Palmprint Recognition Across Different Devices.

PubMed

Jia, Wei; Hu, Rong-Xiang; Gui, Jie; Zhao, Yang; Ren, Xiao-Ming

2012-01-01

In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD.

Palmprint Recognition across Different Devices

PubMed Central

Jia, Wei; Hu, Rong-Xiang; Gui, Jie; Zhao, Yang; Ren, Xiao-Ming

2012-01-01

In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD. PMID:22969380

A real time mobile-based face recognition with fisherface methods

NASA Astrophysics Data System (ADS)

Arisandi, D.; Syahputra, M. F.; Putri, I. L.; Purnamawati, S.; Rahmat, R. F.; Sari, P. P.

2018-03-01

Face Recognition is a field research in Computer Vision that study about learning face and determine the identity of the face from a picture sent to the system. By utilizing this face recognition technology, learning process about people’s identity between students in a university will become simpler. With this technology, student won’t need to browse student directory in university’s server site and look for the person with certain face trait. To obtain this goal, face recognition application use image processing methods consist of two phase, pre-processing phase and recognition phase. In pre-processing phase, system will process input image into the best image for recognition phase. Purpose of this pre-processing phase is to reduce noise and increase signal in image. Next, to recognize face phase, we use Fisherface Methods. This methods is chosen because of its advantage that would help system of its limited data. Therefore from experiment the accuracy of face recognition using fisherface is 90%.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

PubMed Central

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

PubMed

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Critical object recognition in millimeter-wave images with robustness to rotation and scale.

PubMed

Mohammadzade, Hoda; Ghojogh, Benyamin; Faezi, Sina; Shabany, Mahdi

2017-06-01

Locating critical objects is crucial in various security applications and industries. For example, in security applications, such as in airports, these objects might be hidden or covered under shields or secret sheaths. Millimeter-wave images can be utilized to discover and recognize the critical objects out of the hidden cases without any health risk due to their non-ionizing features. However, millimeter-wave images usually have waves in and around the detected objects, making object recognition difficult. Thus, regular image processing and classification methods cannot be used for these images and additional pre-processings and classification methods should be introduced. This paper proposes a novel pre-processing method for canceling rotation and scale using principal component analysis. In addition, a two-layer classification method is introduced and utilized for recognition. Moreover, a large dataset of millimeter-wave images is collected and created for experiments. Experimental results show that a typical classification method such as support vector machines can recognize 45.5% of a type of critical objects at 34.2% false alarm rate (FAR), which is a drastically poor recognition. The same method within the proposed recognition framework achieves 92.9% recognition rate at 0.43% FAR, which indicates a highly significant improvement. The significant contribution of this work is to introduce a new method for analyzing millimeter-wave images based on machine vision and learning approaches, which is not yet widely noted in the field of millimeter-wave image analysis.

Exploring the feasibility of iris recognition for visible spectrum iris images obtained using smartphone camera

NASA Astrophysics Data System (ADS)

Trokielewicz, Mateusz; Bartuzi, Ewelina; Michowska, Katarzyna; Andrzejewska, Antonina; Selegrat, Monika

2015-09-01

In the age of modern, hyperconnected society that increasingly relies on mobile devices and solutions, implementing a reliable and accurate biometric system employing iris recognition presents new challenges. Typical biometric systems employing iris analysis require expensive and complicated hardware. We therefore explore an alternative way using visible spectrum iris imaging. This paper aims at answering several questions related to applying iris biometrics for images obtained in the visible spectrum using smartphone camera. Can irides be successfully and effortlessly imaged using a smartphone's built-in camera? Can existing iris recognition methods perform well when presented with such images? The main advantage of using near-infrared (NIR) illumination in dedicated iris recognition cameras is good performance almost independent of the iris color and pigmentation. Are the images obtained from smartphone's camera of sufficient quality even for the dark irides? We present experiments incorporating simple image preprocessing to find the best visibility of iris texture, followed by a performance study to assess whether iris recognition methods originally aimed at NIR iris images perform well with visible light images. To our best knowledge this is the first comprehensive analysis of iris recognition performance using a database of high-quality images collected in visible light using the smartphones flashlight together with the application of commercial off-the-shelf (COTS) iris recognition methods.

Application of image recognition-based automatic hyphae detection in fungal keratitis.

PubMed

Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

2018-03-01

The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p < 0.05). The sensitivity of the technology of automatic hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with the conventional artificial identification of confocal microscope corneal images, of being accurate, stable and does not rely on human expertise. It was the most useful to the medical experts who are not familiar with fungal keratitis. The technology of automatic hyphae detection based on image recognition can quantify the hyphae density and grade this property. Being noninvasive, it can provide an evaluation criterion to fungal keratitis in a timely, accurate, objective and quantitative manner.

Speaker-independent phoneme recognition with a binaural auditory image model

NASA Astrophysics Data System (ADS)

Francis, Keith Ivan

1997-09-01

This dissertation presents phoneme recognition techniques based on a binaural fusion of outputs of the auditory image model and subsequent azimuth-selective phoneme recognition in a noisy environment. Background information concerning speech variations, phoneme recognition, current binaural fusion techniques and auditory modeling issues is explained. The research is constrained to sources in the frontal azimuthal plane of a simulated listener. A new method based on coincidence detection of neural activity patterns from the auditory image model of Patterson is used for azimuth-selective phoneme recognition. The method is tested in various levels of noise and the results are reported in contrast to binaural fusion methods based on various forms of correlation to demonstrate the potential of coincidence- based binaural phoneme recognition. This method overcomes smearing of fine speech detail typical of correlation based methods. Nevertheless, coincidence is able to measure similarity of left and right inputs and fuse them into useful feature vectors for phoneme recognition in noise.

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors

PubMed Central

Hong, Hyung Gil; Lee, Min Beom; Park, Kang Ryoung

2017-01-01

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods. PMID:28587269

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors.

PubMed

Hong, Hyung Gil; Lee, Min Beom; Park, Kang Ryoung

2017-06-06

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

A method of object recognition for single pixel imaging

NASA Astrophysics Data System (ADS)

Li, Boxuan; Zhang, Wenwen

2018-01-01

Computational ghost imaging(CGI), utilizing a single-pixel detector, has been extensively used in many fields. However, in order to achieve a high-quality reconstructed image, a large number of iterations are needed, which limits the flexibility of using CGI in practical situations, especially in the field of object recognition. In this paper, we purpose a method utilizing the feature matching to identify the number objects. In the given system, approximately 90% of accuracy of recognition rates can be achieved, which provides a new idea for the application of single pixel imaging in the field of object recognition

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.

Near infrared and visible face recognition based on decision fusion of LBP and DCT features

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Zhang, Shuai; Liu, Guodong; Xiong, Jinquan

2018-03-01

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In order to extract the discriminative complementary features between near infrared and visible images, in this paper, we proposed a novel near infrared and visible face fusion recognition algorithm based on DCT and LBP features. Firstly, the effective features in near-infrared face image are extracted by the low frequency part of DCT coefficients and the partition histograms of LBP operator. Secondly, the LBP features of visible-light face image are extracted to compensate for the lacking detail features of the near-infrared face image. Then, the LBP features of visible-light face image, the DCT and LBP features of near-infrared face image are sent to each classifier for labeling. Finally, decision level fusion strategy is used to obtain the final recognition result. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. The experiment results show that the proposed method extracts the complementary features of near-infrared and visible face images and improves the robustness of unconstrained face recognition. Especially for the circumstance of small training samples, the recognition rate of proposed method can reach 96.13%, which has improved significantly than 92.75 % of the method based on statistical feature fusion.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Image object recognition based on the Zernike moment and neural networks

NASA Astrophysics Data System (ADS)

Wan, Jianwei; Wang, Ling; Huang, Fukan; Zhou, Liangzhu

1998-03-01

This paper first give a comprehensive discussion about the concept of artificial neural network its research methods and the relations with information processing. On the basis of such a discussion, we expound the mathematical similarity of artificial neural network and information processing. Then, the paper presents a new method of image recognition based on invariant features and neural network by using image Zernike transform. The method not only has the invariant properties for rotation, shift and scale of image object, but also has good fault tolerance and robustness. Meanwhile, it is also compared with statistical classifier and invariant moments recognition method.

Super-resolution method for face recognition using nonlinear mappings on coherent features.

PubMed

Huang, Hua; He, Huiting

2011-01-01

Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Enhanced iris recognition method based on multi-unit iris images

NASA Astrophysics Data System (ADS)

Shin, Kwang Yong; Kim, Yeong Gon; Park, Kang Ryoung

2013-04-01

For the purpose of biometric person identification, iris recognition uses the unique characteristics of the patterns of the iris; that is, the eye region between the pupil and the sclera. When obtaining an iris image, the iris's image is frequently rotated because of the user's head roll toward the left or right shoulder. As the rotation of the iris image leads to circular shifting of the iris features, the accuracy of iris recognition is degraded. To solve this problem, conventional iris recognition methods use shifting of the iris feature codes to perform the matching. However, this increases the computational complexity and level of false acceptance error. To solve these problems, we propose a novel iris recognition method based on multi-unit iris images. Our method is novel in the following five ways compared with previous methods. First, to detect both eyes, we use Adaboost and a rapid eye detector (RED) based on the iris shape feature and integral imaging. Both eyes are detected using RED in the approximate candidate region that consists of the binocular region, which is determined by the Adaboost detector. Second, we classify the detected eyes into the left and right eyes, because the iris patterns in the left and right eyes in the same person are different, and they are therefore considered as different classes. We can improve the accuracy of iris recognition using this pre-classification of the left and right eyes. Third, by measuring the angle of head roll using the two center positions of the left and right pupils, detected by two circular edge detectors, we obtain the information of the iris rotation angle. Fourth, in order to reduce the error and processing time of iris recognition, adaptive bit-shifting based on the measured iris rotation angle is used in feature matching. Fifth, the recognition accuracy is enhanced by the score fusion of the left and right irises. Experimental results on the iris open database of low-resolution images showed that the averaged equal error rate of iris recognition using the proposed method was 4.3006%, which is lower than that of other methods.

