, and texture filter to enhance images for MCs. Using a total of 591 images, the classification accuracy of the proposed method in MC detection showed 82.75%, which is comparable to that of Haralick texture features (81.38%). When combined, the performance was as high as 85.11%. In addition, our method also showed the ability in mass classification when combined with existing features. In conclusion, the proposed method exploiting image enhancement and TAS features has the potential to deal with MC detection in ultrasound images efficiently and extend to the real-time localization and visualization of MCs.

A non-reference evaluation method for edge detection of wear particles in ferrograph images

NASA Astrophysics Data System (ADS)

Wang, Jingqiu; Bi, Ju; Wang, Lianjun; Wang, Xiaolei

2018-02-01

Edges are one of the most important features of wear particles in a ferrograph image and are widely used to extract parameters, recognize types of wear particles, and assist in the identification of the wear mode and severity. Edge detection is a critical step in ferrograph image processing and analysis. Till date, there has been no single algorithm that guarantees the production of good quality edges in ferrograph images for a variety of applications. Therefore, it is desirable to have a reliable evaluation method for measuring the performance of various edge detection algorithms and for aiding in the selection of the optimal parameter and algorithm for ferrographic applications. In this paper, a new non-reference method for the objective evaluation of wear particle edge detection is proposed. In this method, a comprehensive index of edge evaluation is composed of three components, i.e., the reconstruction based similarity sub-index between the original image and the reconstructed image, the confidence degree sub-index used to show the true or false degree of the edge pixels, and the edge form sub-index that is used to determine the direction consistency and width uniformity of the edges. Two experiments are performed to illustrate the validity of the proposed method. First, this method is used to select the best parameters for an edge detection algorithm, and it is then used to compare the results obtained using various edge detection algorithms and determine the best algorithm. Experimental results of various real ferrograph images verify the effectiveness of the proposed method.

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

PubMed Central

Schettini, Raimondo

2018-01-01

Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art. PMID:29329268

Improved and Robust Detection of Cell Nuclei from Four Dimensional Fluorescence Images

PubMed Central

Bashar, Md. Khayrul; Yamagata, Kazuo; Kobayashi, Tetsuya J.

2014-01-01

Segmentation-free direct methods are quite efficient for automated nuclei extraction from high dimensional images. A few such methods do exist but most of them do not ensure algorithmic robustness to parameter and noise variations. In this research, we propose a method based on multiscale adaptive filtering for efficient and robust detection of nuclei centroids from four dimensional (4D) fluorescence images. A temporal feedback mechanism is employed between the enhancement and the initial detection steps of a typical direct method. We estimate the minimum and maximum nuclei diameters from the previous frame and feed back them as filter lengths for multiscale enhancement of the current frame. A radial intensity-gradient function is optimized at positions of initial centroids to estimate all nuclei diameters. This procedure continues for processing subsequent images in the sequence. Above mechanism thus ensures proper enhancement by automated estimation of major parameters. This brings robustness and safeguards the system against additive noises and effects from wrong parameters. Later, the method and its single-scale variant are simplified for further reduction of parameters. The proposed method is then extended for nuclei volume segmentation. The same optimization technique is applied to final centroid positions of the enhanced image and the estimated diameters are projected onto the binary candidate regions to segment nuclei volumes.Our method is finally integrated with a simple sequential tracking approach to establish nuclear trajectories in the 4D space. Experimental evaluations with five image-sequences (each having 271 3D sequential images) corresponding to five different mouse embryos show promising performances of our methods in terms of nuclear detection, segmentation, and tracking. A detail analysis with a sub-sequence of 101 3D images from an embryo reveals that the proposed method can improve the nuclei detection accuracy by 9 over the previous methods, which used inappropriate large valued parameters. Results also confirm that the proposed method and its variants achieve high detection accuracies ( 98 mean F-measure) irrespective of the large variations of filter parameters and noise levels. PMID:25020042

Brain tumor segmentation with Vander Lugt correlator based active contour.

PubMed

Essadike, Abdelaziz; Ouabida, Elhoussaine; Bouzid, Abdenbi

2018-07-01

The manual segmentation of brain tumors from medical images is an error-prone, sensitive, and time-absorbing process. This paper presents an automatic and fast method of brain tumor segmentation. In the proposed method, a numerical simulation of the optical Vander Lugt correlator is used for automatically detecting the abnormal tissue region. The tumor filter, used in the simulated optical correlation, is tailored to all the brain tumor types and especially to the Glioblastoma, which considered to be the most aggressive cancer. The simulated optical correlation, computed between Magnetic Resonance Images (MRI) and this filter, estimates precisely and automatically the initial contour inside the tumorous tissue. Further, in the segmentation part, the detected initial contour is used to define an active contour model and presenting the problematic as an energy minimization problem. As a result, this initial contour assists the algorithm to evolve an active contour model towards the exact tumor boundaries. Equally important, for a comparison purposes, we considered different active contour models and investigated their impact on the performance of the segmentation task. Several images from BRATS database with tumors anywhere in images and having different sizes, contrast, and shape, are used to test the proposed system. Furthermore, several performance metrics are computed to present an aggregate overview of the proposed method advantages. The proposed method achieves a high accuracy in detecting the tumorous tissue by a parameter returned by the simulated optical correlation. In addition, the proposed method yields better performance compared to the active contour based methods with the averages of Sensitivity=0.9733, Dice coefficient = 0.9663, Hausdroff distance = 2.6540, Specificity = 0.9994, and faster with a computational time average of 0.4119 s per image. Results reported on BRATS database reveal that our proposed system improves over the recently published state-of-the-art methods in brain tumor detection and segmentation. Copyright © 2018 Elsevier B.V. All rights reserved.

4D numerical observer for lesion detection in respiratory-gated PET

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

Lorsakul, Auranuch; Li, Quanzheng; Ouyang, Jinsong

2014-10-15

Purpose: Respiratory-gated positron emission tomography (PET)/computed tomography protocols reduce lesion smearing and improve lesion detection through a synchronized acquisition of emission data. However, an objective assessment of image quality of the improvement gained from respiratory-gated PET is mainly limited to a three-dimensional (3D) approach. This work proposes a 4D numerical observer that incorporates both spatial and temporal informations for detection tasks in pulmonary oncology. Methods: The authors propose a 4D numerical observer constructed with a 3D channelized Hotelling observer for the spatial domain followed by a Hotelling observer for the temporal domain. Realistic {sup 18}F-fluorodeoxyglucose activity distributions were simulated usingmore » a 4D extended cardiac torso anthropomorphic phantom including 12 spherical lesions at different anatomical locations (lower, upper, anterior, and posterior) within the lungs. Simulated data based on Monte Carlo simulation were obtained using GEANT4 application for tomographic emission (GATE). Fifty noise realizations of six respiratory-gated PET frames were simulated by GATE using a model of the Siemens Biograph mMR scanner geometry. PET sinograms of the thorax background and pulmonary lesions that were simulated separately were merged to generate different conditions of the lesions to the background (e.g., lesion contrast and motion). A conventional ordered subset expectation maximization (OSEM) reconstruction (5 iterations and 6 subsets) was used to obtain: (1) gated, (2) nongated, and (3) motion-corrected image volumes (a total of 3200 subimage volumes: 2400 gated, 400 nongated, and 400 motion-corrected). Lesion-detection signal-to-noise ratios (SNRs) were measured in different lesion-to-background contrast levels (3.5, 8.0, 9.0, and 20.0), lesion diameters (10.0, 13.0, and 16.0 mm), and respiratory motion displacements (17.6–31.3 mm). The proposed 4D numerical observer applied on multiple-gated images was compared to the conventional 3D approach applied on the nongated and motion-corrected images. Results: On average, the proposed 4D numerical observer improved the detection SNR by 48.6% (p < 0.005), whereas the 3D methods on motion-corrected images improved by 31.0% (p < 0.005) as compared to the nongated method. For all different conditions of the lesions, the relative SNR measurement (Gain = SNR{sub Observed}/SNR{sub Nongated}) of the 4D method was significantly higher than one from the motion-corrected 3D method by 13.8% (p < 0.02), where Gain{sub 4D} was 1.49 ± 0.21 and Gain{sub 3D} was 1.31 ± 0.15. For the lesion with the highest amplitude of motion, the 4D numerical observer yielded the highest observer-performance improvement (176%). For the lesion undergoing the smallest motion amplitude, the 4D method provided superior lesion detectability compared with the 3D method, which provided a detection SNR close to the nongated method. The investigation on a structure of the 4D numerical observer showed that a Laguerre–Gaussian channel matrix with a volumetric 3D function yielded higher lesion-detection performance than one with a 2D-stack-channelized function, whereas a different kind of channels that have the ability to mimic the human visual system, i.e., difference-of-Gaussian, showed similar performance in detecting uniform and spherical lesions. The investigation of the detection performance when increasing noise levels yielded decreasing detection SNR by 27.6% and 41.5% for the nongated and gated methods, respectively. The investigation of lesion contrast and diameter showed that the proposed 4D observer preserved the linearity property of an optimal-linear observer while the motion was present. Furthermore, the investigation of the iteration and subset numbers of the OSEM algorithm demonstrated that these parameters had impact on the lesion detectability and the selection of the optimal parameters could provide the maximum lesion-detection performance. The proposed 4D numerical observer outperformed the other observers for the lesion-detection task in various lesion conditions and motions. Conclusions: The 4D numerical observer shows substantial improvement in lesion detectability over the 3D observer method. The proposed 4D approach could potentially provide a more reliable objective assessment of the impact of respiratory-gated PET improvement for lesion-detection tasks. On the other hand, the 4D approach may be used as an upper bound to investigate the performance of the motion correction method. In future work, the authors will validate the proposed 4D approach on clinical data for detection tasks in pulmonary oncology.« less

The ship edge feature detection based on high and low threshold for remote sensing image

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Shengyang

2018-05-01

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

Classification of SD-OCT volumes for DME detection: an anomaly detection approach

NASA Astrophysics Data System (ADS)

Sankar, S.; Sidibé, D.; Cheung, Y.; Wong, T. Y.; Lamoureux, E.; Milea, D.; Meriaudeau, F.

2016-03-01

Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binary Pattern (LBP) features are considered. The dimensionality of the extracted features is reduced using PCA. As the last stage, a GMM is fitted with features from normal volumes. During testing, features extracted from the test volume are evaluated with the fitted model for anomaly and classification is made based on the number of B-scans detected as outliers. The proposed method is tested on two OCT datasets achieving a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, experiments show that the proposed method achieves better classification performances than other recently published works.

A hyper-spherical adaptive sparse-grid method for high-dimensional discontinuity detection

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

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.

This work proposes and analyzes a hyper-spherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces is proposed. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hyper-surface of an N-dimensional dis- continuous quantity of interest, by virtue of a hyper-spherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyper-spherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of themore » hyper-surface, the new technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous error estimates and complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

2018-05-01

Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

Optimal chroma-like channel design for passive color image splicing detection

NASA Astrophysics Data System (ADS)

Zhao, Xudong; Li, Shenghong; Wang, Shilin; Li, Jianhua; Yang, Kongjin

2012-12-01

Image splicing is one of the most common image forgeries in our daily life and due to the powerful image manipulation tools, image splicing is becoming easier and easier. Several methods have been proposed for image splicing detection and all of them worked on certain existing color channels. However, the splicing artifacts vary in different color channels and the selection of color model is important for image splicing detection. In this article, instead of finding an existing color model, we propose a color channel design method to find the most discriminative channel which is referred to as optimal chroma-like channel for a given feature extraction method. Experimental results show that both spatial and frequency features extracted from the designed channel achieve higher detection rate than those extracted from traditional color channels.

Detecting Service Chains and Feature Interactions in Sensor-Driven Home Network Services

PubMed Central

Inada, Takuya; Igaki, Hiroshi; Ikegami, Kosuke; Matsumoto, Shinsuke; Nakamura, Masahide; Kusumoto, Shinji

2012-01-01

Sensor-driven services often cause chain reactions, since one service may generate an environmental impact that automatically triggers another service. We first propose a framework that can formalize and detect such service chains based on ECA (event, condition, action) rules. Although the service chain can be a major source of feature interactions, not all service chains lead to harmful interactions. Therefore, we then propose a method that identifies feature interactions within the service chains. Specifically, we characterize the degree of deviation of every service chain by evaluating the gap between expected and actual service states. An experimental evaluation demonstrates that the proposed method successfully detects 11 service chains and 6 feature interactions within 7 practical sensor-driven services. PMID:23012499

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

PubMed

Li, Yang; Cui, Weigang; Luo, Meilin; Li, Ke; Wang, Lina

2018-01-25

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

A Colorimetric and Fluorescent Probe for the Detection of Cu2+ in a Complete Aqueous Solution.

PubMed

Xu, Jing; Wang, Zuokai; Liu, Caiyun; Xu, Zhenghe; Zhu, Baocun; Wang, Ning; Wang, Kun; Wang, Jiangting

2018-01-01

The fluorescent probe has become an important method for the detection of heavy metal ions. In the present work, a new and simple fluorescent probe, Cu-P, for detecting copper ion (Cu 2+ ) was designed and synthesized. The probe has shown high sensitivity and selectivity toward Cu 2+ . The detection limit was 13 nM (based on the 3σ/slope). A significant color change from yellow to pink was observed; thus, the probe Cu-P could serve as a "naked-eye" indicator for Cu 2+ . Furthermore, the proposed probe was used to detect Cu 2+ in real water and soil extract samples, with the result being satisfactory. Therefore, our proposed probe would provide a promising method for the detection of Cu 2+ in the environment.