General tensor discriminant analysis and gabor features for gait recognition.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

Video-based face recognition via convolutional neural networks

NASA Astrophysics Data System (ADS)

Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming

2017-06-01

Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

Terrain type recognition using ERTS-1 MSS images

NASA Technical Reports Server (NTRS)

Gramenopoulos, N.

1973-01-01

For the automatic recognition of earth resources from ERTS-1 digital tapes, both multispectral and spatial pattern recognition techniques are important. Recognition of terrain types is based on spatial signatures that become evident by processing small portions of an image through selected algorithms. An investigation of spatial signatures that are applicable to ERTS-1 MSS images is described. Artifacts in the spatial signatures seem to be related to the multispectral scanner. A method for suppressing such artifacts is presented. Finally, results of terrain type recognition for one ERTS-1 image are presented.

A novel iris patterns matching algorithm of weighted polar frequency correlation

NASA Astrophysics Data System (ADS)

Zhao, Weijie; Jiang, Linhua

2014-11-01

Iris recognition is recognized as one of the most accurate techniques for biometric authentication. In this paper, we present a novel correlation method - Weighted Polar Frequency Correlation(WPFC) - to match and evaluate two iris images, actually it can also be used for evaluating the similarity of any two images. The WPFC method is a novel matching and evaluating method for iris image matching, which is complete different from the conventional methods. For instance, the classical John Daugman's method of iris recognition uses 2D Gabor wavelets to extract features of iris image into a compact bit stream, and then matching two bit streams with hamming distance. Our new method is based on the correlation in the polar coordinate system in frequency domain with regulated weights. The new method is motivated by the observation that the pattern of iris that contains far more information for recognition is fine structure at high frequency other than the gross shapes of iris images. Therefore, we transform iris images into frequency domain and set different weights to frequencies. Then calculate the correlation of two iris images in frequency domain. We evaluate the iris images by summing the discrete correlation values with regulated weights, comparing the value with preset threshold to tell whether these two iris images are captured from the same person or not. Experiments are carried out on both CASIA database and self-obtained images. The results show that our method is functional and reliable. Our method provides a new prospect for iris recognition system.

Research on pre-processing of QR Code

NASA Astrophysics Data System (ADS)

Sun, Haixing; Xia, Haojie; Dong, Ning

2013-10-01

QR code encodes many kinds of information because of its advantages: large storage capacity, high reliability, full arrange of utter-high-speed reading, small printing size and high-efficient representation of Chinese characters, etc. In order to obtain the clearer binarization image from complex background, and improve the recognition rate of QR code, this paper researches on pre-processing methods of QR code (Quick Response Code), and shows algorithms and results of image pre-processing for QR code recognition. Improve the conventional method by changing the Souvola's adaptive text recognition method. Additionally, introduce the QR code Extraction which adapts to different image size, flexible image correction approach, and improve the efficiency and accuracy of QR code image processing.

Pornographic image recognition and filtering using incremental learning in compressed domain

NASA Astrophysics Data System (ADS)

Zhang, Jing; Wang, Chao; Zhuo, Li; Geng, Wenhao

2015-11-01

With the rapid development and popularity of the network, the openness, anonymity, and interactivity of networks have led to the spread and proliferation of pornographic images on the Internet, which have done great harm to adolescents' physical and mental health. With the establishment of image compression standards, pornographic images are mainly stored with compressed formats. Therefore, how to efficiently filter pornographic images is one of the challenging issues for information security. A pornographic image recognition and filtering method in the compressed domain is proposed by using incremental learning, which includes the following steps: (1) low-resolution (LR) images are first reconstructed from the compressed stream of pornographic images, (2) visual words are created from the LR image to represent the pornographic image, and (3) incremental learning is adopted to continuously adjust the classification rules to recognize the new pornographic image samples after the covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic images. The experimental results show that the proposed pornographic image recognition method using incremental learning has a higher recognition rate as well as costing less recognition time in the compressed domain.

Vehicle license plate recognition based on geometry restraints and multi-feature decision

NASA Astrophysics Data System (ADS)

Wu, Jianwei; Wang, Zongyue

2005-10-01

Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Spatiotemporal Pixelization to Increase the Recognition Score of Characters for Retinal Prostheses

PubMed Central

Kim, Hyun Seok; Park, Kwang Suk

2017-01-01

Most of the retinal prostheses use a head-fixed camera and a video processing unit. Some studies proposed various image processing methods to improve visual perception for patients. However, previous studies only focused on using spatial information. The present study proposes a spatiotemporal pixelization method mimicking fixational eye movements to generate stimulation images for artificial retina arrays by combining spatial and temporal information. Input images were sampled with a resolution that was four times higher than the number of pixel arrays. We subsampled this image and generated four different phosphene images. We then evaluated the recognition scores of characters by sequentially presenting phosphene images with varying pixel array sizes (6 × 6, 8 × 8 and 10 × 10) and stimulus frame rates (10 Hz, 15 Hz, 20 Hz, 30 Hz, and 60 Hz). The proposed method showed the highest recognition score at a stimulus frame rate of approximately 20 Hz. The method also significantly improved the recognition score for complex characters. This method provides a new way to increase practical resolution over restricted spatial resolution by merging the higher resolution image into high-frame time slots. PMID:29073735

The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

2016-09-01

The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

NASA Astrophysics Data System (ADS)

Nugraha, Nurma; Madenda, Sarifuddin; Indarti, Dina; Dewi Agushinta, R.; Ernastuti

2016-06-01

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

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.

Line-based logo recognition through a web-camera

NASA Astrophysics Data System (ADS)

Chen, Xiaolu; Wang, Yangsheng; Feng, Xuetao

2007-11-01

Logo recognition has gained much development in the document retrieval and shape analysis domain. As human computer interaction becomes more and more popular, the logo recognition through a web-camera is a promising technology in view of application. But for practical application, the study of logo recognition in real scene is much more difficult than the work in clear scene. To cope with the need, we make some improvements on conventional method. First, moment information is used to calculate the test image's orientation angle, which is used to normalize the test image. Second, the main structure of the test image, which is represented by lines patterns, is acquired and modified Hausdorff distance is employed to match the image and each of the existing templates. The proposed method, which is invariant to scale and rotation, gives good result and can work at real-time. The main contribution of this paper is that some improvements are introduced into the exiting recognition framework which performs much better than the original one. Besides, we have built a highly successful logo recognition system using our improved method.

Locality constrained joint dynamic sparse representation for local matching based face recognition.

PubMed

Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

2014-01-01

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.

Transfer learning for bimodal biometrics recognition

NASA Astrophysics Data System (ADS)

Dan, Zhiping; Sun, Shuifa; Chen, Yanfei; Gan, Haitao

2013-10-01

Biometrics recognition aims to identify and predict new personal identities based on their existing knowledge. As the use of multiple biometric traits of the individual may enables more information to be used for recognition, it has been proved that multi-biometrics can produce higher accuracy than single biometrics. However, a common problem with traditional machine learning is that the training and test data should be in the same feature space, and have the same underlying distribution. If the distributions and features are different between training and future data, the model performance often drops. In this paper, we propose a transfer learning method for face recognition on bimodal biometrics. The training and test samples of bimodal biometric images are composed of the visible light face images and the infrared face images. Our algorithm transfers the knowledge across feature spaces, relaxing the assumption of same feature space as well as same underlying distribution by automatically learning a mapping between two different but somewhat similar face images. According to the experiments in the face images, the results show that the accuracy of face recognition has been greatly improved by the proposed method compared with the other previous methods. It demonstrates the effectiveness and robustness of our method.

Optical Fourier diffractometry applied to degraded bone structure recognition

NASA Astrophysics Data System (ADS)

Galas, Jacek; Godwod, Krzysztof; Szawdyn, Jacek; Sawicki, Andrzej

1993-09-01

Image processing and recognition methods are useful in many fields. This paper presents the hybrid optical and digital method applied to recognition of pathological changes in bones involved by metabolic bone diseases. The trabecular bone structure, registered by x ray on the photographic film, is analyzed in the new type of computer controlled diffractometer. The set of image parameters, extracted from diffractogram, is evaluated by statistical analysis. The synthetic image descriptors in discriminant space, constructed on the base of 3 training groups of images (control, osteoporosis, and osteomalacia groups) by discriminant analysis, allow us to recognize bone samples with degraded bone structure and to recognize the disease. About 89% of the images were classified correctly. This method after optimization process will be verified in medical investigations.

Recognition of blurred images by the method of moments.

PubMed

Flusser, J; Suk, T; Saic, S

1996-01-01

The article is devoted to the feature-based recognition of blurred images acquired by a linear shift-invariant imaging system against an image database. The proposed approach consists of describing images by features that are invariant with respect to blur and recognizing images in the feature space. The PSF identification and image restoration are not required. A set of symmetric blur invariants based on image moments is introduced. A numerical experiment is presented to illustrate the utilization of the invariants for blurred image recognition. Robustness of the features is also briefly discussed.

Component-based target recognition inspired by human vision

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Agyepong, Kwabena

2009-05-01

In contrast with machine vision, human can recognize an object from complex background with great flexibility. For example, given the task of finding and circling all cars (no further information) in a picture, you may build a virtual image in mind from the task (or target) description before looking at the picture. Specifically, the virtual car image may be composed of the key components such as driver cabin and wheels. In this paper, we propose a component-based target recognition method by simulating the human recognition process. The component templates (equivalent to the virtual image in mind) of the target (car) are manually decomposed from the target feature image. Meanwhile, the edges of the testing image can be extracted by using a difference of Gaussian (DOG) model that simulates the spatiotemporal response in visual process. A phase correlation matching algorithm is then applied to match the templates with the testing edge image. If all key component templates are matched with the examining object, then this object is recognized as the target. Besides the recognition accuracy, we will also investigate if this method works with part targets (half cars). In our experiments, several natural pictures taken on streets were used to test the proposed method. The preliminary results show that the component-based recognition method is very promising.

Heterogeneous iris image hallucination using sparse representation on a learned heterogeneous patch dictionary

NASA Astrophysics Data System (ADS)

Li, Yung-Hui; Zheng, Bo-Ren; Ji, Dai-Yan; Tien, Chung-Hao; Liu, Po-Tsun

2014-09-01

Cross sensor iris matching may seriously degrade the recognition performance because of the sensor mis-match problem of iris images between the enrollment and test stage. In this paper, we propose two novel patch-based heterogeneous dictionary learning method to attack this problem. The first method applies the latest sparse representation theory while the second method tries to learn the correspondence relationship through PCA in heterogeneous patch space. Both methods learn the basic atoms in iris textures across different image sensors and build connections between them. After such connections are built, at test stage, it is possible to hallucinate (synthesize) iris images across different sensors. By matching training images with hallucinated images, the recognition rate can be successfully enhanced. The experimental results showed the satisfied results both visually and in terms of recognition rate. Experimenting with an iris database consisting of 3015 images, we show that the EER is decreased 39.4% relatively by the proposed method.

Consistency of response and image recognition, pulmonary nodules

PubMed Central

Liu, M A Q; Galvan, E; Bassett, R; Murphy, W A; Matamoros, A; Marom, E M

2014-01-01

Objective: To investigate the effect of recognition of a previously encountered radiograph on consistency of response in localized pulmonary nodules. Methods: 13 radiologists interpreted 40 radiographs each to locate pulmonary nodules. A few days later, they again interpreted 40 radiographs. Half of the images in the second set were new. We asked the radiologists whether each image had been in the first set. We used Fisher's exact test and Kruskal–Wallis test to evaluate the correlation between recognition of an image and consistency in its interpretation. We evaluated the data using all possible recognition levels—definitely, probably or possibly included vs definitely, probably or possibly not included by collapsing the recognition levels into two and by eliminating the “possibly included” and “possibly not included” scores. Results: With all but one of six methods of looking at the data, there was no significant correlation between consistency in interpretation and recognition of the image. When the possibly included and possibly not included scores were eliminated, there was a borderline statistical significance (p = 0.04) with slightly greater consistency in interpretation of recognized than that of non-recognized images. Conclusion: We found no convincing evidence that radiologists' recognition of images in an observer performance study affects their interpretation on a second encounter. Advances in knowledge: Conscious recognition of chest radiographs did not result in a greater degree of consistency in the tested interpretation than that in the interpretation of images that were not recognized. PMID:24697724

Compressive sensing method for recognizing cat-eye effect targets.

PubMed

Li, Li; Li, Hui; Dang, Ersheng; Liu, Bo

2013-10-01

This paper proposes a cat-eye effect target recognition method with compressive sensing (CS) and presents a recognition method (sample processing before reconstruction based on compressed sensing, or SPCS) for image processing. In this method, the linear projections of original image sequences are applied to remove dynamic background distractions and extract cat-eye effect targets. Furthermore, the corresponding imaging mechanism for acquiring active and passive image sequences is put forward. This method uses fewer images to recognize cat-eye effect targets, reduces data storage, and translates the traditional target identification, based on original image processing, into measurement vectors processing. The experimental results show that the SPCS method is feasible and superior to the shape-frequency dual criteria method.

Multispectral image fusion for illumination-invariant palmprint recognition

PubMed Central

Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied. PMID:28558064

Multispectral image fusion for illumination-invariant palmprint recognition.

PubMed

Lu, Longbin; Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.

Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex

PubMed Central

Xie, Shan Juan; Lu, Yu; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2015-01-01

Finger vein recognition has been considered one of the most promising biometrics for personal authentication. However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person. This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs). In this paper, the intrinsic factors of finger tissue causing poor quality of finger vein images are analyzed, and an intensity variation (IV) normalization method using guided filter based single scale retinex (GFSSR) is proposed for finger vein image enhancement. The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy. PMID:26184226

Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex.

PubMed

Xie, Shan Juan; Lu, Yu; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2015-07-14

Finger vein recognition has been considered one of the most promising biometrics for personal authentication. However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person. This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs). In this paper, the intrinsic factors of finger tissue causing poor quality of finger vein images are analyzed, and an intensity variation (IV) normalization method using guided filter based single scale retinex (GFSSR) is proposed for finger vein image enhancement. The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy.

Text recognition and correction for automated data collection by mobile devices

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-03-01

Participatory sensing is an approach which allows mobile devices such as mobile phones to be used for data collection, analysis and sharing processes by individuals. Data collection is the first and most important part of a participatory sensing system, but it is time consuming for the participants. In this paper, we discuss automatic data collection approaches for reducing the time required for collection, and increasing the amount of collected data. In this context, we explore automated text recognition on images of store receipts which are captured by mobile phone cameras, and the correction of the recognized text. Accordingly, our first goal is to evaluate the performance of the Optical Character Recognition (OCR) method with respect to data collection from store receipt images. Images captured by mobile phones exhibit some typical problems, and common image processing methods cannot handle some of them. Consequently, the second goal is to address these types of problems through our proposed Knowledge Based Correction (KBC) method used in support of the OCR, and also to evaluate the KBC method with respect to the improvement on the accurate recognition rate. Results of the experiments show that the KBC method improves the accurate data recognition rate noticeably.

Image-based corrosion recognition for ship steel structures

NASA Astrophysics Data System (ADS)

Ma, Yucong; Yang, Yang; Yao, Yuan; Li, Shengyuan; Zhao, Xuefeng

2018-03-01

Ship structures are subjected to corrosion inevitably in service. Existed image-based methods are influenced by the noises in images because they recognize corrosion by extracting features. In this paper, a novel method of image-based corrosion recognition for ship steel structures is proposed. The method utilizes convolutional neural networks (CNN) and will not be affected by noises in images. A CNN used to recognize corrosion was designed through fine-turning an existing CNN architecture and trained by datasets built using lots of images. Combining the trained CNN classifier with a sliding window technique, the corrosion zone in an image can be recognized.

Dynamic facial expression recognition based on geometric and texture features

NASA Astrophysics Data System (ADS)

Li, Ming; Wang, Zengfu

2018-04-01

Recently, dynamic facial expression recognition in videos has attracted growing attention. In this paper, we propose a novel dynamic facial expression recognition method by using geometric and texture features. In our system, the facial landmark movements and texture variations upon pairwise images are used to perform the dynamic facial expression recognition tasks. For one facial expression sequence, pairwise images are created between the first frame and each of its subsequent frames. Integration of both geometric and texture features further enhances the representation of the facial expressions. Finally, Support Vector Machine is used for facial expression recognition. Experiments conducted on the extended Cohn-Kanade database show that our proposed method can achieve a competitive performance with other methods.

A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network.

PubMed

Zhao, Jianwei; Lv, Yongbiao; Zhou, Zhenghua; Cao, Feilong

2017-10-01

There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel convolution neural network frame, named a low-rank-recovery network (LRRNet), is proposed to conquer the difficulty effectively inspired by matrix completion and deep learning techniques. The proposed LRRNet first recovers the incomplete face images via an approach of matrix completion with the truncated nuclear norm regularization solution, and then extracts some low-rank parts of the recovered images as the filters. With these filters, some important features are obtained by means of the binaryzation and histogram algorithms. Finally, these features are classified with the classical support vector machines (SVMs). The proposed LRRNet method has high face recognition rate for the heavily corrupted images, especially for the images in the large databases. The proposed LRRNet performs well and efficiently for the images with heavily corrupted, especially in the case of large databases. Extensive experiments on several benchmark databases demonstrate that the proposed LRRNet performs better than some other excellent robust face recognition methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Infrared vehicle recognition using unsupervised feature learning based on K-feature

NASA Astrophysics Data System (ADS)

Lin, Jin; Tan, Yihua; Xia, Haijiao; Tian, Jinwen

2018-02-01

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

Fine-grained recognition of plants from images.