Unsupervised change detection of multispectral images based on spatial constraint chi-squared transform and Markov random field model

NASA Astrophysics Data System (ADS)

Shi, Aiye; Wang, Chao; Shen, Shaohong; Huang, Fengchen; Ma, Zhenli

2016-10-01

Chi-squared transform (CST), as a statistical method, can describe the difference degree between vectors. The CST-based methods operate directly on information stored in the difference image and are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. However, the technique does not take spatial information into consideration, which leads to much noise in the result of change detection. An improved unsupervised change detection method is proposed based on spatial constraint CST (SCCST) in combination with a Markov random field (MRF) model. First, the mean and variance matrix of the difference image of bitemporal images are estimated by an iterative trimming method. In each iteration, spatial information is injected to reduce scattered changed points (also known as "salt and pepper" noise). To determine the key parameter confidence level in the SCCST method, a pseudotraining dataset is constructed to estimate the optimal value. Then, the result of SCCST, as an initial solution of change detection, is further improved by the MRF model. The experiments on simulated and real multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.

Curvature correction of retinal OCTs using graph-based geometry detection

NASA Astrophysics Data System (ADS)

Kafieh, Raheleh; Rabbani, Hossein; Abramoff, Michael D.; Sonka, Milan

2013-05-01

In this paper, we present a new algorithm as an enhancement and preprocessing step for acquired optical coherence tomography (OCT) images of the retina. The proposed method is composed of two steps, first of which is a denoising algorithm with wavelet diffusion based on a circular symmetric Laplacian model, and the second part can be described in terms of graph-based geometry detection and curvature correction according to the hyper-reflective complex layer in the retina. The proposed denoising algorithm showed an improvement of contrast-to-noise ratio from 0.89 to 1.49 and an increase of signal-to-noise ratio (OCT image SNR) from 18.27 to 30.43 dB. By applying the proposed method for estimation of the interpolated curve using a full automatic method, the mean ± SD unsigned border positioning error was calculated for normal and abnormal cases. The error values of 2.19 ± 1.25 and 8.53 ± 3.76 µm were detected for 200 randomly selected slices without pathological curvature and 50 randomly selected slices with pathological curvature, respectively. The important aspect of this algorithm is its ability in detection of curvature in strongly pathological images that surpasses previously introduced methods; the method is also fast, compared to the relatively low speed of similar methods.

Salient region detection by fusing bottom-up and top-down features extracted from a single image.

PubMed

Tian, Huawei; Fang, Yuming; Zhao, Yao; Lin, Weisi; Ni, Rongrong; Zhu, Zhenfeng

2014-10-01

Recently, some global contrast-based salient region detection models have been proposed based on only the low-level feature of color. It is necessary to consider both color and orientation features to overcome their limitations, and thus improve the performance of salient region detection for images with low-contrast in color and high-contrast in orientation. In addition, the existing fusion methods for different feature maps, like the simple averaging method and the selective method, are not effective sufficiently. To overcome these limitations of existing salient region detection models, we propose a novel salient region model based on the bottom-up and top-down mechanisms: the color contrast and orientation contrast are adopted to calculate the bottom-up feature maps, while the top-down cue of depth-from-focus from the same single image is used to guide the generation of final salient regions, since depth-from-focus reflects the photographer's preference and knowledge of the task. A more general and effective fusion method is designed to combine the bottom-up feature maps. According to the degree-of-scattering and eccentricities of feature maps, the proposed fusion method can assign adaptive weights to different feature maps to reflect the confidence level of each feature map. The depth-from-focus of the image as a significant top-down feature for visual attention in the image is used to guide the salient regions during the fusion process; with its aid, the proposed fusion method can filter out the background and highlight salient regions for the image. Experimental results show that the proposed model outperforms the state-of-the-art models on three public available data sets.

Crack image segmentation based on improved DBC method

NASA Astrophysics Data System (ADS)

Cao, Ting; Yang, Nan; Wang, Fengping; Gao, Ting; Wang, Weixing

2017-11-01

With the development of computer vision technology, crack detection based on digital image segmentation method arouses global attentions among researchers and transportation ministries. Since the crack always exhibits the random shape and complex texture, it is still a challenge to accomplish reliable crack detection results. Therefore, a novel crack image segmentation method based on fractal DBC (differential box counting) is introduced in this paper. The proposed method can estimate every pixel fractal feature based on neighborhood information which can consider the contribution from all possible direction in the related block. The block moves just one pixel every time so that it could cover all the pixels in the crack image. Unlike the classic DBC method which only describes fractal feature for the related region, this novel method can effectively achieve crack image segmentation according to the fractal feature of every pixel. The experiment proves the proposed method can achieve satisfactory results in crack detection.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

NASA Astrophysics Data System (ADS)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

Automatic Implementation of Prony Analysis for Electromechanical Mode Identification from Phasor Measurements

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

Zhou, Ning; Huang, Zhenyu; Tuffner, Francis K.

2010-07-31

Small signal stability problems are one of the major threats to grid stability and reliability. Prony analysis has been successfully applied on ringdown data to monitor electromechanical modes of a power system using phasor measurement unit (PMU) data. To facilitate an on-line application of mode estimation, this paper developed a recursive algorithm for implementing Prony analysis and proposed an oscillation detection method to detect ringdown data in real time. By automatically detect ringdown data, the proposed method helps guarantee that Prony analysis is applied properly and timely on the ringdown data. Thus, the mode estimation results can be performed reliablymore » and timely. The proposed method is tested using Monte Carlo simulations based on a 17-machine model and is shown to be able to properly identify the oscillation data for on-line application of Prony analysis.« less

Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering.

PubMed

Saffarzadeh, Vahid Mohammadi; Osareh, Alireza; Shadgar, Bita

2014-04-01

Detecting blood vessels is a vital task in retinal image analysis. The task is more challenging with the presence of bright and dark lesions in retinal images. Here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. First, the negative impact of bright lesions is reduced by using K-means segmentation in a perceptive space. Then, a multi-scale line operator is utilized to detect vessels while ignoring some of the dark lesions, which have intensity structures different from the line-shaped vessels in the retina. The proposed algorithm is tested on two publicly available STARE and DRIVE databases. The performance of the method is measured by calculating the area under the receiver operating characteristic curve and the segmentation accuracy. The proposed method achieves 0.9483 and 0.9387 localization accuracy against STARE and DRIVE respectively.

Mover Position Detection for PMTLM Based on Linear Hall Sensors through EKF Processing

PubMed Central

Yan, Leyang; Zhang, Hui; Ye, Peiqing

2017-01-01

Accurate mover position is vital for a permanent magnet tubular linear motor (PMTLM) control system. In this paper, two linear Hall sensors are utilized to detect the mover position. However, Hall sensor signals contain third-order harmonics, creating errors in mover position detection. To filter out the third-order harmonics, a signal processing method based on the extended Kalman filter (EKF) is presented. The limitation of conventional processing method is first analyzed, and then EKF is adopted to detect the mover position. In the EKF model, the amplitude of the fundamental component and the percentage of the harmonic component are taken as state variables, and they can be estimated based solely on the measured sensor signals. Then, the harmonic component can be calculated and eliminated. The proposed method has the advantages of faster convergence, better stability and higher accuracy. Finally, experimental results validate the effectiveness and superiority of the proposed method. PMID:28383505

Seamless image stitching by homography refinement and structure deformation using optimal seam pair detection

NASA Astrophysics Data System (ADS)

Lee, Daeho; Lee, Seohyung

2017-11-01

We propose an image stitching method that can remove ghost effects and realign the structure misalignments that occur in common image stitching methods. To reduce the artifacts caused by different parallaxes, an optimal seam pair is selected by comparing the cross correlations from multiple seams detected by variable cost weights. Along the optimal seam pair, a histogram of oriented gradients is calculated, and feature points for matching are detected. The homography is refined using the matching points, and the remaining misalignment is eliminated using the propagation of deformation vectors calculated from matching points. In multiband blending, the overlapping regions are determined from a distance between the matching points to remove overlapping artifacts. The experimental results show that the proposed method more robustly eliminates misalignments and overlapping artifacts than the existing method that uses single seam detection and gradient features.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning

PubMed Central

Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). PMID:28264011

Small target detection based on difference accumulation and Gaussian curvature under complex conditions

NASA Astrophysics Data System (ADS)

Zhang, He; Niu, Yanxiong; Zhang, Hao

2017-12-01

Small target detection is a significant subject in infrared search and track and other photoelectric imaging systems. The small target is imaged under complex conditions, which contains clouds, horizon and bright part. In this paper, a novel small target detection method is proposed based on difference accumulation, clustering and Gaussian curvature. Difference accumulation varies from regions. Therefore, after obtaining difference accumulations, clustering is applied to determine whether the pixel belongs to the heterogeneous region, and eliminate heterogeneous region. Then Gaussian curvature is used to separate target from the homogeneous region. Experiments are conducted for verification, along with comparisons to several other methods. The experimental results demonstrate that our method has an advantage of 1-2 orders of magnitude on SCRG and BSF than others. Given that the false alarm rate is 1, the detection probability can be approximately 0.9 by using proposed method.

Detection and Classification of Transformer Winding Mechanical Faults Using UWB Sensors and Bayesian Classifier

NASA Astrophysics Data System (ADS)

Alehosseini, Ali; A. Hejazi, Maryam; Mokhtari, Ghassem; B. Gharehpetian, Gevork; Mohammadi, Mohammad

2015-06-01

In this paper, the Bayesian classifier is used to detect and classify the radial deformation and axial displacement of transformer windings. The proposed method is tested on a model of transformer for different volumes of radial deformation and axial displacement. In this method, ultra-wideband (UWB) signal is sent to the simplified model of the transformer winding. The received signal from the winding model is recorded and used for training and testing of Bayesian classifier in different axial displacement and radial deformation states of the winding. It is shown that the proposed method has a good accuracy to detect and classify the axial displacement and radial deformation of the winding.

Analysis of the dynamic behavior of structures using the high-rate GNSS-PPP method combined with a wavelet-neural model: Numerical simulation and experimental tests

NASA Astrophysics Data System (ADS)

Kaloop, Mosbeh R.; Yigit, Cemal O.; Hu, Jong W.

2018-03-01

Recently, the high rate global navigation satellite system-precise point positioning (GNSS-PPP) technique has been used to detect the dynamic behavior of structures. This study aimed to increase the accuracy of the extraction oscillation properties of structural movements based on the high-rate (10 Hz) GNSS-PPP monitoring technique. A developmental model based on the combination of wavelet package transformation (WPT) de-noising and neural network prediction (NN) was proposed to improve the dynamic behavior of structures for GNSS-PPP method. A complicated numerical simulation involving highly noisy data and 13 experimental cases with different loads were utilized to confirm the efficiency of the proposed model design and the monitoring technique in detecting the dynamic behavior of structures. The results revealed that, when combined with the proposed model, GNSS-PPP method can be used to accurately detect the dynamic behavior of engineering structures as an alternative to relative GNSS method.

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

NASA Astrophysics Data System (ADS)

Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

2018-04-01

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

2018-02-01

This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

Structural health monitoring using DOG multi-scale space: an approach for analyzing damage characteristics

NASA Astrophysics Data System (ADS)

Guo, Tian; Xu, Zili

2018-03-01

Measurement noise is inevitable in practice; thus, it is difficult to identify defects, cracks or damage in a structure while suppressing noise simultaneously. In this work, a novel method is introduced to detect multiple damage in noisy environments. Based on multi-scale space analysis for discrete signals, a method for extracting damage characteristics from the measured displacement mode shape is illustrated. Moreover, the proposed method incorporates a data fusion algorithm to further eliminate measurement noise-based interference. The effectiveness of the method is verified by numerical and experimental methods applied to different structural types. The results demonstrate that there are two advantages to the proposed method. First, damage features are extracted by the difference of the multi-scale representation; this step is taken such that the interference of noise amplification can be avoided. Second, a data fusion technique applied to the proposed method provides a global decision, which retains the damage features while maximally eliminating the uncertainty. Monte Carlo simulations are utilized to validate that the proposed method has a higher accuracy in damage detection.

Chatter detection in milling process based on VMD and energy entropy

NASA Astrophysics Data System (ADS)

Liu, Changfu; Zhu, Lida; Ni, Chenbing

2018-05-01

This paper presents a novel approach to detect the milling chatter based on Variational Mode Decomposition (VMD) and energy entropy. VMD has already been employed in feature extraction from non-stationary signals. The parameters like number of modes (K) and the quadratic penalty (α) need to be selected empirically when raw signal is decomposed by VMD. Aimed at solving the problem how to select K and α, the automatic selection method of VMD's based on kurtosis is proposed in this paper. When chatter occurs in the milling process, energy will be absorbed to chatter frequency bands. To detect the chatter frequency bands automatically, the chatter detection method based on energy entropy is presented. The vibration signal containing chatter frequency is simulated and three groups of experiments which represent three cutting conditions are conducted. To verify the effectiveness of method presented by this paper, chatter feather extraction has been successfully employed on simulation signals and experimental signals. The simulation and experimental results show that the proposed method can effectively detect the chatter.

A rapid low-cost high-density DNA-based multi-detection test for routine inspection of meat species.

PubMed

Lin, Chun Chi; Fung, Lai Ling; Chan, Po Kwok; Lee, Cheuk Man; Chow, Kwok Fai; Cheng, Shuk Han

2014-02-01

The increasing occurrence of food frauds suggests that species identification should be part of food authentication. Current molecular-based species identification methods have their own limitations or drawbacks, such as relatively time-consuming experimental steps, expensive equipment and, in particular, these methods cannot identify mixed species in a single experiment. This project proposes an improved method involving PCR amplification of the COI gene and detection of species-specific sequences by hybridisation. Major innovative breakthrough lies in the detection of multiple species, including pork, beef, lamb, horse, cat, dog and mouse, from a mixed sample within a single experiment. The probes used are species-specific either in sole or mixed species samples. As little as 5 pg of DNA template in the PCR is detectable in the proposed method. By designing species-specific probes and adopting reverse dot blot hybridisation and flow-through hybridisation, a low-cost high-density DNA-based multi-detection test suitable for routine inspection of meat species was developed. © 2013.