PubMed

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

Image recognition on raw and processed potato detection: a review

NASA Astrophysics Data System (ADS)

Qi, Yan-nan; Lü, Cheng-xu; Zhang, Jun-ning; Li, Ya-shuo; Zeng, Zhen; Mao, Wen-hua; Jiang, Han-lu; Yang, Bing-nan

2018-02-01

Objective: Chinese potato staple food strategy clearly pointed out the need to improve potato processing, while the bottleneck of this strategy is technology and equipment of selection of appropriate raw and processed potato. The purpose of this paper is to summarize the advanced raw and processed potato detection methods. Method: According to consult research literatures in the field of image recognition based potato quality detection, including the shape, weight, mechanical damage, germination, greening, black heart, scab potato etc., the development and direction of this field were summarized in this paper. Result: In order to obtain whole potato surface information, the hardware was built by the synchronous of image sensor and conveyor belt to achieve multi-angle images of a single potato. Researches on image recognition of potato shape are popular and mature, including qualitative discrimination on abnormal and sound potato, and even round and oval potato, with the recognition accuracy of more than 83%. Weight is an important indicator for potato grading, and the image classification accuracy presents more than 93%. The image recognition of potato mechanical damage focuses on qualitative identification, with the main affecting factors of damage shape and damage time. The image recognition of potato germination usually uses potato surface image and edge germination point. Both of the qualitative and quantitative detection of green potato have been researched, currently scab and blackheart image recognition need to be operated using the stable detection environment or specific device. The image recognition of processed potato mainly focuses on potato chips, slices and fries, etc. Conclusion: image recognition as a food rapid detection tool have been widely researched on the area of raw and processed potato quality analyses, its technique and equipment have the potential for commercialization in short term, to meet to the strategy demand of development potato as staple food in China.

The fast iris image clarity evaluation based on Tenengrad and ROI selection

NASA Astrophysics Data System (ADS)

Gao, Shuqin; Han, Min; Cheng, Xu

2018-04-01

In iris recognition system, the clarity of iris image is an important factor that influences recognition effect. In the process of recognition, the blurred image may possibly be rejected by the automatic iris recognition system, which will lead to the failure of identification. Therefore it is necessary to evaluate the iris image definition before recognition. Considered the existing evaluation methods on iris image definition, we proposed a fast algorithm to evaluate the definition of iris image in this paper. In our algorithm, firstly ROI (Region of Interest) is extracted based on the reference point which is determined by using the feature of the light spots within the pupil, then Tenengrad operator is used to evaluate the iris image's definition. Experiment results show that, the iris image definition algorithm proposed in this paper could accurately distinguish the iris images of different clarity, and the algorithm has the merit of low computational complexity and more effectiveness.

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

NASA Astrophysics Data System (ADS)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images †.

PubMed

Lee, Yeongjun; Choi, Jinwoo; Ko, Nak Yong; Choi, Hyun-Taek

2017-08-24

This paper proposes a probability-based framework for recognizing underwater landmarks using sonar images. Current recognition methods use a single image, which does not provide reliable results because of weaknesses of the sonar image such as unstable acoustic source, many speckle noises, low resolution images, single channel image, and so on. However, using consecutive sonar images, if the status-i.e., the existence and identity (or name)-of an object is continuously evaluated by a stochastic method, the result of the recognition method is available for calculating the uncertainty, and it is more suitable for various applications. Our proposed framework consists of three steps: (1) candidate selection, (2) continuity evaluation, and (3) Bayesian feature estimation. Two probability methods-particle filtering and Bayesian feature estimation-are used to repeatedly estimate the continuity and feature of objects in consecutive images. Thus, the status of the object is repeatedly predicted and updated by a stochastic method. Furthermore, we develop an artificial landmark to increase detectability by an imaging sonar, which we apply to the characteristics of acoustic waves, such as instability and reflection depending on the roughness of the reflector surface. The proposed method is verified by conducting basin experiments, and the results are presented.

Noisy Ocular Recognition Based on Three Convolutional Neural Networks.

PubMed

Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

2017-12-17

In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user's eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras

PubMed Central

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-01

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems. PMID:26828487

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras.

PubMed

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-27

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems.

Pose-Invariant Face Recognition via RGB-D Images.

PubMed

Sang, Gaoli; Li, Jing; Zhao, Qijun

2016-01-01

Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions.

Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

PubMed Central

Xu, Xin; Tang, Jinshan; Zhang, Xiaolong; Liu, Xiaoming; Zhang, Hong; Qiu, Yimin

2013-01-01

With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activities, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation of the performance of human activity recognition. PMID:23353144

Background feature descriptor for offline handwritten numeral recognition

NASA Astrophysics Data System (ADS)

Ming, Delie; Wang, Hao; Tian, Tian; Jie, Feiran; Lei, Bo

2011-11-01

This paper puts forward an offline handwritten numeral recognition method based on background structural descriptor (sixteen-value numerical background expression). Through encoding the background pixels in the image according to a certain rule, 16 different eigenvalues were generated, which reflected the background condition of every digit, then reflected the structural features of the digits. Through pattern language description of images by these features, automatic segmentation of overlapping digits and numeral recognition can be realized. This method is characterized by great deformation resistant ability, high recognition speed and easy realization. Finally, the experimental results and conclusions are presented. The experimental results of recognizing datasets from various practical application fields reflect that with this method, a good recognition effect can be achieved.

Offline Arabic handwriting recognition: a survey.

PubMed

Lorigo, Liana M; Govindaraju, Venu

2006-05-01

The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It includes background on the field, discussion of the methods, and future research directions.

Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Li, Jiafeng; Zhuo, Li; Zhang, Hui; Li, Xiaoguang

2017-12-01

Color is one of the most stable attributes of vehicles and often used as a valuable cue in some important applications. Various complex environmental factors, such as illumination, weather, noise and etc., result in the visual characteristics of the vehicle color being obvious diversity. Vehicle color recognition in complex environments has been a challenging task. The state-of-the-arts methods roughly take the whole image for color recognition, but many parts of the images such as car windows; wheels and background contain no color information, which will have negative impact on the recognition accuracy. In this paper, a novel vehicle color recognition method using local vehicle-color saliency detection and dual-orientational dimensionality reduction of convolutional neural network (CNN) deep features has been proposed. The novelty of the proposed method includes two parts: (1) a local vehicle-color saliency detection method has been proposed to determine the vehicle color region of the vehicle image and exclude the influence of non-color regions on the recognition accuracy; (2) dual-orientational dimensionality reduction strategy has been designed to greatly reduce the dimensionality of deep features that are learnt from CNN, which will greatly mitigate the storage and computational burden of the subsequent processing, while improving the recognition accuracy. Furthermore, linear support vector machine is adopted as the classifier to train the dimensionality reduced features to obtain the recognition model. The experimental results on public dataset demonstrate that the proposed method can achieve superior recognition performance over the state-of-the-arts methods.

3D automatic anatomy recognition based on iterative graph-cut-ASM

NASA Astrophysics Data System (ADS)

Chen, Xinjian; Udupa, Jayaram K.; Bagci, Ulas; Alavi, Abass; Torigian, Drew A.

2010-02-01

We call the computerized assistive process of recognizing, delineating, and quantifying organs and tissue regions in medical imaging, occurring automatically during clinical image interpretation, automatic anatomy recognition (AAR). The AAR system we are developing includes five main parts: model building, object recognition, object delineation, pathology detection, and organ system quantification. In this paper, we focus on the delineation part. For the modeling part, we employ the active shape model (ASM) strategy. For recognition and delineation, we integrate several hybrid strategies of combining purely image based methods with ASM. In this paper, an iterative Graph-Cut ASM (IGCASM) method is proposed for object delineation. An algorithm called GC-ASM was presented at this symposium last year for object delineation in 2D images which attempted to combine synergistically ASM and GC. Here, we extend this method to 3D medical image delineation. The IGCASM method effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. We propose a new GC cost function, which effectively integrates the specific image information with the ASM shape model information. The proposed methods are tested on a clinical abdominal CT data set. The preliminary results show that: (a) it is feasible to explicitly bring prior 3D statistical shape information into the GC framework; (b) the 3D IGCASM delineation method improves on ASM and GC and can provide practical operational time on clinical images.

Target recognition and phase acquisition by using incoherent digital holographic imaging

NASA Astrophysics Data System (ADS)

Lee, Munseob; Lee, Byung-Tak

2017-05-01

In this study, we proposed the Incoherent Digital Holographic Imaging (IDHI) for recognition and phase information of dedicated target. Although recent development of a number of target recognition techniques such as LIDAR, there have limited success in target discrimination, in part due to low-resolution, low scanning speed, and computation power. In the paper, the proposed system consists of the incoherent light source, such as LED, Michelson interferometer, and digital CCD for acquisition of four phase shifting image. First of all, to compare with relative coherence, we used a source as laser and LED, respectively. Through numerical reconstruction by using the four phase shifting method and Fresnel diffraction method, we recovered the intensity and phase image of USAF resolution target apart from about 1.0m distance. In this experiment, we show 1.2 times improvement in resolution compared to conventional imaging. Finally, to confirm the recognition result of camouflaged targets with the same color from background, we carry out to test holographic imaging in incoherent light. In this result, we showed the possibility of a target detection and recognition that used three dimensional shape and size signatures, numerical distance from phase information of obtained holographic image.

Quick acquisition and recognition method for the beacon in deep space optical communications.