Automatic abdominal lymph node detection method based on local intensity structure analysis from 3D x-ray CT images

NASA Astrophysics Data System (ADS)

Nakamura, Yoshihiko; Nimura, Yukitaka; Kitasaka, Takayuki; Mizuno, Shinji; Furukawa, Kazuhiro; Goto, Hidemi; Fujiwara, Michitaka; Misawa, Kazunari; Ito, Masaaki; Nawano, Shigeru; Mori, Kensaku

2013-03-01

This paper presents an automated method of abdominal lymph node detection to aid the preoperative diagnosis of abdominal cancer surgery. In abdominal cancer surgery, surgeons must resect not only tumors and metastases but also lymph nodes that might have a metastasis. This procedure is called lymphadenectomy or lymph node dissection. Insufficient lymphadenectomy carries a high risk for relapse. However, excessive resection decreases a patient's quality of life. Therefore, it is important to identify the location and the structure of lymph nodes to make a suitable surgical plan. The proposed method consists of candidate lymph node detection and false positive reduction. Candidate lymph nodes are detected using a multi-scale blob-like enhancement filter based on local intensity structure analysis. To reduce false positives, the proposed method uses a classifier based on support vector machine with the texture and shape information. The experimental results reveal that it detects 70.5% of the lymph nodes with 13.0 false positives per case.

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

NASA Astrophysics Data System (ADS)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

Exudate detection in color retinal images for mass screening of diabetic retinopathy.

PubMed

Zhang, Xiwei; Thibault, Guillaume; Decencière, Etienne; Marcotegui, Beatriz; Laÿ, Bruno; Danno, Ronan; Cazuguel, Guy; Quellec, Gwénolé; Lamard, Mathieu; Massin, Pascale; Chabouis, Agnès; Victor, Zeynep; Erginay, Ali

2014-10-01

The automatic detection of exudates in color eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to automatically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also detect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods. Copyright © 2014 Elsevier B.V. All rights reserved.

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings

NASA Astrophysics Data System (ADS)

Liang, Sheng-Fu; Chen, Yi-Chun; Wang, Yu-Lin; Chen, Pin-Tzu; Yang, Chia-Hsiang; Chiueh, Herming

2013-08-01

Objective. Around 1% of the world's population is affected by epilepsy, and nearly 25% of patients cannot be treated effectively by available therapies. The presence of closed-loop seizure-triggered stimulation provides a promising solution for these patients. Realization of fast, accurate, and energy-efficient seizure detection is the key to such implants. In this study, we propose a two-stage on-line seizure detection algorithm with low-energy consumption for temporal lobe epilepsy (TLE). Approach. Multi-channel signals are processed through independent component analysis and the most representative independent component (IC) is automatically selected to eliminate artifacts. Seizure-like intracranial electroencephalogram (iEEG) segments are fast detected in the first stage of the proposed method and these seizures are confirmed in the second stage. The conditional activation of the second-stage signal processing reduces the computational effort, and hence energy, since most of the non-seizure events are filtered out in the first stage. Main results. Long-term iEEG recordings of 11 patients who suffered from TLE were analyzed via leave-one-out cross validation. The proposed method has a detection accuracy of 95.24%, a false alarm rate of 0.09/h, and an average detection delay time of 9.2 s. For the six patients with mesial TLE, a detection accuracy of 100.0%, a false alarm rate of 0.06/h, and an average detection delay time of 4.8 s can be achieved. The hierarchical approach provides a 90% energy reduction, yielding effective and energy-efficient implementation for real-time epileptic seizure detection. Significance. An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.

Adaboost multi-view face detection based on YCgCr skin color model

NASA Astrophysics Data System (ADS)

Lan, Qi; Xu, Zhiyong

2016-09-01

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.

PubMed

Akram, Usman M; Khan, Shoab A

2012-10-01

There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early. DR has different signs and the most distinctive are microaneurysm and haemorrhage which are dark lesions and hard exudates and cotton wool spots which are bright lesions. Location and structure of blood vessels and optic disk play important role in accurate detection and classification of dark and bright lesions for early detection of DR. In this article, we propose a computer aided system for the early detection of DR. The article presents algorithms for retinal image preprocessing, blood vessel enhancement and segmentation and optic disk localization and detection which eventually lead to detection of different DR lesions using proposed hybrid fuzzy classifier. The developed methods are tested on four different publicly available databases. The presented methods are compared with recently published methods and the results show that presented methods outperform all others.

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

PubMed Central

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-01-01

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

PubMed

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-02-26

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

Fast and simultaneous determination of 12 polyphenols in apple peel and pulp by using chemometrics-assisted high-performance liquid chromatography with diode array detection.

PubMed

Wang, Tong; Wu, Hai-Long; Xie, Li-Xia; Zhu, Li; Liu, Zhi; Sun, Xiao-Dong; Xiao, Rong; Yu, Ru-Qin

2017-04-01

In this work, a smart chemometrics-enhanced strategy, high-performance liquid chromatography, and diode array detection coupled with second-order calibration method based on alternating trilinear decomposition algorithm was proposed to simultaneously quantify 12 polyphenols in different kinds of apple peel and pulp samples. The proposed strategy proved to be a powerful tool to solve the problems of coelution, unknown interferences, and chromatographic shifts in the process of high-performance liquid chromatography analysis, making it possible for the determination of 12 polyphenols in complex apple matrices within 10 min under simple conditions of elution. The average recoveries with standard deviations, and figures of merit including sensitivity, selectivity, limit of detection, and limit of quantitation were calculated to validate the accuracy of the proposed method. Compared to the quantitative analysis results from the classic high-performance liquid chromatography method, the statistical and graphical analysis showed that our proposed strategy obtained more reliable results. All results indicated that our proposed method used in the quantitative analysis of apple polyphenols was an accurate, fast, universal, simple, and green one, and it was expected to be developed as an attractive alternative method for simultaneous determination of multitargeted analytes in complex matrices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

PubMed Central

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. PMID:27413364

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.

PubMed

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.

Multitask assessment of roads and vehicles network (MARVN)

NASA Astrophysics Data System (ADS)

Yang, Fang; Yi, Meng; Cai, Yiran; Blasch, Erik; Sullivan, Nichole; Sheaff, Carolyn; Chen, Genshe; Ling, Haibin

2018-05-01

Vehicle detection in wide area motion imagery (WAMI) has drawn increasing attention from the computer vision research community in recent decades. In this paper, we present a new architecture for vehicle detection on road using multi-task network, which is able to detect and segment vehicles, estimate their pose, and meanwhile yield road isolation for a given region. The multi-task network consists of three components: 1) vehicle detection, 2) vehicle and road segmentation, and 3) detection screening. Segmentation and detection components share the same backbone network and are trained jointly in an end-to-end way. Unlike background subtraction or frame differencing based methods, the proposed Multitask Assessment of Roads and Vehicles Network (MARVN) method can detect vehicles which are slowing down, stopped, and/or partially occluded in a single image. In addition, the method can eliminate the detections which are located at outside road using yielded road segmentation so as to decrease the false positive rate. As few WAMI datasets have road mask and vehicles bounding box anotations, we extract 512 frames from WPAFB 2009 dataset and carefully refine the original annotations. The resulting dataset is thus named as WAMI512. We extensively compare the proposed method with state-of-the-art methods on WAMI512 dataset, and demonstrate superior performance in terms of efficiency and accuracy.

High-performance combination method of electric network frequency and phase for audio forgery detection in battery-powered devices.

PubMed

Savari, Maryam; Abdul Wahab, Ainuddin Wahid; Anuar, Nor Badrul

2016-09-01

Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Brain medical image diagnosis based on corners with importance-values.

PubMed

Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

2017-11-21

Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

Detection of circuit-board components with an adaptive multiclass correlation filter

NASA Astrophysics Data System (ADS)

Diaz-Ramirez, Victor H.; Kober, Vitaly

2008-08-01

A new method for reliable detection of circuit-board components is proposed. The method is based on an adaptive multiclass composite correlation filter. The filter is designed with the help of an iterative algorithm using complex synthetic discriminant functions. The impulse response of the filter contains information needed to localize and classify geometrically distorted circuit-board components belonging to different classes. Computer simulation results obtained with the proposed method are provided and compared with those of known multiclass correlation based techniques in terms of performance criteria for recognition and classification of objects.

A Method of Character Detection and Segmentation for Highway Guide Signs

NASA Astrophysics Data System (ADS)

Xu, Jiawei; Zhang, Chongyang

2018-01-01

In this paper, a method of character detection and segmentation for highway signs in China is proposed. It consists of four steps. Firstly, the highway sign area is detectedby colour and geometric features, andthe possible character region is obtained by multi-level projection strategy. Secondly, pseudo target character region is removed by local binary patterns (LBP) feature. Thirdly, convolutional neural network (CNN)is used to classify target regions. Finally, adaptive projection strategies are used to segment characters strings. Experimental results indicate that the proposed method achieves new state-of-the-art results.

Text String Detection from Natural Scenes by Structure-based Partition and Grouping

PubMed Central

Yi, Chucai; Tian, YingLi

2012-01-01

Text information in natural scene images serves as important clues for many image-based applications such as scene understanding, content-based image retrieval, assistive navigation, and automatic geocoding. However, locating text from complex background with multiple colors is a challenging task. In this paper, we explore a new framework to detect text strings with arbitrary orientations in complex natural scene images. Our proposed framework of text string detection consists of two steps: 1) Image partition to find text character candidates based on local gradient features and color uniformity of character components. 2) Character candidate grouping to detect text strings based on joint structural features of text characters in each text string such as character size differences, distances between neighboring characters, and character alignment. By assuming that a text string has at least three characters, we propose two algorithms of text string detection: 1) adjacent character grouping method, and 2) text line grouping method. The adjacent character grouping method calculates the sibling groups of each character candidate as string segments and then merges the intersecting sibling groups into text string. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitted text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region covering all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi-scales. The proposed methods outperform the state-of-the-art results on the public Robust Reading Dataset which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in non-horizontal orientations. PMID:21411405

Text string detection from natural scenes by structure-based partition and grouping.

PubMed

Yi, Chucai; Tian, YingLi

2011-09-01

Text information in natural scene images serves as important clues for many image-based applications such as scene understanding, content-based image retrieval, assistive navigation, and automatic geocoding. However, locating text from a complex background with multiple colors is a challenging task. In this paper, we explore a new framework to detect text strings with arbitrary orientations in complex natural scene images. Our proposed framework of text string detection consists of two steps: 1) image partition to find text character candidates based on local gradient features and color uniformity of character components and 2) character candidate grouping to detect text strings based on joint structural features of text characters in each text string such as character size differences, distances between neighboring characters, and character alignment. By assuming that a text string has at least three characters, we propose two algorithms of text string detection: 1) adjacent character grouping method and 2) text line grouping method. The adjacent character grouping method calculates the sibling groups of each character candidate as string segments and then merges the intersecting sibling groups into text string. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitted text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region covering all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi-scales. The proposed methods outperform the state-of-the-art results on the public Robust Reading Dataset, which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in nonhorizontal orientations.

Multitarget detection algorithm for automotive FMCW radar

NASA Astrophysics Data System (ADS)

Hyun, Eugin; Oh, Woo-Jin; Lee, Jong-Hun

2012-06-01

Today, 77 GHz FMCW (Frequency Modulation Continuous Wave) radar has strong advantages of range and velocity detection for automotive applications. However, FMCW radar brings out ghost targets and missed targets in multi-target situations. In this paper, in order to resolve these limitations, we propose an effective pairing algorithm, which consists of two steps. In the proposed method, a waveform with different slopes in two periods is used. In the 1st pairing processing, all combinations of range and velocity are obtained in each of two wave periods. In the 2nd pairing step, using the results of the 1st pairing processing, fine range and velocity are detected. In that case, we propose the range-velocity windowing technique in order to compensate for the non-ideal beat-frequency characteristic that arises due to the non-linearity of the RF module. Based on experimental results, the performance of the proposed algorithm is improved compared with that of the typical method.

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery.

PubMed

Siddiqui, Fasahat Ullah; Teng, Shyh Wei; Awrangjeb, Mohammad; Lu, Guojun

2016-07-19

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery

PubMed Central

Siddiqui, Fasahat Ullah; Teng, Shyh Wei; Awrangjeb, Mohammad; Lu, Guojun

2016-01-01

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics. PMID:27447631

A novel spatial-temporal detection method of dim infrared moving small target

NASA Astrophysics Data System (ADS)

Chen, Zhong; Deng, Tao; Gao, Lei; Zhou, Heng; Luo, Song

2014-09-01

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial-temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time-space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

Envelope detection using temporal magnetization dynamics of resonantly interacting spin-torque oscillator

NASA Astrophysics Data System (ADS)

Nakamura, Y.; Nishikawa, M.; Osawa, H.; Okamoto, Y.; Kanao, T.; Sato, R.

2018-05-01

In this article, we propose the detection method of the recorded data pattern by the envelope of the temporal magnetization dynamics of resonantly interacting spin-torque oscillator on the microwave assisted magnetic recording for three-dimensional magnetic recording. We simulate the envelope of the waveform from recorded dots with the staggered magnetization configuration, which are calculated by using a micromagnetic simulation. We study the data detection methods for the envelope and propose a soft-output Viterbi algorithm (SOVA) for partial response (PR) system as a signal processing system for three dimensional magnetic recording.

Portable and low-cost colorimetric office paper-based device for phenacetin detection in seized cocaine samples.