PubMed

Wang, Qiang; Liu, Yuefei; Ma, Jing; Tan, Liying; Yu, Siyuan; Li, Changjiang

2016-12-01

In deep space optical communications, it is very difficult to acquire the beacon given the long communication distance. Acquisition efficiency is essential for establishing and holding the optical communication link. Here we proposed a quick acquisition and recognition method for the beacon in deep optical communications based on the characteristics of the deep optical link. To identify the beacon from the background light efficiently, we utilized the maximum similarity between the collecting image and the reference image for accurate recognition and acquisition of the beacon in the area of uncertainty. First, the collecting image and the reference image were processed by Fourier-Mellin. Second, image sampling and image matching were applied for the accurate positioning of the beacon. Finally, the field programmable gate array (FPGA)-based system was used to verify and realize this method. The experimental results showed that the acquisition time for the beacon was as fast as 8.1s. Future application of this method in the system design of deep optical communication will be beneficial.

A general framework for face reconstruction using single still image based on 2D-to-3D transformation kernel.

PubMed

Fooprateepsiri, Rerkchai; Kurutach, Werasak

2014-03-01

Face authentication is a biometric classification method that verifies the identity of a user based on image of their face. Accuracy of the authentication is reduced when the pose, illumination and expression of the training face images are different than the testing image. The methods in this paper are designed to improve the accuracy of a features-based face recognition system when the pose between the input images and training images are different. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Second, realistic virtual faces with different poses are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; and (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex pose, illumination and expression. From the experimental results, we conclude that the proposed method improves the accuracy of face recognition by varying the pose, illumination and expression. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Clustering of Farsi sub-word images for whole-book recognition

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2015-01-01

Redundancy of word and sub-word occurrences in large documents can be effectively utilized in an OCR system to improve recognition results. Most OCR systems employ language modeling techniques as a post-processing step; however these techniques do not use important pictorial information that exist in the text image. In case of large-scale recognition of degraded documents, this information is even more valuable. In our previous work, we proposed a subword image clustering method for the applications dealing with large printed documents. In our clustering method, the ideal case is when all equivalent sub-word images lie in one cluster. To overcome the issues of low print quality, the clustering method uses an image matching algorithm for measuring the distance between two sub-word images. The measured distance with a set of simple shape features were used to cluster all sub-word images. In this paper, we analyze the effects of adding more shape features on processing time, purity of clustering, and the final recognition rate. Previously published experiments have shown the efficiency of our method on a book. Here we present extended experimental results and evaluate our method on another book with totally different font face. Also we show that the number of the new created clusters in a page can be used as a criteria for assessing the quality of print and evaluating preprocessing phases.

Fully automatic segmentation of the femur from 3D-CT images using primitive shape recognition and statistical shape models.

PubMed

Ben Younes, Lassad; Nakajima, Yoshikazu; Saito, Toki

2014-03-01

Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM. Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting. The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm. The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Wavelet Types Comparison for Extracting Iris Feature Based on Energy Compaction

NASA Astrophysics Data System (ADS)

Rizal Isnanto, R.

2015-06-01

Human iris has a very unique pattern which is possible to be used as a biometric recognition. To identify texture in an image, texture analysis method can be used. One of method is wavelet that extract the image feature based on energy. Wavelet transforms used are Haar, Daubechies, Coiflets, Symlets, and Biorthogonal. In the research, iris recognition based on five mentioned wavelets was done and then comparison analysis was conducted for which some conclusions taken. Some steps have to be done in the research. First, the iris image is segmented from eye image then enhanced with histogram equalization. The features obtained is energy value. The next step is recognition using normalized Euclidean distance. Comparison analysis is done based on recognition rate percentage with two samples stored in database for reference images. After finding the recognition rate, some tests are conducted using Energy Compaction for all five types of wavelets above. As the result, the highest recognition rate is achieved using Haar, whereas for coefficients cutting for C(i) < 0.1, Haar wavelet has a highest percentage, therefore the retention rate or significan coefficient retained for Haaris lower than other wavelet types (db5, coif3, sym4, and bior2.4)

Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

NASA Astrophysics Data System (ADS)

Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

2018-03-01

Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

Compact hybrid optoelectrical unit for image processing and recognition

NASA Astrophysics Data System (ADS)

Cheng, Gang; Jin, Guofan; Wu, Minxian; Liu, Haisong; He, Qingsheng; Yuan, ShiFu

1998-07-01

In this paper a compact opto-electric unit (CHOEU) for digital image processing and recognition is proposed. The central part of CHOEU is an incoherent optical correlator, which is realized with a SHARP QA-1200 8.4 inch active matrix TFT liquid crystal display panel which is used as two real-time spatial light modulators for both the input image and reference template. CHOEU can do two main processing works. One is digital filtering; the other is object matching. Using CHOEU an edge-detection operator is realized to extract the edges from the input images. Then the reprocessed images are sent into the object recognition unit for identifying the important targets. A novel template- matching method is proposed for gray-tome image recognition. A positive and negative cycle-encoding method is introduced to realize the absolute difference measurement pixel- matching on a correlator structure simply. The system has god fault-tolerance ability for rotation distortion, Gaussian noise disturbance or information losing. The experiments are given at the end of this paper.

Practical vision based degraded text recognition system

NASA Astrophysics Data System (ADS)

Mohammad, Khader; Agaian, Sos; Saleh, Hani

2011-02-01

Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature, and there are many commercial and research products available on this topic. These products achieve acceptable recognition accuracy and reasonable processing times especially with trained software, and constrained text characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font, size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable character recognition. Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested using a development framework system that is capable of performing OCR on camera captured images. The framework allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images. Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a custom system that is capable of performing automatic OCR which can be used for different applications. The developed framework system includes: new image enhancement, filtering, and segmentation techniques which enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best state of the art published techniques. The system successfully produced impressive OCR accuracies (90% -to- 93%) using customized systems generated by our development framework in two industrial OCR applications: water bottle label text recognition and concrete slab plate text recognition. The system was also trained for the Arabic language alphabet, and demonstrated extremely high recognition accuracy (99%) for Arabic license name plate text recognition with processing times of 10 seconds. The accuracy and run times of the system were compared to conventional and many states of art methods, the proposed system shows excellent results.

Research on application of LADAR in ground vehicle recognition

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Shen, Zhuoxun

2009-11-01

For the requirement of many practical applications in the field of military, the research of 3D target recognition is active. The representation that captures the salient attributes of a 3D target independent of the viewing angle will be especially useful to the automatic 3D target recognition system. This paper presents a new approach of image generation based on Laser Detection and Ranging (LADAR) data. Range image of target is obtained by transformation of point cloud. In order to extract features of different ground vehicle targets and to recognize targets, zernike moment properties of typical ground vehicle targets are researched in this paper. A technique of support vector machine is applied to the classification and recognition of target. The new method of image generation and feature representation has been applied to the outdoor experiments. Through outdoor experiments, it can be proven that the method of image generation is stability, the moments are effective to be used as features for recognition, and the LADAR can be applied to the field of 3D target recognition.

Chinese character recognition based on Gabor feature extraction and CNN

NASA Astrophysics Data System (ADS)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Noisy Ocular Recognition Based on Three Convolutional Neural Networks

PubMed Central

Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

2017-01-01

In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods. PMID:29258217

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Sheng, Yehua; Zhang, Ka; Ye, Chun; Liang, Cheng; Li, Jian

2008-04-01

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

PubMed

Han, Guanghui; Liu, Xiabi; Zheng, Guangyuan; Wang, Murong; Huang, Shan

2018-06-06

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images. Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.

An improved finger-vein recognition algorithm based on template matching

NASA Astrophysics Data System (ADS)

Liu, Yueyue; Di, Si; Jin, Jian; Huang, Daoping

2016-10-01

Finger-vein recognition has became the most popular biometric identify methods. The investigation on the recognition algorithms always is the key point in this field. So far, there are many applicable algorithms have been developed. However, there are still some problems in practice, such as the variance of the finger position which may lead to the image distortion and shifting; during the identification process, some matching parameters determined according to experience may also reduce the adaptability of algorithm. Focus on above mentioned problems, this paper proposes an improved finger-vein recognition algorithm based on template matching. In order to enhance the robustness of the algorithm for the image distortion, the least squares error method is adopted to correct the oblique finger. During the feature extraction, local adaptive threshold method is adopted. As regard as the matching scores, we optimized the translation preferences as well as matching distance between the input images and register images on the basis of Naoto Miura algorithm. Experimental results indicate that the proposed method can improve the robustness effectively under the finger shifting and rotation conditions.

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.

Action recognition in depth video from RGB perspective: A knowledge transfer manner

NASA Astrophysics Data System (ADS)

Chen, Jun; Xiao, Yang; Cao, Zhiguo; Fang, Zhiwen

2018-03-01

Different video modal for human action recognition has becoming a highly promising trend in the video analysis. In this paper, we propose a method for human action recognition from RGB video to Depth video using domain adaptation, where we use learned feature from RGB videos to do action recognition for depth videos. More specifically, we make three steps for solving this problem in this paper. First, different from image, video is more complex as it has both spatial and temporal information, in order to better encode this information, dynamic image method is used to represent each RGB or Depth video to one image, based on this, most methods for extracting feature in image can be used in video. Secondly, as video can be represented as image, so standard CNN model can be used for training and testing for videos, beside, CNN model can be also used for feature extracting as its powerful feature expressing ability. Thirdly, as RGB videos and Depth videos are belong to two different domains, in order to make two different feature domains has more similarity, domain adaptation is firstly used for solving this problem between RGB and Depth video, based on this, the learned feature from RGB video model can be directly used for Depth video classification. We evaluate the proposed method on one complex RGB-D action dataset (NTU RGB-D), and our method can have more than 2% accuracy improvement using domain adaptation from RGB to Depth action recognition.

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

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

Finger vein recognition based on finger crease location

NASA Astrophysics Data System (ADS)

Lu, Zhiying; Ding, Shumeng; Yin, Jing

2016-07-01

Finger vein recognition technology has significant advantages over other methods in terms of accuracy, uniqueness, and stability, and it has wide promising applications in the field of biometric recognition. We propose using finger creases to locate and extract an object region. Then we use linear fitting to overcome the problem of finger rotation in the plane. The method of modular adaptive histogram equalization (MAHE) is presented to enhance image contrast and reduce computational cost. To extract the finger vein features, we use a fusion method, which can obtain clear and distinguishable vein patterns under different conditions. We used the Hausdorff average distance algorithm to examine the recognition performance of the system. The experimental results demonstrate that MAHE can better balance the recognition accuracy and the expenditure of time compared with three other methods. Our resulting equal error rate throughout the total procedure was 3.268% in a database of 153 finger vein images.

The research of multi-frame target recognition based on laser active imaging

NASA Astrophysics Data System (ADS)

Wang, Can-jin; Sun, Tao; Wang, Tin-feng; Chen, Juan

2013-09-01

Laser active imaging is fit to conditions such as no difference in temperature between target and background, pitch-black night, bad visibility. Also it can be used to detect a faint target in long range or small target in deep space, which has advantage of high definition and good contrast. In one word, it is immune to environment. However, due to the affect of long distance, limited laser energy and atmospheric backscatter, it is impossible to illuminate the whole scene at the same time. It means that the target in every single frame is unevenly or partly illuminated, which make the recognition more difficult. At the same time the speckle noise which is common in laser active imaging blurs the images . In this paper we do some research on laser active imaging and propose a new target recognition method based on multi-frame images . Firstly, multi pulses of laser is used to obtain sub-images for different parts of scene. A denoising method combined homomorphic filter with wavelet domain SURE is used to suppress speckle noise. And blind deconvolution is introduced to obtain low-noise and clear sub-images. Then these sub-images are registered and stitched to combine a completely and uniformly illuminated scene image. After that, a new target recognition method based on contour moments is proposed. Firstly, canny operator is used to obtain contours. For each contour, seven invariant Hu moments are calculated to generate the feature vectors. At last the feature vectors are input into double hidden layers BP neural network for classification . Experiments results indicate that the proposed algorithm could achieve a high recognition rate and satisfactory real-time performance for laser active imaging.

Gender recognition from unconstrained and articulated human body.

PubMed

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

Gender Recognition from Unconstrained and Articulated Human Body

PubMed Central

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. PMID:24977203

RecceMan: an interactive recognition assistance for image-based reconnaissance: synergistic effects of human perception and computational methods for object recognition, identification, and infrastructure analysis

NASA Astrophysics Data System (ADS)

El Bekri, Nadia; Angele, Susanne; Ruckhäberle, Martin; Peinsipp-Byma, Elisabeth; Haelke, Bruno

2015-10-01

This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The infrastructure analysis mode pursues the goal to analyze the function of the infrastructure. The image analyst extracts visually certain target object signatures, assigns them to corresponding object features and is finally able to recognize the object type. The system offers him the possibility to assign the image signatures to features given by sample images. The underlying data set contains a wide range of objects features and object types for different domains like ships or land vehicles. Each domain has its own feature tree developed by aerial image analyst experts. By selecting the corresponding features, the possible solution set of objects is automatically reduced and matches only the objects that contain the selected features. Moreover, we give an outlook of current research in the field of ground target analysis in which we deal with partly automated methods to extract image signatures and assign them to the corresponding features. This research includes methods for automatically determining the orientation of an object and geometric features like width and length of the object. This step enables to reduce automatically the possible object types offered to the image analyst by the interactive recognition assistance system.

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

Finger-Vein Image Enhancement Using a Fuzzy-Based Fusion Method with Gabor and Retinex Filtering

PubMed Central

Shin, Kwang Yong; Park, Young Ho; Nguyen, Dat Tien; Park, Kang Ryoung

2014-01-01

Because of the advantages of finger-vein recognition systems such as live detection and usage as bio-cryptography systems, they can be used to authenticate individual people. However, images of finger-vein patterns are typically unclear because of light scattering by the skin, optical blurring, and motion blurring, which can degrade the performance of finger-vein recognition systems. In response to these issues, a new enhancement method for finger-vein images is proposed. Our method is novel compared with previous approaches in four respects. First, the local and global features of the vein lines of an input image are amplified using Gabor filters in four directions and Retinex filtering, respectively. Second, the means and standard deviations in the local windows of the images produced after Gabor and Retinex filtering are used as inputs for the fuzzy rule and fuzzy membership function, respectively. Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method. Fourth, the use of a fuzzy-based method means that image enhancement does not require additional training data to determine the optimal weights. Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods. PMID:24549251

Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images †

PubMed Central

Choi, Jinwoo; Choi, Hyun-Taek

2017-01-01

This paper proposes a probability-based framework for recognizing underwater landmarks using sonar images. Current recognition methods use a single image, which does not provide reliable results because of weaknesses of the sonar image such as unstable acoustic source, many speckle noises, low resolution images, single channel image, and so on. However, using consecutive sonar images, if the status—i.e., the existence and identity (or name)—of an object is continuously evaluated by a stochastic method, the result of the recognition method is available for calculating the uncertainty, and it is more suitable for various applications. Our proposed framework consists of three steps: (1) candidate selection, (2) continuity evaluation, and (3) Bayesian feature estimation. Two probability methods—particle filtering and Bayesian feature estimation—are used to repeatedly estimate the continuity and feature of objects in consecutive images. Thus, the status of the object is repeatedly predicted and updated by a stochastic method. Furthermore, we develop an artificial landmark to increase detectability by an imaging sonar, which we apply to the characteristics of acoustic waves, such as instability and reflection depending on the roughness of the reflector surface. The proposed method is verified by conducting basin experiments, and the results are presented. PMID:28837068

Boost OCR accuracy using iVector based system combination approach

NASA Astrophysics Data System (ADS)

Peng, Xujun; Cao, Huaigu; Natarajan, Prem

2015-01-01

Optical character recognition (OCR) is a challenging task because most existing preprocessing approaches are sensitive to writing style, writing material, noises and image resolution. Thus, a single recognition system cannot address all factors of real document images. In this paper, we describe an approach to combine diverse recognition systems by using iVector based features, which is a newly developed method in the field of speaker verification. Prior to system combination, document images are preprocessed and text line images are extracted with different approaches for each system, where iVector is transformed from a high-dimensional supervector of each text line and is used to predict the accuracy of OCR. We merge hypotheses from multiple recognition systems according to the overlap ratio and the predicted OCR score of text line images. We present evaluation results on an Arabic document database where the proposed method is compared against the single best OCR system using word error rate (WER) metric.

The location and recognition of anti-counterfeiting code image with complex background

NASA Astrophysics Data System (ADS)

Ni, Jing; Liu, Quan; Lou, Ping; Han, Ping

2017-07-01

The order of cigarette market is a key issue in the tobacco business system. The anti-counterfeiting code, as a kind of effective anti-counterfeiting technology, can identify counterfeit goods, and effectively maintain the normal order of market and consumers' rights and interests. There are complex backgrounds, light interference and other problems in the anti-counterfeiting code images obtained by the tobacco recognizer. To solve these problems, the paper proposes a locating method based on Susan operator, combined with sliding window and line scanning,. In order to reduce the interference of background and noise, we extract the red component of the image and convert the color image into gray image. For the confusing characters, recognition results correction based on the template matching method has been adopted to improve the recognition rate. In this method, the anti-counterfeiting code can be located and recognized correctly in the image with complex background. The experiment results show the effectiveness and feasibility of the approach.

A comparison of image processing techniques for bird recognition.

PubMed

Nadimpalli, Uma D; Price, Randy R; Hall, Steven G; Bomma, Pallavi

2006-01-01

Bird predation is one of the major concerns for fish culture in open ponds. A novel method for dispersing birds is the use of autonomous vehicles. Image recognition software can improve their efficiency. Several image processing techniques for recognition of birds have been tested. A series of morphological operations were implemented. We divided images into 3 types, Type 1, Type 2, and Type 3, based on the level of difficulty of recognizing birds. Type 1 images were clear; Type 2 images were medium clear, and Type 3 images were unclear. Local thresholding has been implemented using HSV (Hue, Saturation, and Value), GRAY, and RGB (Red, Green, and Blue) color models on all three sections of images and results were tabulated. Template matching using normal correlation and artificial neural networks (ANN) are the other methods that have been developed in this study in addition to image morphology. Template matching produced satisfactory results irrespective of the difficulty level of images, but artificial neural networks produced accuracies of 100, 60, and 50% on Type 1, Type 2, and Type 3 images, respectively. Correct classification rate can be increased by further training. Future research will focus on testing the recognition algorithms in natural or aquacultural settings on autonomous boats. Applications of such techniques to industrial, agricultural, or related areas are additional future possibilities.