PubMed

da Silva, Gabriela O; de Araujo, William R; Paixão, Thiago R L C

2018-01-01

An office paper-based colorimetric device is proposed as a portable, rapid, and low-cost sensor for forensic applications aiming to detect phenacetin used as adulterant in illicit seized materials such as cocaine. The proposed method uses white office paper as the substrate and wax printing technology to fabricate the detection zones. Based on the optimum conditions, a linear analytical curve was obtained for phenacetin concentrations ranging from 0 to 64.52µgmL ‒1 , and the straight line was in accordance with the following equation: (Magenta percentage color) = 1.19 + 0.458 (C Phe /µgmL ‒1 ), R 2 = 0.990. The limit of detection was calculated as 3.5µgmL ‒1 (3σ/slope). The accuracy of the proposed method was evaluated using real seized cocaine samples and the spike-recovery procedure. Copyright © 2017 Elsevier B.V. All rights reserved.

Multiple sound source localization using gammatone auditory filtering and direct sound componence detection

NASA Astrophysics Data System (ADS)

Chen, Huaiyu; Cao, Li

2017-06-01

In order to research multiple sound source localization with room reverberation and background noise, we analyze the shortcomings of traditional broadband MUSIC and ordinary auditory filtering based broadband MUSIC method, then a new broadband MUSIC algorithm with gammatone auditory filtering of frequency component selection control and detection of ascending segment of direct sound componence is proposed. The proposed algorithm controls frequency component within the interested frequency band in multichannel bandpass filter stage. Detecting the direct sound componence of the sound source for suppressing room reverberation interference is also proposed, whose merits are fast calculation and avoiding using more complex de-reverberation processing algorithm. Besides, the pseudo-spectrum of different frequency channels is weighted by their maximum amplitude for every speech frame. Through the simulation and real room reverberation environment experiments, the proposed method has good performance. Dynamic multiple sound source localization experimental results indicate that the average absolute error of azimuth estimated by the proposed algorithm is less and the histogram result has higher angle resolution.

A Kalman filtering framework for physiological detection of anxiety-related arousal in children with autism spectrum disorder.

PubMed

Kushki, Azadeh; Khan, Ajmal; Brian, Jessica; Anagnostou, Evdokia

2015-03-01

Anxiety is associated with physiological changes that can be noninvasively measured using inexpensive and wearable sensors. These changes provide an objective and language-free measure of arousal associated with anxiety, which can complement treatment programs for clinical populations who have difficulty with introspection, communication, and emotion recognition. This motivates the development of automatic methods for detection of anxiety-related arousal using physiology signals. While several supervised learning methods have been proposed for this purpose, these methods require regular collection and updating of training data and are, therefore, not suitable for clinical populations, where obtaining labelled data may be challenging due to impairments in communication and introspection. In this context, the objective of this paper is to develop an unsupervised and real-time arousal detection algorithm. We propose a learning framework based on the Kalman filtering theory for detection of physiological arousal based on cardiac activity. The performance of the system was evaluated on data obtained from a sample of children with autism spectrum disorder. The results indicate that the system can detect anxiety-related arousal in these children with sensitivity and specificity of 99% and 92%, respectively. Our results show that the proposed method can detect physiological arousal associated with anxiety with high accuracy, providing support for technical feasibility of augmenting anxiety treatments with automatic detection techniques. This approach can ultimately lead to more effective anxiety treatment for a larger and more diverse population.

Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

PubMed

Emam, Mahmoud; Han, Qi; Zhang, Hongli

2018-01-01

In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods. © 2017 American Academy of Forensic Sciences.

A fast button surface defects detection method based on convolutional neural network

NASA Astrophysics Data System (ADS)

Liu, Lizhe; Cao, Danhua; Wu, Songlin; Wu, Yubin; Wei, Taoran

2018-01-01

Considering the complexity of the button surface texture and the variety of buttons and defects, we propose a fast visual method for button surface defect detection, based on convolutional neural network (CNN). CNN has the ability to extract the essential features by training, avoiding designing complex feature operators adapted to different kinds of buttons, textures and defects. Firstly, we obtain the normalized button region and then use HOG-SVM method to identify the front and back side of the button. Finally, a convolutional neural network is developed to recognize the defects. Aiming at detecting the subtle defects, we propose a network structure with multiple feature channels input. To deal with the defects of different scales, we take a strategy of multi-scale image block detection. The experimental results show that our method is valid for a variety of buttons and able to recognize all kinds of defects that have occurred, including dent, crack, stain, hole, wrong paint and uneven. The detection rate exceeds 96%, which is much better than traditional methods based on SVM and methods based on template match. Our method can reach the speed of 5 fps on DSP based smart camera with 600 MHz frequency.

A Simple Method Based on the Application of a CCD Camera as a Sensor to Detect Low Concentrations of Barium Sulfate in Suspension

PubMed Central

de Sena, Rodrigo Caciano; Soares, Matheus; Pereira, Maria Luiza Oliveira; da Silva, Rogério Cruz Domingues; do Rosário, Francisca Ferreira; da Silva, Joao Francisco Cajaiba

2011-01-01

The development of a simple, rapid and low cost method based on video image analysis and aimed at the detection of low concentrations of precipitated barium sulfate is described. The proposed system is basically composed of a webcam with a CCD sensor and a conventional dichroic lamp. For this purpose, software for processing and analyzing the digital images based on the RGB (Red, Green and Blue) color system was developed. The proposed method had shown very good repeatability and linearity and also presented higher sensitivity than the standard turbidimetric method. The developed method is presented as a simple alternative for future applications in the study of precipitations of inorganic salts and also for detecting the crystallization of organic compounds. PMID:22346607

A novel sensitivity-based method for damage detection of structures under unknown periodic excitations

NASA Astrophysics Data System (ADS)

Naseralavi, S. S.; Salajegheh, E.; Fadaee, M. J.; Salajegheh, J.

2014-06-01

This paper presents a technique for damage detection in structures under unknown periodic excitations using the transient displacement response. The method is capable of identifying the damage parameters without finding the input excitations. We first define the concept of displacement space as a linear space in which each point represents displacements of structure under an excitation and initial condition. Roughly speaking, the method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering this novel geometrical viewpoint, an equation called kernel parallelization equation (KPE) is derived for damage detection under unknown periodic excitations and a sensitivity-based algorithm for solving KPE is proposed accordingly. The method is evaluated via three case studies under periodic excitations, which confirm the efficiency of the proposed method.

A novel line segment detection algorithm based on graph search

NASA Astrophysics Data System (ADS)

Zhao, Hong-dan; Liu, Guo-ying; Song, Xu

2018-02-01

To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).

Finger tips detection for two handed gesture recognition

NASA Astrophysics Data System (ADS)

Bhuyan, M. K.; Kar, Mithun Kumar; Neog, Debanga Raj

2011-10-01

In this paper, a novel algorithm is proposed for fingertips detection in view of two-handed static hand pose recognition. In our method, finger tips of both hands are detected after detecting hand regions by skin color-based segmentation. At first, the face is removed in the image by using Haar classifier and subsequently, the regions corresponding to the gesturing hands are isolated by a region labeling technique. Next, the key geometric features characterizing gesturing hands are extracted for two hands. Finally, for all possible/allowable finger movements, a probabilistic model is developed for pose recognition. Proposed method can be employed in a variety of applications like sign language recognition and human-robot-interactions etc.

Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion

PubMed Central

Qian, Zhi-Ming; Cheng, Xi En; Chen, Yan Qiu

2014-01-01

Due to its universality, swarm behavior in nature attracts much attention of scientists from many fields. Fish schools are examples of biological communities that demonstrate swarm behavior. The detection and tracking of fish in a school are of important significance for the quantitative research on swarm behavior. However, different from other biological communities, there are three problems in the detection and tracking of fish school, that is, variable appearances, complex motion and frequent occlusion. To solve these problems, we propose an effective method of fish detection and tracking. In this method, first, the fish head region is positioned through extremum detection and ellipse fitting; second, The Kalman filtering and feature matching are used to track the target in complex motion; finally, according to the feature information obtained by the detection and tracking, the tracking problems caused by frequent occlusion are processed through trajectory linking. We apply this method to track swimming fish school of different densities. The experimental results show that the proposed method is both accurate and reliable. PMID:25207811

Sunglass detection method for automation of video surveillance system

NASA Astrophysics Data System (ADS)

Sikandar, Tasriva; Samsudin, Wan Nur Azhani W.; Hawari Ghazali, Kamarul; Mohd, Izzeldin I.; Fazle Rabbi, Mohammad

2018-04-01

Wearing sunglass to hide face from surveillance camera is a common activity in criminal incidences. Therefore, sunglass detection from surveillance video has become a demanding issue in automation of security systems. In this paper we propose an image processing method to detect sunglass from surveillance images. Specifically, a unique feature using facial height and width has been employed to identify the covered region of the face. The presence of covered area by sunglass is evaluated using facial height-width ratio. Threshold value of covered area percentage is used to classify the glass wearing face. Two different types of glasses have been considered i.e. eye glass and sunglass. The results of this study demonstrate that the proposed method is able to detect sunglasses in two different illumination conditions such as, room illumination as well as in the presence of sunlight. In addition, due to the multi-level checking in facial region, this method has 100% accuracy of detecting sunglass. However, in an exceptional case where fabric surrounding the face has similar color as skin, the correct detection rate was found 93.33% for eye glass.

Robust detection of heartbeats using association models from blood pressure and EEG signals.

PubMed

Jeon, Taegyun; Yu, Jongmin; Pedrycz, Witold; Jeon, Moongu; Lee, Boreom; Lee, Byeongcheol

2016-01-15

The heartbeat is fundamental cardiac activity which is straightforwardly detected with a variety of measurement techniques for analyzing physiological signals. Unfortunately, unexpected noise or contaminated signals can distort or cut out electrocardiogram (ECG) signals in practice, misleading the heartbeat detectors to report a false heart rate or suspend itself for a considerable length of time in the worst case. To deal with the problem of unreliable heartbeat detection, PhysioNet/CinC suggests a challenge in 2014 for developing robust heart beat detectors using multimodal signals. This article proposes a multimodal data association method that supplements ECG as a primary input signal with blood pressure (BP) and electroencephalogram (EEG) as complementary input signals when input signals are unreliable. If the current signal quality index (SQI) qualifies ECG as a reliable input signal, our method applies QRS detection to ECG and reports heartbeats. Otherwise, the current SQI selects the best supplementary input signal between BP and EEG after evaluating the current SQI of BP. When BP is chosen as a supplementary input signal, our association model between ECG and BP enables us to compute their regular intervals, detect characteristics BP signals, and estimate the locations of the heartbeat. When both ECG and BP are not qualified, our fusion method resorts to the association model between ECG and EEG that allows us to apply an adaptive filter to ECG and EEG, extract the QRS candidates, and report heartbeats. The proposed method achieved an overall score of 86.26 % for the test data when the input signals are unreliable. Our method outperformed the traditional method, which achieved 79.28 % using QRS detector and BP detector from PhysioNet. Our multimodal signal processing method outperforms the conventional unimodal method of taking ECG signals alone for both training and test data sets. To detect the heartbeat robustly, we have proposed a novel multimodal data association method of supplementing ECG with a variety of physiological signals and accounting for the patient-specific lag between different pulsatile signals and ECG. Multimodal signal detectors and data-fusion approaches such as those proposed in this article can reduce false alarms and improve patient monitoring.

Failure Detecting Method of Fault Current Limiter System with Rectifier

NASA Astrophysics Data System (ADS)

Tokuda, Noriaki; Matsubara, Yoshio; Asano, Masakuni; Ohkuma, Takeshi; Sato, Yoshibumi; Takahashi, Yoshihisa

A fault current limiter (FCL) is extensively needed to suppress fault current, particularly required for trunk power systems connecting high-voltage transmission lines, such as 500kV class power system which constitutes the nucleus of the electric power system. We proposed a new type FCL system (rectifier type FCL), consisting of solid-state diodes, DC reactor and bypass AC reactor, and demonstrated the excellent performances of this FCL by developing the small 6.6kV and 66kV model. It is important to detect the failure of power devices used in the rectifier under the normal operating condition, for keeping the excellent reliability of the power system. In this paper, we have proposed a new failure detecting method of power devices most suitable for the rectifier type FCL. This failure detecting system is simple and compact. We have adapted the proposed system to the 66kV prototype single-phase model and successfully demonstrated to detect the failure of power devices.

Blood detection in wireless capsule endoscope images based on salient superpixels.

PubMed

Iakovidis, Dimitris K; Chatzis, Dimitris; Chrysanthopoulos, Panos; Koulaouzidis, Anastasios

2015-08-01

Wireless capsule endoscopy (WCE) enables screening of the gastrointestinal (GI) tract with a miniature, optical endoscope packed within a small swallowable capsule, wirelessly transmitting color images. In this paper we propose a novel method for automatic blood detection in contemporary WCE images. Blood is an alarming indication for the presence of pathologies requiring further treatment. The proposed method is based on a new definition of superpixel saliency. The saliency of superpixels is assessed upon their color, enabling the identification of image regions that are likely to contain blood. The blood patterns are recognized by their color features using a supervised learning machine. Experiments performed on a public dataset using automatically selected first-order statistical features from various color components indicate that the proposed method outperforms state-of-the-art methods.

A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots.

PubMed

Lee, Tae-Jae; Yi, Dong-Hoon; Cho, Dong-Il Dan

2016-03-01

This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%.

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

NASA Astrophysics Data System (ADS)

Raj, Nithin; Jagadanand, G.; George, Saly

2018-04-01

The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

Eye Tracking and Head Movement Detection: A State-of-Art Survey

PubMed Central

2013-01-01

Eye-gaze detection and tracking have been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human computer interaction. Head movement detection has also received researchers' attention and interest as it has been found to be a simple and effective interaction method. Both technologies are considered the easiest alternative interface methods. They serve a wide range of severely disabled people who are left with minimal motor abilities. For both eye tracking and head movement detection, several different approaches have been proposed and used to implement different algorithms for these technologies. Despite the amount of research done on both technologies, researchers are still trying to find robust methods to use effectively in various applications. This paper presents a state-of-art survey for eye tracking and head movement detection methods proposed in the literature. Examples of different fields of applications for both technologies, such as human-computer interaction, driving assistance systems, and assistive technologies are also investigated. PMID:27170851

Stability Analysis of Radial Turning Process for Superalloys

NASA Astrophysics Data System (ADS)

Jiménez, Alberto; Boto, Fernando; Irigoien, Itziar; Sierra, Basilio; Suarez, Alfredo

2017-09-01

Stability detection in machining processes is an essential component for the design of efficient machining processes. Automatic methods are able to determine when instability is happening and prevent possible machine failures. In this work a variety of methods are proposed for detecting stability anomalies based on the measured forces in the radial turning process of superalloys. Two different methods are proposed to determine instabilities. Each one is tested on real data obtained in the machining of Waspalloy, Haynes 282 and Inconel 718. Experimental data, in both Conventional and High Pressure Coolant (HPC) environments, are set in four different states depending on materials grain size and Hardness (LGA, LGS, SGA and SGS). Results reveal that PCA method is useful for visualization of the process and detection of anomalies in online processes.

An Effective Electrical Resonance-Based Method to Detect Delamination in Thermal Barrier Coating

NASA Astrophysics Data System (ADS)

Kim, Jong Min; Park, Jae-Ha; Lee, Ho Girl; Kim, Hak-Joon; Song, Sung-Jin; Seok, Chang-Sung; Lee, Young-Ze

2017-12-01

This research proposes a simple yet highly sensitive method based on electrical resonance of an eddy-current probe to detect delamination of thermal barrier coating (TBC). This method can directly measure the mechanical characteristics of TBC compared to conventional ultrasonic testing and infrared thermography methods. The electrical resonance-based method can detect the delamination of TBC from the metallic bond coat by shifting the electrical impedance of eddy current testing (ECT) probe coupling with degraded TBC, and, due to this shift, the resonant frequencies near the peak impedance of ECT probe revealed high sensitivity to the delamination. In order to verify the performance of the proposed method, a simple experiment is performed with degraded TBC specimens by thermal cyclic exposure. Consequently, the delamination with growth of thermally grown oxide in a TBC system is experimentally identified. Additionally, the results are in good agreement with the results obtained from ultrasonic C-scanning.

An Effective Electrical Resonance-Based Method to Detect Delamination in Thermal Barrier Coating

NASA Astrophysics Data System (ADS)

Kim, Jong Min; Park, Jae-Ha; Lee, Ho Girl; Kim, Hak-Joon; Song, Sung-Jin; Seok, Chang-Sung; Lee, Young-Ze

2018-02-01

This research proposes a simple yet highly sensitive method based on electrical resonance of an eddy-current probe to detect delamination of thermal barrier coating (TBC). This method can directly measure the mechanical characteristics of TBC compared to conventional ultrasonic testing and infrared thermography methods. The electrical resonance-based method can detect the delamination of TBC from the metallic bond coat by shifting the electrical impedance of eddy current testing (ECT) probe coupling with degraded TBC, and, due to this shift, the resonant frequencies near the peak impedance of ECT probe revealed high sensitivity to the delamination. In order to verify the performance of the proposed method, a simple experiment is performed with degraded TBC specimens by thermal cyclic exposure. Consequently, the delamination with growth of thermally grown oxide in a TBC system is experimentally identified. Additionally, the results are in good agreement with the results obtained from ultrasonic C-scanning.

A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

PubMed

Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya

2007-01-01

A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.

Overlapping communities from dense disjoint and high total degree clusters

NASA Astrophysics Data System (ADS)

Zhang, Hongli; Gao, Yang; Zhang, Yue

2018-04-01

Community plays an important role in the field of sociology, biology and especially in domains of computer science, where systems are often represented as networks. And community detection is of great importance in the domains. A community is a dense subgraph of the whole graph with more links between its members than between its members to the outside nodes, and nodes in the same community probably share common properties or play similar roles in the graph. Communities overlap when nodes in a graph belong to multiple communities. A vast variety of overlapping community detection methods have been proposed in the literature, and the local expansion method is one of the most successful techniques dealing with large networks. The paper presents a density-based seeding method, in which dense disjoint local clusters are searched and selected as seeds. The proposed method selects a seed by the total degree and density of local clusters utilizing merely local structures of the network. Furthermore, this paper proposes a novel community refining phase via minimizing the conductance of each community, through which the quality of identified communities is largely improved in linear time. Experimental results in synthetic networks show that the proposed seeding method outperforms other seeding methods in the state of the art and the proposed refining method largely enhances the quality of the identified communities. Experimental results in real graphs with ground-truth communities show that the proposed approach outperforms other state of the art overlapping community detection algorithms, in particular, it is more than two orders of magnitude faster than the existing global algorithms with higher quality, and it obtains much more accurate community structure than the current local algorithms without any priori information.

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

PubMed Central

Leng, Xiangguang; Ji, Kefeng; Zhou, Shilin; Xing, Xiangwei; Zou, Huanxin

2016-01-01

With the rapid development of spaceborne synthetic aperture radar (SAR) and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of great importance. Focusing on practical problems of ship detection, this paper presents a highly adaptive ship detection scheme for spaceborne SAR imagery. It is able to process a wide range of sensors, imaging modes and resolutions. Two main stages are identified in this paper, namely: ship candidate detection and ship discrimination. Firstly, this paper proposes an adaptive land masking method using ship size and pixel size. Secondly, taking into account the imaging mode, incidence angle, and polarization channel of SAR imagery, it implements adaptive ship candidate detection in spaceborne SAR imagery by applying different strategies to different resolution SAR images. Finally, aiming at different types of typical false alarms, this paper proposes a comprehensive ship discrimination method in spaceborne SAR imagery based on confidence level and complexity analysis. Experimental results based on RADARSAT-1, RADARSAT-2, TerraSAR-X, RS-1, and RS-3 images demonstrate that the adaptive scheme proposed in this paper is able to detect ship targets in a fast, efficient and robust way. PMID:27563902

Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity

NASA Astrophysics Data System (ADS)

Zhang, Ling; Yin, Jian-qin; Lin, Jia-ben; Feng, Zhi-quan; Zhou, Jin

2017-07-01

Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space-time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space-time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

New coherent laser communication detection scheme based on channel-switching method.

PubMed

Liu, Fuchuan; Sun, Jianfeng; Ma, Xiaoping; Hou, Peipei; Cai, Guangyu; Sun, Zhiwei; Lu, Zhiyong; Liu, Liren

2015-04-01

A new coherent laser communication detection scheme based on the channel-switching method is proposed. The detection front end of this scheme comprises a 90° optical hybrid and two balanced photodetectors which outputs the in-phase (I) channel and quadrature-phase (Q) channel signal current, respectively. With this method, the ultrahigh speed analog/digital transform of the signal of the I or Q channel is not required. The phase error between the signal and local lasers is obtained by simple analog circuit. Using the phase error signal, the signals of the I/Q channel are switched alternately. The principle of this detection scheme is presented. Moreover, the comparison of the sensitivity of this scheme with that of homodyne detection with an optical phase-locked loop is discussed. An experimental setup was constructed to verify the proposed detection scheme. The offline processing procedure and results are presented. This scheme could be realized through simple structure and has potential applications in cost-effective high-speed laser communication.

Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.

PubMed

Lee, Jack; Zee, Benny Chung Ying; Li, Qing

2013-01-01

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.

[An automatic peak detection method for LIBS spectrum based on continuous wavelet transform].

PubMed

Chen, Peng-Fei; Tian, Di; Qiao, Shu-Jun; Yang, Guang

2014-07-01

Spectrum peak detection in the laser-induced breakdown spectroscopy (LIBS) is an essential step, but the presence of background and noise seriously disturb the accuracy of peak position. The present paper proposed a method applied to automatic peak detection for LIBS spectrum in order to enhance the ability of overlapping peaks searching and adaptivity. We introduced the ridge peak detection method based on continuous wavelet transform to LIBS, and discussed the choice of the mother wavelet and optimized the scale factor and the shift factor. This method also improved the ridge peak detection method with a correcting ridge method. The experimental results show that compared with other peak detection methods (the direct comparison method, derivative method and ridge peak search method), our method had a significant advantage on the ability to distinguish overlapping peaks and the precision of peak detection, and could be be applied to data processing in LIBS.

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy.

PubMed

Zhao, Yitian; Zheng, Yalin; Liu, Yonghuai; Yang, Jian; Zhao, Yifan; Chen, Duanduan; Wang, Yongtian

2017-01-01

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection.

Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals

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

Zhang, Y., E-mail: thuzhangyu@foxmail.com; Huang, S. L., E-mail: huangsling@tsinghua.edu.cn; Wang, S.

The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency formore » all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert–Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.« less

Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals.

PubMed

Zhang, Y; Huang, S L; Wang, S; Zhao, W

2016-05-01

The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency for all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert-Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

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

Abdulbaqi, Hayder Saad; Department of Physics, College of Education, University of Al-Qadisiya, Al-Qadisiya; Jafri, Mohd Zubir Mat

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introducemore » a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.« less

A novel visual saliency detection method for infrared video sequences

NASA Astrophysics Data System (ADS)

Wang, Xin; Zhang, Yuzhen; Ning, Chen

2017-12-01

Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the ;important; content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.

Allelic-based gene-gene interaction associated with quantitative traits.

PubMed

Jung, Jeesun; Sun, Bin; Kwon, Deukwoo; Koller, Daniel L; Foroud, Tatiana M

2009-05-01

Recent studies have shown that quantitative phenotypes may be influenced not only by multiple single nucleotide polymorphisms (SNPs) within a gene but also by the interaction between SNPs at unlinked genes. We propose a new statistical approach that can detect gene-gene interactions at the allelic level which contribute to the phenotypic variation in a quantitative trait. By testing for the association of allelic combinations at multiple unlinked loci with a quantitative trait, we can detect the SNP allelic interaction whether or not it can be detected as a main effect. Our proposed method assigns a score to unrelated subjects according to their allelic combination inferred from observed genotypes at two or more unlinked SNPs, and then tests for the association of the allelic score with a quantitative trait. To investigate the statistical properties of the proposed method, we performed a simulation study to estimate type I error rates and power and demonstrated that this allelic approach achieves greater power than the more commonly used genotypic approach to test for gene-gene interaction. As an example, the proposed method was applied to data obtained as part of a candidate gene study of sodium retention by the kidney. We found that this method detects an interaction between the calcium-sensing receptor gene (CaSR), the chloride channel gene (CLCNKB) and the Na, K, 2Cl cotransporter gene (CLC12A1) that contributes to variation in diastolic blood pressure.

Automatic Polyp Detection via A Novel Unified Bottom-up and Top-down Saliency Approach.

PubMed

Yuan, Yixuan; Li, Dengwang; Meng, Max Q-H

2017-07-31

In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To find the perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then a novel unified bottom-up and top-down saliency method is proposed to detect polyps. In the first stage, we propose a weak bottom-up (WBU) saliency map by fusing the contrast based saliency and object-center based saliency together. The contrast based saliency map highlights image parts that show different appearances compared with surrounding areas while the object-center based saliency map emphasizes the center of the salient object. In the second stage, a strong classifier with Multiple Kernel Boosting (MKB) is learned to calculate the strong top-down (STD) saliency map based on samples directly from the obtained multi-level WBU saliency maps. We finally integrate these two stage saliency maps from all levels together to highlight polyps. Experiment results achieve 0.818 recall for saliency calculation, validating the effectiveness of our method. Extensive experiments on public polyp datasets demonstrate that the proposed saliency algorithm performs favorably against state-of-the-art saliency methods to detect polyps.

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images

PubMed Central

Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

2017-01-01

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency. PMID:28640236

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images.

PubMed

Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

2017-06-22

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency.

A method based on multi-sensor data fusion for fault detection of planetary gearboxes.

PubMed

Lei, Yaguo; Lin, Jing; He, Zhengjia; Kong, Detong

2012-01-01

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

A hybrid approach for efficient anomaly detection using metaheuristic methods

PubMed Central

Ghanem, Tamer F.; Elkilani, Wail S.; Abdul-kader, Hatem M.

2014-01-01

Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms. PMID:26199752

A hybrid approach for efficient anomaly detection using metaheuristic methods.

PubMed

Ghanem, Tamer F; Elkilani, Wail S; Abdul-Kader, Hatem M

2015-07-01

Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms.

Novel face-detection method under various environments

NASA Astrophysics Data System (ADS)

Jing, Min-Quan; Chen, Ling-Hwei

2009-06-01

We propose a method to detect a face with different poses under various environments. On the basis of skin color information, skin regions are first extracted from an input image. Next, the shoulder part is cut out by using shape information and the head part is then identified as a face candidate. For a face candidate, a set of geometric features is applied to determine if it is a profile face. If not, then a set of eyelike rectangles extracted from the face candidate and the lighting distribution are used to determine if the face candidate is a nonprofile face. Experimental results show that the proposed method is robust under a wide range of lighting conditions, different poses, and races. The detection rate for the HHI face database is 93.68%. For the Champion face database, the detection rate is 95.15%.

Robust Curb Detection with Fusion of 3D-Lidar and Camera Data

PubMed Central

Tan, Jun; Li, Jian; An, Xiangjing; He, Hangen

2014-01-01

Curb detection is an essential component of Autonomous Land Vehicles (ALV), especially important for safe driving in urban environments. In this paper, we propose a fusion-based curb detection method through exploiting 3D-Lidar and camera data. More specifically, we first fuse the sparse 3D-Lidar points and high-resolution camera images together to recover a dense depth image of the captured scene. Based on the recovered dense depth image, we propose a filter-based method to estimate the normal direction within the image. Then, by using the multi-scale normal patterns based on the curb's geometric property, curb point features fitting the patterns are detected in the normal image row by row. After that, we construct a Markov Chain to model the consistency of curb points which utilizes the continuous property of the curb, and thus the optimal curb path which links the curb points together can be efficiently estimated by dynamic programming. Finally, we perform post-processing operations to filter the outliers, parameterize the curbs and give the confidence scores on the detected curbs. Extensive evaluations clearly show that our proposed method can detect curbs with strong robustness at real-time speed for both static and dynamic scenes. PMID:24854364

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

PubMed

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Development of a methodology for the detection of hospital financial outliers using information systems.