A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems

NASA Astrophysics Data System (ADS)

Chaa, Mourad; Boukezzoula, Naceur-Eddine; Attia, Abdelouahab

2017-01-01

Two types of scores extracted from two-dimensional (2-D) and three-dimensional (3-D) palmprint for personal recognition systems are merged, introducing a local image descriptor for 2-D palmprint-based recognition systems, named bank of binarized statistical image features (B-BSIF). The main idea of B-BSIF is that the extracted histograms from the binarized statistical image features (BSIF) code images (the results of applying the different BSIF descriptor size with the length 12) are concatenated into one to produce a large feature vector. 3-D palmprint contains the depth information of the palm surface. The self-quotient image (SQI) algorithm is applied for reconstructing illumination-invariant 3-D palmprint images. To extract discriminative Gabor features from SQI images, Gabor wavelets are defined and used. Indeed, the dimensionality reduction methods have shown their ability in biometrics systems. Given this, a principal component analysis (PCA)+linear discriminant analysis (LDA) technique is employed. For the matching process, the cosine Mahalanobis distance is applied. Extensive experiments were conducted on a 2-D and 3-D palmprint database with 10,400 range images from 260 individuals. Then, a comparison was made between the proposed algorithm and other existing methods in the literature. Results clearly show that the proposed framework provides a higher correct recognition rate. Furthermore, the best results were obtained by merging the score of B-BSIF descriptor with the score of the SQI+Gabor wavelets+PCA+LDA method, yielding an equal error rate of 0.00% and a recognition rate of rank-1=100.00%.

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

NASA Astrophysics Data System (ADS)

Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

2014-03-01

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

Automatic anatomy recognition in whole-body PET/CT images

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

Wang, Huiqian; Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity ofmore » anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.« less

Comparing object recognition from binary and bipolar edge images for visual prostheses.

PubMed

Jung, Jae-Hyun; Pu, Tian; Peli, Eli

2016-11-01

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition.

Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

2018-01-01

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

PubMed

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

Motion Imagery Processing and Exploitation (MIPE)

DTIC Science & Technology

2013-01-01

facial recognition —i.e., the identification of a specific person.37 Object detection is often (but not always) considered a prerequisite for instance...The goal of segmentation is to distinguish objects and identify boundaries in images. Some of the earliest approaches to facial recognition involved...methods of instance recognition are at varying levels of maturity. Facial recognition methods are arguably the most mature; the technology is well

Physical Principles of the Method for Determination of Geometrical Characteristics and Particle Recognition in Digital Holography

NASA Astrophysics Data System (ADS)

Dyomin, V. V.; Polovtsev, I. G.; Davydova, A. Yu.

2018-03-01

The physical principles of a method for determination of geometrical characteristics of particles and particle recognition based on the concepts of digital holography, followed by processing of the particle images reconstructed from the digital hologram, using the morphological parameter are reported. An example of application of this method for fast plankton particle recognition is given.

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

PubMed

Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

2013-01-01

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

Warped document image correction method based on heterogeneous registration strategies

NASA Astrophysics Data System (ADS)

Tong, Lijing; Zhan, Guoliang; Peng, Quanyao; Li, Yang; Li, Yifan

2013-03-01

With the popularity of digital camera and the application requirement of digitalized document images, using digital cameras to digitalize document images has become an irresistible trend. However, the warping of the document surface impacts on the quality of the Optical Character Recognition (OCR) system seriously. To improve the warped document image's vision quality and the OCR rate, this paper proposed a warped document image correction method based on heterogeneous registration strategies. This method mosaics two warped images of the same document from different viewpoints. Firstly, two feature points are selected from one image. Then the two feature points are registered in the other image base on heterogeneous registration strategies. At last, image mosaics are done for the two images, and the best mosaiced image is selected by OCR recognition results. As a result, for the best mosaiced image, the distortions are mostly removed and the OCR results are improved markedly. Experimental results show that the proposed method can resolve the issue of warped document image correction more effectively.

Word Spotting and Recognition with Embedded Attributes.

PubMed

Almazán, Jon; Gordo, Albert; Fornés, Alicia; Valveny, Ernest

2014-12-01

This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

PubMed

Wang, Rong

2015-01-01

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images

PubMed Central

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-01-01

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition. PMID:29786665

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images.

PubMed

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-05-22

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition.

Combining 1D and 2D linear discriminant analysis for palmprint recognition

NASA Astrophysics Data System (ADS)

Zhang, Jian; Ji, Hongbing; Wang, Lei; Lin, Lin

2011-11-01

In this paper, a novel feature extraction method for palmprint recognition termed as Two-dimensional Combined Discriminant Analysis (2DCDA) is proposed. By connecting the adjacent rows of a image sequentially, the obtained new covariance matrices contain the useful information among local geometry structures in the image, which is eliminated by 2DLDA. In this way, 2DCDA combines LDA and 2DLDA for a promising recognition accuracy, but the number of coefficients of its projection matrix is lower than that of other two-dimensional methods. Experimental results on the CASIA palmprint database demonstrate the effectiveness of the proposed method.

Efficient local representations for three-dimensional palmprint recognition

NASA Astrophysics Data System (ADS)

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

2013-10-01

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

Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

PubMed

Jia, Qi; Gao, Xinkai; Guo, He; Luo, Zhongxuan; Wang, Yi

2015-03-19

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

Extraction and fusion of spectral parameters for face recognition

NASA Astrophysics Data System (ADS)

Boisier, B.; Billiot, B.; Abdessalem, Z.; Gouton, P.; Hardeberg, J. Y.

2011-03-01

Many methods have been developed in image processing for face recognition, especially in recent years with the increase of biometric technologies. However, most of these techniques are used on grayscale images acquired in the visible range of the electromagnetic spectrum. The aims of our study are to improve existing tools and to develop new methods for face recognition. The techniques used take advantage of the different spectral ranges, the visible, optical infrared and thermal infrared, by either combining them or analyzing them separately in order to extract the most appropriate information for face recognition. We also verify the consistency of several keypoints extraction techniques in the Near Infrared (NIR) and in the Visible Spectrum.

A Kinect based sign language recognition system using spatio-temporal features

NASA Astrophysics Data System (ADS)

Memiş, Abbas; Albayrak, Songül

2013-12-01

This paper presents a sign language recognition system that uses spatio-temporal features on RGB video images and depth maps for dynamic gestures of Turkish Sign Language. Proposed system uses motion differences and accumulation approach for temporal gesture analysis. Motion accumulation method, which is an effective method for temporal domain analysis of gestures, produces an accumulated motion image by combining differences of successive video frames. Then, 2D Discrete Cosine Transform (DCT) is applied to accumulated motion images and temporal domain features transformed into spatial domain. These processes are performed on both RGB images and depth maps separately. DCT coefficients that represent sign gestures are picked up via zigzag scanning and feature vectors are generated. In order to recognize sign gestures, K-Nearest Neighbor classifier with Manhattan distance is performed. Performance of the proposed sign language recognition system is evaluated on a sign database that contains 1002 isolated dynamic signs belongs to 111 words of Turkish Sign Language (TSL) in three different categories. Proposed sign language recognition system has promising success rates.

Neural network face recognition using wavelets

NASA Astrophysics Data System (ADS)

Karunaratne, Passant V.; Jouny, Ismail I.

1997-04-01

The recognition of human faces is a phenomenon that has been mastered by the human visual system and that has been researched extensively in the domain of computer neural networks and image processing. This research is involved in the study of neural networks and wavelet image processing techniques in the application of human face recognition. The objective of the system is to acquire a digitized still image of a human face, carry out pre-processing on the image as required, an then, given a prior database of images of possible individuals, be able to recognize the individual in the image. The pre-processing segment of the system includes several procedures, namely image compression, denoising, and feature extraction. The image processing is carried out using Daubechies wavelets. Once the images have been passed through the wavelet-based image processor they can be efficiently analyzed by means of a neural network. A back- propagation neural network is used for the recognition segment of the system. The main constraints of the system is with regard to the characteristics of the images being processed. The system should be able to carry out effective recognition of the human faces irrespective of the individual's facial-expression, presence of extraneous objects such as head-gear or spectacles, and face/head orientation. A potential application of this face recognition system would be as a secondary verification method in an automated teller machine.

Colour Based Image Processing Method for Recognizing Ribbed Smoked Sheet Grade

NASA Astrophysics Data System (ADS)

Fibriani, Ike; Sumardi; Bayu Satriya, Alfredo; Budi Utomo, Satryo

2017-03-01

This research proposes a colour based image processing technique to recognize the Ribbed Smoked Sheet (RSS) grade so that the RSS sorting process can be faster and more accurate than the traditional one. The RSS sheet image captured by the camera is transformed into grayscale image to simplify the recognition of rust and mould on the RSS sheet. Then the grayscale image is transformed into binary image using threshold value which is obtained from the RSS 1 reference colour. The grade recognition is determined by counting the white pixel percentage. The result shows that the system has 88% of accuracy. Most faults exist on RSS 2 recognition. This is due to the illumination distribution which is not equal over the RSS image.