PubMed

Okada, Sachiko; Nagase, Keisuke; Ito, Ayako; Ando, Fumihiko; Nakagawa, Yoshiaki; Okamoto, Kazuya; Kume, Naoto; Takemura, Tadamasa; Kuroda, Tomohiro; Yoshihara, Hiroyuki

2014-01-01

Comparison of financial indices helps to illustrate differences in operations and efficiency among similar hospitals. Outlier data tend to influence statistical indices, and so detection of outliers is desirable. Development of a methodology for financial outlier detection using information systems will help to reduce the time and effort required, eliminate the subjective elements in detection of outlier data, and improve the efficiency and quality of analysis. The purpose of this research was to develop such a methodology. Financial outliers were defined based on a case model. An outlier-detection method using the distances between cases in multi-dimensional space is proposed. Experiments using three diagnosis groups indicated successful detection of cases for which the profitability and income structure differed from other cases. Therefore, the method proposed here can be used to detect outliers. Copyright © 2013 John Wiley & Sons, Ltd.

Ship detection in optical remote sensing images based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Zhao, Danpei; Cai, Bowen

2017-10-01

Automatic ship detection in optical remote sensing images has attracted wide attention for its broad applications. Major challenges for this task include the interference of cloud, wave, wake, and the high computational expenses. We propose a fast and robust ship detection algorithm to solve these issues. The framework for ship detection is designed based on deep convolutional neural networks (CNNs), which provide the accurate locations of ship targets in an efficient way. First, the deep CNN is designed to extract features. Then, a region proposal network (RPN) is applied to discriminate ship targets and regress the detection bounding boxes, in which the anchors are designed by intrinsic shape of ship targets. Experimental results on numerous panchromatic images demonstrate that, in comparison with other state-of-the-art ship detection methods, our method is more efficient and achieves higher detection accuracy and more precise bounding boxes in different complex backgrounds.

A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data

PubMed Central

Feng, Hao; Conneely, Karen N.; Wu, Hao

2014-01-01

DNA methylation is an important epigenetic modification that has essential roles in cellular processes including gene regulation, development and disease and is widely dysregulated in most types of cancer. Recent advances in sequencing technology have enabled the measurement of DNA methylation at single nucleotide resolution through methods such as whole-genome bisulfite sequencing and reduced representation bisulfite sequencing. In DNA methylation studies, a key task is to identify differences under distinct biological contexts, for example, between tumor and normal tissue. A challenge in sequencing studies is that the number of biological replicates is often limited by the costs of sequencing. The small number of replicates leads to unstable variance estimation, which can reduce accuracy to detect differentially methylated loci (DML). Here we propose a novel statistical method to detect DML when comparing two treatment groups. The sequencing counts are described by a lognormal-beta-binomial hierarchical model, which provides a basis for information sharing across different CpG sites. A Wald test is developed for hypothesis testing at each CpG site. Simulation results show that the proposed method yields improved DML detection compared to existing methods, particularly when the number of replicates is low. The proposed method is implemented in the Bioconductor package DSS. PMID:24561809

Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.

PubMed

You, Zhu-Hong; Li, Shuai; Gao, Xin; Luo, Xin; Ji, Zhen

2014-01-01

Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.

Prostate cancer detection: Fusion of cytological and textural features.

PubMed

Nguyen, Kien; Jain, Anil K; Sabata, Bikash

2011-01-01

A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification.

Prostate cancer detection: Fusion of cytological and textural features

PubMed Central

Nguyen, Kien; Jain, Anil K.; Sabata, Bikash

2011-01-01

A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification. PMID:22811959

Laser Generated Leaky Acoustic Waves for Needle Visualization.

PubMed

Wu, Kai-Wen; Wang, Yi-An; Li, Pai-Chi

2018-04-01

Ultrasound (US)-guided needle operation is usually used to visualize both tissue and needle position such as tissue biopsy and localized drug delivery. However, the transducer-needle orientation is limited due to reflection of the acoustic waves. We proposed a leaky acoustic wave method to visualize the needle position and orientation. Laser pulses are emitted on top of the needle to generate acoustic waves; then, these acoustic waves propagate along the needle surface. Leaky wave signals are detected by the US array transducer. The needle position can be calculated by phase velocities of two different wave modes and their corresponding emission angles. In our experiments, a series of needles was inserted into a tissue mimicking phantom and porcine tissue to evaluate the accuracy of the proposed method. The results show that the detection depth is up to 51 mm and the insertion angle is up to 40° with needles of different diameters. It is demonstrated that the proposed approach outperforms the conventional B-mode US-guided needle operation in terms of the detection range while achieving similar accuracy. The proposed method reveals the potentials for further clinical applications.

Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

PubMed Central

Al-Kaff, Abdulla; García, Fernando; Martín, David; De La Escalera, Arturo; Armingol, José María

2017-01-01

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works. PMID:28481277

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Proposal and Implementation of a Robust Sensing Method for DVB-T Signal

NASA Astrophysics Data System (ADS)

Song, Chunyi; Rahman, Mohammad Azizur; Harada, Hiroshi

This paper proposes a sensing method for TV signals of DVB-T standard to realize effective TV White Space (TVWS) Communication. In the TVWS technology trial organized by the Infocomm Development Authority (iDA) of Singapore, with regard to the sensing level and sensing time, detecting DVB-T signal at the level of -120dBm over an 8MHz channel with a sensing time below 1 second is required. To fulfill such a strict sensing requirement, we propose a smart sensing method which combines feature detection and energy detection (CFED), and is also characterized by using dynamic threshold selection (DTS) based on a threshold table to improve sensing robustness to noise uncertainty. The DTS based CFED (DTS-CFED) is evaluated by computer simulations and is also implemented into a hardware sensing prototype. The results show that the DTS-CFED achieves a detection probability above 0.9 for a target false alarm probability of 0.1 for DVB-T signals at the level of -120dBm over an 8MHz channel with the sensing time equals to 0.1 second.

Ground-based remote sensing of HDO/H2O ratio profiles: introduction and validation of an innovative retrieval approach

NASA Astrophysics Data System (ADS)

Schneider, M.; Hase, F.; Blumenstock, T.

2006-10-01

We propose an innovative approach for analysing ground-based FTIR spectra which allows us to detect variabilities of lower and middle/upper tropospheric HDO/H2O ratios. We show that the proposed method is superior to common approaches. We estimate that lower tropospheric HDO/H2O ratios can be detected with a noise to signal ratio of 15% and middle/upper tropospheric ratios with a noise to signal ratio of 50%. The method requires the inversion to be performed on a logarithmic scale and to introduce an inter-species constraint. While common methods calculate the isotope ratio posterior to an independent, optimal estimation of the HDO and H2O profile, the proposed approach is an optimal estimator for the ratio itself. We apply the innovative approach to spectra measured continuously during 15 months and present, for the first time, an annual cycle of tropospheric HDO/H2O ratio profiles as detected by ground-based measurements. Outliers in the detected middle/upper tropospheric ratios are interpreted by backward trajectories.

Ground-based remote sensing of HDO/H2O ratio profiles: introduction and validation of an innovative retrieval approach

NASA Astrophysics Data System (ADS)

Schneider, M.; Hase, F.; Blumenstock, T.

2006-06-01

We propose an innovative approach for analysing ground-based FTIR spectra which allows us to detect variabilities of lower and middle/upper tropospheric HDO/H2O ratios. We show that the proposed method is superior to common approaches. We estimate that lower tropospheric HDO/H2O ratios can be detected with a noise to signal ratio of 15% and middle/upper tropospheric ratios with a noise to signal ratio of 50%. The method requires the inversion to be performed on a logarithmic scale and to introduce an inter-species constraint. While common methods calculate the isotope ratio posterior to an independent, optimal estimation of the HDO and H2O profile, the proposed approach is an optimal estimator for the ratio itself. We apply the innovative approach to spectra measured continuously during 15 months and present, for the first time, an annual cycle of tropospheric HDO/H2O ratio profiles as detected by ground-based measurements. Outliers in the detected middle/upper tropospheric ratios are interpreted by backward trajectories.

Damage detection methodology on beam-like structures based on combined modal Wavelet Transform strategy

NASA Astrophysics Data System (ADS)

Serra, Roger; Lopez, Lautaro

2018-05-01

Different approaches on the detection of damages based on dynamic measurement of structures have appeared in the last decades. They were based, amongst others, on changes in natural frequencies, modal curvatures, strain energy or flexibility. Wavelet analysis has also been used to detect the abnormalities on modal shapes induced by damages. However the majority of previous work was made with non-corrupted by noise signals. Moreover, the damage influence for each mode shape was studied separately. This paper proposes a new methodology based on combined modal wavelet transform strategy to cope with noisy signals, while at the same time, able to extract the relevant information from each mode shape. The proposed methodology will be then compared with the most frequently used and wide-studied methods from the bibliography. To evaluate the performance of each method, their capacity to detect and localize damage will be analyzed in different cases. The comparison will be done by simulating the oscillations of a cantilever steel beam with and without defect as a numerical case. The proposed methodology proved to outperform classical methods in terms of noisy signals.

Colitis detection on abdominal CT scans by rich feature hierarchies

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Lay, Nathan; Wei, Zhuoshi; Lu, Le; Kim, Lauren; Turkbey, Evrim; Summers, Ronald M.

2016-03-01

Colitis is inflammation of the colon due to neutropenia, inflammatory bowel disease (such as Crohn disease), infection and immune compromise. Colitis is often associated with thickening of the colon wall. The wall of a colon afflicted with colitis is much thicker than normal. For example, the mean wall thickness in Crohn disease is 11-13 mm compared to the wall of the normal colon that should measure less than 3 mm. Colitis can be debilitating or life threatening, and early detection is essential to initiate proper treatment. In this work, we apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals to detect potential colitis on CT scans. Our method first generates around 3000 category-independent region proposals for each slice of the input CT scan using selective search. Then, a fixed-length feature vector is extracted from each region proposal using a CNN. Finally, each region proposal is classified and assigned a confidence score with linear SVMs. We applied the detection method to 260 images from 26 CT scans of patients with colitis for evaluation. The detection system can achieve 0.85 sensitivity at 1 false positive per image.

Imaging of underground karst water channels using an improved multichannel transient Rayleigh wave detecting method

PubMed Central

Zheng, Xuhui; Liu, Lei; Li, Gao; Zhou, Fubiao; Xu, Jiemin

2018-01-01

Geological and hydrogeological conditions in karst areas are complicated from the viewpoint of engineering. The construction of underground structures in these areas is often disturbed by the gushing of karst water, which may delay the construction schedule, result in economic losses, and even cause heavy casualties. In this paper, an innovative method of multichannel transient Rayleigh wave detecting is proposed by introducing the concept of arrival time difference phase between channels (TDP). Overcoming the restriction of the space-sampling law, the proposed method can extract the phase velocities of different frequency components from only two channels of transient Rayleigh wave recorded on two adjacent detecting points. This feature greatly improves the work efficiency and lateral resolution of transient Rayleigh wave detecting. The improved multichannel transient Rayleigh wave detecting method is applied to the detection of karst caves and fractures in rock mass of the foundation pit of Yan’an Road Station of Guiyang Metro. The imaging of the detecting results clearly reveals the distribution of karst water inflow channels, which provided significant guidance for water plugging and enabled good control over karst water gushing in the foundation pit. PMID:29883492

Imaging of underground karst water channels using an improved multichannel transient Rayleigh wave detecting method.

PubMed

Zheng, Xuhui; Liu, Lei; Sun, Jinzhong; Li, Gao; Zhou, Fubiao; Xu, Jiemin

2018-01-01

Geological and hydrogeological conditions in karst areas are complicated from the viewpoint of engineering. The construction of underground structures in these areas is often disturbed by the gushing of karst water, which may delay the construction schedule, result in economic losses, and even cause heavy casualties. In this paper, an innovative method of multichannel transient Rayleigh wave detecting is proposed by introducing the concept of arrival time difference phase between channels (TDP). Overcoming the restriction of the space-sampling law, the proposed method can extract the phase velocities of different frequency components from only two channels of transient Rayleigh wave recorded on two adjacent detecting points. This feature greatly improves the work efficiency and lateral resolution of transient Rayleigh wave detecting. The improved multichannel transient Rayleigh wave detecting method is applied to the detection of karst caves and fractures in rock mass of the foundation pit of Yan'an Road Station of Guiyang Metro. The imaging of the detecting results clearly reveals the distribution of karst water inflow channels, which provided significant guidance for water plugging and enabled good control over karst water gushing in the foundation pit.

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video

PubMed Central

Ghosh, Tonmoy; Wahid, Khan A.

2018-01-01

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data. PMID:29468094

Locally adaptive decision in detection of clustered microcalcifications in mammograms.

PubMed

Sainz de Cea, María V; Nishikawa, Robert M; Yang, Yongyi

2018-02-15

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10 -4 ). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Locally adaptive decision in detection of clustered microcalcifications in mammograms

NASA Astrophysics Data System (ADS)

Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi

2018-02-01

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10-4). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera.

PubMed

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-10-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

PubMed Central

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-01-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods. PMID:28974031

Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces.

PubMed

Abu-Alqumsan, Mohammad; Peer, Angelika

2016-06-01

Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation

PubMed Central

2018-01-01

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user’s subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated. PMID:29316731

A simple method for the detection of PM2.5 air pollutions using MODIS data

NASA Astrophysics Data System (ADS)

Kato, Yoshinobu

2016-05-01

In recent years, PM2.5 air pollution is a social and transboundary environmental issue with the rapid economic growth in many countries. As PM2.5 is small and includes various ingredients, the detection of PM2.5 air pollutions by using satellite data is difficult compared with the detection of dust and sandstorms. In this paper, we examine various images (i.e., single-band images, band-difference images, RGB composite color images) to find a good method for detecting PM2.5 air pollutions by using MODIS data. A good method for the detection of PM2.5 air pollution is {R, G, B = band10, band9, T11}, where T11 is the brightness temperature of band31. In this composite color image, PM2.5 air pollutions are represented by light purple or pink color. This proposed method is simpler than the method by Nagatani et al. (2013), and is useful to grasp the distribution of PM2.5 air pollutions in the wide area (e.g., from China and India to Japan). By comparing AVI image with the image by proposed method, DSS and PM2.5 air pollutions can be classified.

Text Line Detection from Rectangle Traffic Panels of Natural Scene

NASA Astrophysics Data System (ADS)

Wang, Shiyuan; Huang, Linlin; Hu, Jian

2018-01-01

Traffic sign detection and recognition is very important for Intelligent Transportation. Among traffic signs, traffic panel contains rich information. However, due to low resolution and blur in the rectangular traffic panel, it is difficult to extract the character and symbols. In this paper, we propose a coarse-to-fine method to detect the Chinese character on traffic panels from natural scenes. Given a traffic panel Color Quantization is applied to extract candidate regions of Chinese characters. Second, a multi-stage filter based on learning is applied to discard the non-character regions. Third, we aggregate the characters for text lines by Distance Metric Learning method. Experimental results on real traffic images from Baidu Street View demonstrate the effectiveness of the proposed method.

Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

NASA Astrophysics Data System (ADS)

Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

2018-06-01

Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images.

PubMed

Kaya, Sertan; Bayraktar, Mustafa; Kockara, Sinan; Mete, Mutlu; Halic, Tansel; Field, Halle E; Wong, Henry K

2016-10-06

Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy images. Among different color spaces tested, RGB color space's blue color channel is the most informative color channel to detect malignancy for skin lesions. That is followed by YCbCr color spaces Cr channel, and Cr is closely followed by the green color channel of RGB color space.

[Rapid detection of caffeine in blood by freeze-out extraction].

PubMed

Bekhterev, V N; Gavrilova, S N; Kozina, E P; Maslakov, I V

2010-01-01

A new method for the detection of caffeine in blood has been proposed based on the combination of extraction and freezing-out to eliminate the influence of sample matrix. Metrological characteristics of the method are presented. Selectivity of detection is achieved by optimal conditions of analysis by high performance liquid chromatography. The method is technically simple and cost-efficient, it ensures rapid performance of the studies.

Precisely detecting atomic position of atomic intensity images.

PubMed

Wang, Zhijun; Guo, Yaolin; Tang, Sai; Li, Junjie; Wang, Jincheng; Zhou, Yaohe

2015-03-01

We proposed a quantitative method to detect atomic position in atomic intensity images from experiments such as high-resolution transmission electron microscopy, atomic force microscopy, and simulation such as phase field crystal modeling. The evaluation of detection accuracy proves the excellent performance of the method. This method provides a chance to precisely determine atomic interactions based on the detected atomic positions from the atomic intensity image, and hence to investigate the related physical, chemical and electrical properties. Copyright © 2014 Elsevier B.V. All rights reserved.

Automated exploitation of sky polarization imagery.

PubMed

Sadjadi, Firooz A; Chun, Cornell S L

2018-03-10

We propose an automated method for detecting neutral points in the sunlit sky. Until now, detecting these singularities has been done manually. Results are presented that document the application of this method on a limited number of polarimetric images of the sky captured with a camera and rotating polarizer. The results are significant because a method for automatically detecting the neutral points may aid in the determination of the solar position when the sun is obscured and may have applications in meteorology and pollution detection and characterization.

Comparison of proposed survey procedures for detection of forest carnivores

Treesearch

Kerry R. Foresman; Dean E. Pearson

1998-01-01

American marten (Martes americana), fisher (M. pennanti), wolverine (Gulo gulo), and lynx (Lynx lynx) are forest carnivores believed threatened by disturbance of late-successional forests. To manage forested ecosystems for these species, effective methods for their detection must be available. Recently, the U.S. Forest Service proposed standardized survey procedures...

Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.

PubMed

Ahmad, Asif; Asif, Amina; Rajpoot, Nasir; Arif, Muhammad; Minhas, Fayyaz Ul Amir Afsar

2017-11-21

Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware. A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL: http://faculty.pieas.edu.pk/fayyaz/software.html#corehist .

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

PubMed

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

2016-10-01

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

PubMed Central

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

2016-01-01

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses. PMID:27706086

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

Damage detection of structures with detrended fluctuation and detrended cross-correlation analyses

NASA Astrophysics Data System (ADS)

Lin, Tzu-Kang; Fajri, Haikal

2017-03-01

Recently, fractal analysis has shown its potential for damage detection and assessment in fields such as biomedical and mechanical engineering. For its practicability in interpreting irregular, complex, and disordered phenomena, a structural health monitoring (SHM) system based on detrended fluctuation analysis (DFA) and detrended cross-correlation analysis (DCCA) is proposed. First, damage conditions can be swiftly detected by evaluating ambient vibration signals measured from a structure through DFA. Damage locations can then be determined by analyzing the cross correlation of signals of different floors by applying DCCA. A damage index is also proposed based on multi-scale DCCA curves to improve the damage location accuracy. To verify the performance of the proposed SHM system, a four-story numerical model was used to simulate various damage conditions with different noise levels. Furthermore, an experimental verification was conducted on a seven-story benchmark structure to assess the potential damage. The results revealed that the DFA method could detect the damage conditions satisfactorily, and damage locations can be identified through the DCCA method with an accuracy of 75%. Moreover, damage locations can be correctly assessed by the damage index method with an improved accuracy of 87.5%. The proposed SHM system has promising application in practical implementations.

Shilling attack detection for recommender systems based on credibility of group users and rating time series.

PubMed

Zhou, Wei; Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user's credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.

A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones.

PubMed

Kang, Xiaomin; Huang, Baoqi; Qi, Guodong

2018-01-19

Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper, a novel algorithm is proposed to simultaneously detect walking motion and count steps through unconstrained smartphones in the sense that the smartphone placement is not only arbitrary but also alterable. On account of the periodicity of the walking motion and sensitivity of gyroscopes, the proposed algorithm extracts the frequency domain features from three-dimensional (3D) angular velocities of a smartphone through FFT (fast Fourier transform) and identifies whether its holder is walking or not irrespective of its placement. Furthermore, the corresponding step frequency is recursively updated to evaluate the step count in real time. Extensive experiments are conducted by involving eight subjects and different walking scenarios in a realistic environment. It is shown that the proposed method achieves the precision of 93.76 % and recall of 93.65 % for walking detection, and its overall performance is significantly better than other well-known methods. Moreover, the accuracy of step counting by the proposed method is 95.74 % , and is better than both of the several well-known counterparts and commercial products.

Shilling attack detection for recommender systems based on credibility of group users and rating time series

PubMed Central

Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method. PMID:29742134

Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model

NASA Astrophysics Data System (ADS)

Liu, Sijia; Sa, Ruhan; Maguire, Orla; Minderman, Hans; Chaudhary, Vipin

2015-03-01

Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between spot count. As a solution of counting error from overlapping spots, in this study, a Gaussian Mixture Model based classification method is proposed. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) of GMM are used as global image features of this classification method. Via Random Forest classifier, the result shows that the proposed method is able to detect closely overlapping spots which cannot be separated by existing image segmentation based spot detection methods. The experiment results show that by the proposed method we can obtain a significant improvement in spot counting accuracy.

Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.

PubMed

Tripathy, R K; Dandapat, S

2016-06-01

The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.

Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

NASA Astrophysics Data System (ADS)

Avci, Onur; Abdeljaber, Osama; Kiranyaz, Serkan; Hussein, Mohammed; Inman, Daniel J.

2018-06-01

Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural Damage Detection (SDD) approaches available in the SHM literature are centralized as they require transferring data from all sensors within the network to a single processing unit to evaluate the structural condition. These methods are found predominantly feasible for wired SHM systems; however, transmission and synchronization of huge data sets in WSNs has been found to be arduous. As such, the application of centralized methods with WSNs has been a challenge for engineers. In this paper, the authors are presenting a novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes. The SDD is successfully performed completely wireless and real-time under ambient conditions. As a result of this, a decentralized damage detection method suitable for wireless SHM systems is proposed. The proposed method is based on 1D CNNs and it involves training an individual 1D CNN for each wireless sensor in the network in a format where each CNN is assigned to process the locally-available data only, eliminating the need for data transmission and synchronization. The proposed damage detection method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing. Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block. This ability is prevailingly cost-effective and evidently practical in WSNs considering the hardware systems have been occasionally reported to suffer from limited power supply in these networks. To display the capability and verify the success of the proposed method, large-scale experiments conducted on a laboratory structure equipped with a state-of-the-art WSN are reported.

An adaptive field detection method for bridge scour monitoring using motion-sensing radio transponders (RFIDs).

DOT National Transportation Integrated Search

2014-01-01

A comprehensive field detection method is proposed that is aimed at developing advanced capability for : reliable monitoring, inspection and life estimation of bridge infrastructure. The goal is to utilize Motion-Sensing Radio Transponders (RFIDS) on...

Fringe order correction for the absolute phase recovered by two selected spatial frequency fringe projections in fringe projection profilometry.

PubMed

Ding, Yi; Peng, Kai; Yu, Miao; Lu, Lei; Zhao, Kun

2017-08-01

The performance of the two selected spatial frequency phase unwrapping methods is limited by a phase error bound beyond which errors will occur in the fringe order leading to a significant error in the recovered absolute phase map. In this paper, we propose a method to detect and correct the wrong fringe orders. Two constraints are introduced during the fringe order determination of two selected spatial frequency phase unwrapping methods. A strategy to detect and correct the wrong fringe orders is also described. Compared with the existing methods, we do not need to estimate the threshold associated with absolute phase values to determine the fringe order error, thus making it more reliable and avoiding the procedure of search in detecting and correcting successive fringe order errors. The effectiveness of the proposed method is validated by the experimental results.

Correlation matching method for high-precision position detection of optical vortex using Shack-Hartmann wavefront sensor.

PubMed

Huang, Chenxi; Huang, Hongxin; Toyoda, Haruyoshi; Inoue, Takashi; Liu, Huafeng

2012-11-19

We propose a new method for realizing high-spatial-resolution detection of singularity points in optical vortex beams. The method uses a Shack-Hartmann wavefront sensor (SHWS) to record a Hartmanngram. A map of evaluation values related to phase slope is then calculated from the Hartmanngram. The position of an optical vortex is determined by comparing the map with reference maps that are calculated from numerically created spiral phases having various positions. Optical experiments were carried out to verify the method. We displayed various spiral phase distribution patterns on a phase-only spatial light modulator and measured the resulting singularity point using the proposed method. The results showed good linearity in detecting the position of singularity points. The RMS error of the measured position of the singularity point was approximately 0.056, in units normalized to the lens size of the lenslet array used in the SHWS.

A Novel Method for Block Size Forensics Based on Morphological Operations

NASA Astrophysics Data System (ADS)

Luo, Weiqi; Huang, Jiwu; Qiu, Guoping

Passive forensics analysis aims to find out how multimedia data is acquired and processed without relying on pre-embedded or pre-registered information. Since most existing compression schemes for digital images are based on block processing, one of the fundamental steps for subsequent forensics analysis is to detect the presence of block artifacts and estimate the block size for a given image. In this paper, we propose a novel method for blind block size estimation. A 2×2 cross-differential filter is first applied to detect all possible block artifact boundaries, morphological operations are then used to remove the boundary effects caused by the edges of the actual image contents, and finally maximum-likelihood estimation (MLE) is employed to estimate the block size. The experimental results evaluated on over 1300 nature images show the effectiveness of our proposed method. Compared with existing gradient-based detection method, our method achieves over 39% accuracy improvement on average.

Automatic correspondence detection in mammogram and breast tomosynthesis images

NASA Astrophysics Data System (ADS)

Ehrhardt, Jan; Krüger, Julia; Bischof, Arpad; Barkhausen, Jörg; Handels, Heinz

2012-02-01

Two-dimensional mammography is the major imaging modality in breast cancer detection. A disadvantage of mammography is the projective nature of this imaging technique. Tomosynthesis is an attractive modality with the potential to combine the high contrast and high resolution of digital mammography with the advantages of 3D imaging. In order to facilitate diagnostics and treatment in the current clinical work-flow, correspondences between tomosynthesis images and previous mammographic exams of the same women have to be determined. In this paper, we propose a method to detect correspondences in 2D mammograms and 3D tomosynthesis images automatically. In general, this 2D/3D correspondence problem is ill-posed, because a point in the 2D mammogram corresponds to a line in the 3D tomosynthesis image. The goal of our method is to detect the "most probable" 3D position in the tomosynthesis images corresponding to a selected point in the 2D mammogram. We present two alternative approaches to solve this 2D/3D correspondence problem: a 2D/3D registration method and a 2D/2D mapping between mammogram and tomosynthesis projection images with a following back projection. The advantages and limitations of both approaches are discussed and the performance of the methods is evaluated qualitatively and quantitatively using a software phantom and clinical breast image data. Although the proposed 2D/3D registration method can compensate for moderate breast deformations caused by different breast compressions, this approach is not suitable for clinical tomosynthesis data due to the limited resolution and blurring effects perpendicular to the direction of projection. The quantitative results show that the proposed 2D/2D mapping method is capable of detecting corresponding positions in mammograms and tomosynthesis images automatically for 61 out of 65 landmarks. The proposed method can facilitate diagnosis, visual inspection and comparison of 2D mammograms and 3D tomosynthesis images for the physician.

Posture Detection Based on Smart Cushion for Wheelchair Users

PubMed Central

Ma, Congcong; Li, Wenfeng; Gravina, Raffaele; Fortino, Giancarlo

2017-01-01

The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user’s posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques—Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)—are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment. PMID:28353684

Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

NASA Astrophysics Data System (ADS)

Wang, Gaochao; Tse, Peter W.; Yuan, Maodan

2018-02-01

Visual inspection and assessment of the condition of metal structures are essential for safety. Pulse thermography produces visible infrared images, which have been widely applied to detect and characterize defects in structures and materials. When active thermography, a non-destructive testing tool, is applied, the necessity of considerable manual checking can be avoided. However, detecting an internal crack with active thermography remains difficult, since it is usually invisible in the collected sequence of infrared images, which makes the automatic detection of internal cracks even harder. In addition, the detection of an internal crack can be hindered by a complicated inspection environment. With the purpose of putting forward a robust and automatic visual inspection method, a computer vision-based thresholding method is proposed. In this paper, the image signals are a sequence of infrared images collected from the experimental setup with a thermal camera and two flash lamps as stimulus. The contrast of pixels in each frame is enhanced by the Canny operator and then reconstructed by a triple-threshold system. Two features, mean value in the time domain and maximal amplitude in the frequency domain, are extracted from the reconstructed signal to help distinguish the crack pixels from others. Finally, a binary image indicating the location of the internal crack is generated by a K-means clustering method. The proposed procedure has been applied to an iron pipe, which contains two internal cracks and surface abrasion. Some improvements have been made for the computer vision-based automatic crack detection methods. In the future, the proposed method can be applied to realize the automatic detection of internal cracks from many infrared images for the industry.

Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location--a 3-D automatic approach.

PubMed

Shen, Shan; Szameitat, André J; Sterr, Annette

2008-07-01

Detection of infarct lesions using traditional segmentation methods is always problematic due to intensity similarity between lesions and normal tissues, so that multispectral MRI modalities were often employed for this purpose. However, the high costs of MRI scan and the severity of patient conditions restrict the collection of multiple images. Therefore, in this paper, a new 3-D automatic lesion detection approach was proposed, which required only a single type of anatomical MRI scan. It was developed on a theory that, when lesions were present, the voxel-intensity-based segmentation and the spatial-location-based tissue distribution should be inconsistent in the regions of lesions. The degree of this inconsistency was calculated, which indicated the likelihood of tissue abnormality. Lesions were identified when the inconsistency exceeded a defined threshold. In this approach, the intensity-based segmentation was implemented by the conventional fuzzy c-mean (FCM) algorithm, while the spatial location of tissues was provided by prior tissue probability maps. The use of simulated MRI lesions allowed us to quantitatively evaluate the performance of the proposed method, as the size and location of lesions were prespecified. The results showed that our method effectively detected lesions with 40-80% signal reduction compared to normal tissues (similarity index > 0.7). The capability of the proposed method in practice was also demonstrated on real infarct lesions from 15 stroke patients, where the lesions detected were in broad agreement with true lesions. Furthermore, a comparison to a statistical segmentation approach presented in the literature suggested that our 3-D lesion detection approach was more reliable. Future work will focus on adapting the current method to multiple sclerosis lesion detection.

An incremental community detection method for social tagging systems using locality-sensitive hashing.

PubMed

Wu, Zhenyu; Zou, Ming

2014-10-01

An increasing number of users interact, collaborate, and share information through social networks. Unprecedented growth in social networks is generating a significant amount of unstructured social data. From such data, distilling communities where users have common interests and tracking variations of users' interests over time are important research tracks in fields such as opinion mining, trend prediction, and personalized services. However, these tasks are extremely difficult considering the highly dynamic characteristics of the data. Existing community detection methods are time consuming, making it difficult to process data in real time. In this paper, dynamic unstructured data is modeled as a stream. Tag assignments stream clustering (TASC), an incremental scalable community detection method, is proposed based on locality-sensitive hashing. Both tags and latent interactions among users are incorporated in the method. In our experiments, the social dynamic behaviors of users are first analyzed. The proposed TASC method is then compared with state-of-the-art clustering methods such as StreamKmeans and incremental k-clique; results indicate that TASC can detect communities more efficiently and effectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Regional fringe analysis for improving depth measurement in phase-shifting fringe projection profilometry

NASA Astrophysics Data System (ADS)

Chien, Kuang-Che Chang; Tu, Han-Yen; Hsieh, Ching-Huang; Cheng, Chau-Jern; Chang, Chun-Yen

2018-01-01

This study proposes a regional fringe analysis (RFA) method to detect the regions of a target object in captured shifted images to improve depth measurement in phase-shifting fringe projection profilometry (PS-FPP). In the RFA method, region-based segmentation is exploited to segment the de-fringed image of a target object, and a multi-level fuzzy-based classification with five presented features is used to analyze and discriminate the regions of an object from the segmented regions, which were associated with explicit fringe information. Then, in the experiment, the performance of the proposed method is tested and evaluated on 26 test cases made of five types of materials. The qualitative and quantitative results demonstrate that the proposed RFA method can effectively detect the desired regions of an object to improve depth measurement in the PS-FPP system.

Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

DOE PAGES

Tripathi, Ashish; McNulty, Ian; Munson, Todd; ...

2016-10-14

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

An image-based automatic recognition method for the flowering stage of maize

NASA Astrophysics Data System (ADS)

Yu, Zhenghong; Zhou, Huabing; Li, Cuina

2018-03-01

In this paper, we proposed an image-based approach for automatic recognizing the flowering stage of maize. A modified HOG/SVM detection framework is first adopted to detect the ears of maize. Then, we use low-rank matrix recovery technology to precisely extract the ears at pixel level. At last, a new feature called color gradient histogram, as an indicator, is proposed to determine the flowering stage. Comparing experiment has been carried out to testify the validity of our method and the results indicate that our method can meet the demand for practical observation.

A Novel Technique to Detect Code for SAC-OCDMA System

NASA Astrophysics Data System (ADS)

Bharti, Manisha; Kumar, Manoj; Sharma, Ajay K.

2018-04-01

The main task of optical code division multiple access (OCDMA) system is the detection of code used by a user in presence of multiple access interference (MAI). In this paper, new method of detection known as XOR subtraction detection for spectral amplitude coding OCDMA (SAC-OCDMA) based on double weight codes has been proposed and presented. As MAI is the main source of performance deterioration in OCDMA system, therefore, SAC technique is used in this paper to eliminate the effect of MAI up to a large extent. A comparative analysis is then made between the proposed scheme and other conventional detection schemes used like complimentary subtraction detection, AND subtraction detection and NAND subtraction detection. The system performance is characterized by Q-factor, BER and received optical power (ROP) with respect to input laser power and fiber length. The theoretical and simulation investigations reveal that the proposed detection technique provides better quality factor, security and received power in comparison to other conventional techniques. The wide opening of eye in case of proposed technique also proves its robustness.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

PubMed

Aghamohammadi, Amirhossein; Ang, Mei Choo; A Sundararajan, Elankovan; Weng, Ng Kok; Mogharrebi, Marzieh; Banihashem, Seyed Yashar

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

PubMed Central

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods. PMID:29438421

Max-AUC Feature Selection in Computer-Aided Detection of Polyps in CT Colonography

PubMed Central

Xu, Jian-Wu; Suzuki, Kenji

2014-01-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level. PMID:24608058

Max-AUC feature selection in computer-aided detection of polyps in CT colonography.

PubMed

Xu, Jian-Wu; Suzuki, Kenji

2014-03-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.

Label-free and ultrasensitive fluorescence detection of cocaine based on a strategy that utilizes DNA-templated silver nanoclusters and the nicking endonuclease-assisted signal amplification method.

PubMed

Zhang, Kai; Wang, Ke; Zhu, Xue; Zhang, Jue; Xu, Lan; Huang, Biao; Xie, Minhao

2014-01-07

A general and reliable strategy for the detection of cocaine was proposed utilizing DNA-templated silver nanoclusters as signal indicators and the nicking endonuclease-assisted signal amplification method. This strategy can detect cocaine specifically with a detection limit as low as 2 nM by using a small volume of 5 μL.

An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection.

PubMed

M, Soorya; Issac, Ashish; Dutta, Malay Kishore

2018-02-01

Glaucoma is an ocular disease which can cause irreversible blindness. The disease is currently identified using specialized equipment operated by optometrists manually. The proposed work aims to provide an efficient imaging solution which can help in automating the process of Glaucoma diagnosis using computer vision techniques from digital fundus images. The proposed method segments the optic disc using a geometrical feature based strategic framework which improves the detection accuracy and makes the algorithm invariant to illumination and noise. Corner thresholding and point contour joining based novel methods are proposed to construct smooth contours of Optic Disc. Based on a clinical approach as used by ophthalmologist, the proposed algorithm tracks blood vessels inside the disc region and identifies the points at which first vessel bend from the optic disc boundary and connects them to obtain the contours of Optic Cup. The proposed method has been compared with the ground truth marked by the medical experts and the similarity parameters, used to determine the performance of the proposed method, have yield a high similarity of segmentation. The proposed method has achieved a macro-averaged f-score of 0.9485 and accuracy of 97.01% in correctly classifying fundus images. The proposed method is clinically significant and can be used for Glaucoma screening over a large population which will work in a real time. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization.

PubMed

Tan, Weng Chun; Mat Isa, Nor Ashidi

2016-01-01

In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm.

Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox

NASA Astrophysics Data System (ADS)

Zhao, Ming; Jia, Xiaodong; Lin, Jing; Lei, Yaguo; Lee, Jay

2018-01-01

In modern rotating machinery, rotary encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the encoder signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLPD) is proposed to estimate the instantaneous angular speed (IAS) of rotating machines based on the encoder signal. Compared with the central difference method, the KLPD is more robust to noise and it is able to precisely capture the weak speed jitters introduced by mechanical defects. The fault diagnosis of planetary gearbox has proven to be a challenging issue in both industry and academia. Based on the proposed KLPD, a systematic method for the fault diagnosis of planetary gearbox is proposed. In this method, residual time synchronous time averaging (RTSA) is first employed to remove the operation-related IAS components that come from normal gear meshing and non-stationary load variations, KLPD is then utilized to detect and enhance the speed jitter from the IAS residual in a data-driven manner. The effectiveness of proposed method has been validated by both simulated data and experimental data. The results demonstrate that the proposed KLPD-RTSA could not only detect fault signatures but also identify defective components, thus providing a promising tool for the health monitoring of planetary gearbox.

Study of comparison between Ultra-high Frequency (UHF) method and ultrasonic method on PD detection for GIS

NASA Astrophysics Data System (ADS)

Li, Yanran; Chen, Duo; Li, Li; Zhang, Jiwei; Li, Guang; Liu, Hongxia

2017-11-01

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. However, few studies have been conducted on comparison of this two methods. From the view point of safety, it is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. This paper presents study aimed at clarifying the effect of UHF method and ultrasonic method for partial discharge caused by free metal particles in GIS. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for UHF method and ultrasonic method. A new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of detection localization.

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter.

PubMed

Choi, Jihoon; Shin, Joonho; Song, Choonggeun; Han, Suyong; Park, Doo Il

2017-09-13

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter

PubMed Central

Shin, Joonho; Song, Choonggeun; Han, Suyong; Park, Doo Il

2017-01-01

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected. PMID:28902154

Automatic draft reading based on image processing

NASA Astrophysics Data System (ADS)

Tsujii, Takahiro; Yoshida, Hiromi; Iiguni, Youji

2016-10-01

In marine transportation, a draft survey is a means to determine the quantity of bulk cargo. Automatic draft reading based on computer image processing has been proposed. However, the conventional draft mark segmentation may fail when the video sequence has many other regions than draft marks and a hull, and the estimated waterline is inherently higher than the true one. To solve these problems, we propose an automatic draft reading method that uses morphological operations to detect draft marks and estimate the waterline for every frame with Canny edge detection and a robust estimation. Moreover, we emulate surveyors' draft reading process for getting the understanding of a shipper and a receiver. In an experiment in a towing tank, the draft reading error of the proposed method was <1 cm, showing the advantage of the proposed method. It is also shown that accurate draft reading has been achieved in a real-world scene.

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.

PubMed

Guo, Xuan; Meng, Yu; Yu, Ning; Pan, Yi

2014-04-10

Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering

PubMed Central

2014-01-01

Backgroud Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. Results In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Conclusions Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS. PMID:24717145

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2017-02-01

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor.

PubMed

Naqvi, Rizwan Ali; Arsalan, Muhammad; Batchuluun, Ganbayar; Yoon, Hyo Sik; Park, Kang Ryoung

2018-02-03

A paradigm shift is required to prevent the increasing automobile accident deaths that are mostly due to the inattentive behavior of drivers. Knowledge of gaze region can provide valuable information regarding a driver's point of attention. Accurate and inexpensive gaze classification systems in cars can improve safe driving. However, monitoring real-time driving behaviors and conditions presents some challenges: dizziness due to long drives, extreme lighting variations, glasses reflections, and occlusions. Past studies on gaze detection in cars have been chiefly based on head movements. The margin of error in gaze detection increases when drivers gaze at objects by moving their eyes without moving their heads. To solve this problem, a pupil center corneal reflection (PCCR)-based method has been considered. However, the error of accurately detecting the pupil center and corneal reflection center is increased in a car environment due to various environment light changes, reflections on glasses surface, and motion and optical blurring of captured eye image. In addition, existing PCCR-based methods require initial user calibration, which is difficult to perform in a car environment. To address this issue, we propose a deep learning-based gaze detection method using a near-infrared (NIR) camera sensor considering driver head and eye movement that does not require any initial user calibration. The proposed system is evaluated on our self-constructed database as well as on open Columbia gaze dataset (CAVE-DB). The proposed method demonstrated greater accuracy than the previous gaze classification methods.