Blurred image recognition by legendre moment invariants

PubMed Central

Zhang, Hui; Shu, Huazhong; Han, Guo-Niu; Coatrieux, Gouenou; Luo, Limin; Coatrieux, Jean-Louis

2010-01-01

Processing blurred images is a key problem in many image applications. Existing methods to obtain blur invariants which are invariant with respect to centrally symmetric blur are based on geometric moments or complex moments. In this paper, we propose a new method to construct a set of blur invariants using the orthogonal Legendre moments. Some important properties of Legendre moments for the blurred image are presented and proved. The performance of the proposed descriptors is evaluated with various point-spread functions and different image noises. The comparison of the present approach with previous methods in terms of pattern recognition accuracy is also provided. The experimental results show that the proposed descriptors are more robust to noise and have better discriminative power than the methods based on geometric or complex moments. PMID:19933003

Still-to-video face recognition in unconstrained environments

NASA Astrophysics Data System (ADS)

Wang, Haoyu; Liu, Changsong; Ding, Xiaoqing

2015-02-01

Face images from video sequences captured in unconstrained environments usually contain several kinds of variations, e.g. pose, facial expression, illumination, image resolution and occlusion. Motion blur and compression artifacts also deteriorate recognition performance. Besides, in various practical systems such as law enforcement, video surveillance and e-passport identification, only a single still image per person is enrolled as the gallery set. Many existing methods may fail to work due to variations in face appearances and the limit of available gallery samples. In this paper, we propose a novel approach for still-to-video face recognition in unconstrained environments. By assuming that faces from still images and video frames share the same identity space, a regularized least squares regression method is utilized to tackle the multi-modality problem. Regularization terms based on heuristic assumptions are enrolled to avoid overfitting. In order to deal with the single image per person problem, we exploit face variations learned from training sets to synthesize virtual samples for gallery samples. We adopt a learning algorithm combining both affine/convex hull-based approach and regularizations to match image sets. Experimental results on a real-world dataset consisting of unconstrained video sequences demonstrate that our method outperforms the state-of-the-art methods impressively.

Comparing object recognition from binary and bipolar edge images for visual prostheses

PubMed Central

Jung, Jae-Hyun; Pu, Tian; Peli, Eli

2017-01-01

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition. PMID:28458481

Application of syntactic methods of pattern recognition for data mining and knowledge discovery in medicine

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-04-01

This paper presents and discusses possibilities of application of selected algorithms belonging to the group of syntactic methods of patten recognition used to analyze and extract features of shapes and to diagnose morphological lesions seen on selected medical images. This method is particularly useful for specialist morphological analysis of shapes of selected organs of abdominal cavity conducted to diagnose disease symptoms occurring in the main pancreatic ducts, upper segments of ureters and renal pelvis. Analysis of the correct morphology of these organs is possible with the application of the sequential and tree method belonging to the group of syntactic methods of pattern recognition. The objective of this analysis is to support early diagnosis of disease lesions, mainly characteristic for carcinoma and pancreatitis, based on examinations of ERCP images and a diagnosis of morphological lesions in ureters as well as renal pelvis based on an analysis of urograms. In the analysis of ERCP images the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis, while in the case of kidney radiogram analysis the aim is to diagnose local irregularities of ureter lumen and to examine the morphology of renal pelvis and renal calyxes. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of description of features of shapes and context-free sequential attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing of width diagrams of the examined structures. Additionally, in order to support the analysis of the correct structure of renal pelvis a method using the tree grammar for syntactic pattern recognition to define its correct morphological shapes has been presented.

Facial recognition using multisensor images based on localized kernel eigen spaces.

PubMed

Gundimada, Satyanadh; Asari, Vijayan K

2009-06-01

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.

Multispectral Palmprint Recognition Using a Quaternion Matrix

PubMed Central

Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng

2012-01-01

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%. PMID:22666049

Multispectral palmprint recognition using a quaternion matrix.

PubMed

Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng

2012-01-01

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

Computer Vision for Artificially Intelligent Robotic Systems

NASA Astrophysics Data System (ADS)

Ma, Chialo; Ma, Yung-Lung

1987-04-01

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts -- position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed bye the main control unit. In Pulse-Echo Signal Process Unit, we ultilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by u law coding method, and this data together with delay time T, angle information OH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main Control Unit also handles the pattern recognition process. The distance from the target to the transducer plate is limitted by the power and beam angle of transducer elements, in this AIRS Model, we use a narrow beam transducer and it's input voltage is 50V p-p. A RobOt equipped with AIRS can not only measure the distance from the target but also recognize a three dimensional image of target from the image lab of Robot memory. Indexitems, Accoustic System, Supersonic transducer, Dynamic programming, Look-up-table, Image process, pattern Recognition, Quad Tree, Quadappoach.

Improved Performance Characteristics For Indium Antimonide Photovoltaic Detector Arrays Using A FET-Switched Multiplexing Technique

NASA Astrophysics Data System (ADS)

Ma, Yung-Lung; Ma, Chialo

1987-03-01

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts _ position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed by the main control unit. In Pulse-Echo Signal Process Unit, we utilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by p law coding method, and this data together with delay time T, angle information eH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main Control Unit also handles the pattern recognition process. The distance from the target to the transducer plate is limitted by the power and beam angle of transducer elements, in this AIRS Models, we use a narrow beam transducer and it's input voltage is 50V p-p. A Robot equipped with AIRS can not only measure the distance from the target but also recognize a three dimensional image of target from the image lab of Robot memory. Indexitems, Accoustic System, Supersonic transducer, Dynamic programming, Look-up-table, Image process, pattern Recognition, Quad Tree, Quadappoach.

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

Santos-Villalobos, Hector J; Barstow, Del R; Karakaya, Mahmut

Iris recognition has been proven to be an accurate and reliable biometric. However, the recognition of non-ideal iris images such as off angle images is still an unsolved problem. We propose a new biometric targeted eye model and a method to reconstruct the off-axis eye to its frontal view allowing for recognition using existing methods and algorithms. This allows for existing enterprise level algorithms and approaches to be largely unmodified by using our work as a pre-processor to improve performance. In addition, we describe the `Limbus effect' and its importance for an accurate segmentation of off-axis irides. Our method usesmore » an anatomically accurate human eye model and ray-tracing techniques to compute a transformation function, which reconstructs the iris to its frontal, non-refracted state. Then, the same eye model is used to render a frontal view of the reconstructed iris. The proposed method is fully described and results from synthetic data are shown to establish an upper limit on performance improvement and establish the importance of the proposed approach over traditional linear elliptical unwrapping methods. Our results with synthetic data demonstrate the ability to perform an accurate iris recognition with an image taken as much as 70 degrees off-axis.« less

Exploring the feasibility of traditional image querying tasks for industrial radiographs

NASA Astrophysics Data System (ADS)

Bray, Iliana E.; Tsai, Stephany J.; Jimenez, Edward S.

2015-08-01

Although there have been great strides in object recognition with optical images (photographs), there has been comparatively little research into object recognition for X-ray radiographs. Our exploratory work contributes to this area by creating an object recognition system designed to recognize components from a related database of radiographs. Object recognition for radiographs must be approached differently than for optical images, because radiographs have much less color-based information to distinguish objects, and they exhibit transmission overlap that alters perceived object shapes. The dataset used in this work contained more than 55,000 intermixed radiographs and photographs, all in a compressed JPEG form and with multiple ways of describing pixel information. For this work, a robust and efficient system is needed to combat problems presented by properties of the X-ray imaging modality, the large size of the given database, and the quality of the images contained in said database. We have explored various pre-processing techniques to clean the cluttered and low-quality images in the database, and we have developed our object recognition system by combining multiple object detection and feature extraction methods. We present the preliminary results of the still-evolving hybrid object recognition system.

DYNAMIC PATTERN RECOGNITION BY MEANS OF THRESHOLD NETS,

DTIC Science & Technology

A method is expounded for the recognition of visual patterns. A circuit diagram of a device is described which is based on a multilayer threshold ...structure synthesized in accordance with the proposed method. Coded signals received each time an image is displayed are transmitted to the threshold ...circuit which distinguishes the signs, and from there to the layers of threshold resolving elements. The image at each layer is made to correspond

Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

2017-06-01

Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

The study of infrared target recognition at sea background based on visual attention computational model

NASA Astrophysics Data System (ADS)

Wang, Deng-wei; Zhang, Tian-xu; Shi, Wen-jun; Wei, Long-sheng; Wang, Xiao-ping; Ao, Guo-qing

2009-07-01

Infrared images at sea background are notorious for the low signal-to-noise ratio, therefore, the target recognition of infrared image through traditional methods is very difficult. In this paper, we present a novel target recognition method based on the integration of visual attention computational model and conventional approach (selective filtering and segmentation). The two distinct techniques for image processing are combined in a manner to utilize the strengths of both. The visual attention algorithm searches the salient regions automatically, and represented them by a set of winner points, at the same time, demonstrated the salient regions in terms of circles centered at these winner points. This provides a priori knowledge for the filtering and segmentation process. Based on the winner point, we construct a rectangular region to facilitate the filtering and segmentation, then the labeling operation will be added selectively by requirement. Making use of the labeled information, from the final segmentation result we obtain the positional information of the interested region, label the centroid on the corresponding original image, and finish the localization for the target. The cost time does not depend on the size of the image but the salient regions, therefore the consumed time is greatly reduced. The method is used in the recognition of several kinds of real infrared images, and the experimental results reveal the effectiveness of the algorithm presented in this paper.

False match elimination for face recognition based on SIFT algorithm

NASA Astrophysics Data System (ADS)

Gu, Xuyuan; Shi, Ping; Shao, Meide

2011-06-01

The SIFT (Scale Invariant Feature Transform) is a well known algorithm used to detect and describe local features in images. It is invariant to image scale, rotation and robust to the noise and illumination. In this paper, a novel method used for face recognition based on SIFT is proposed, which combines the optimization of SIFT, mutual matching and Progressive Sample Consensus (PROSAC) together and can eliminate the false matches of face recognition effectively. Experiments on ORL face database show that many false matches can be eliminated and better recognition rate is achieved.

Survey of Technologies for the Airport Border of the Future

DTIC Science & Technology

2014-04-01

geometry Handwriting recognition ID cards Image classification Image enhancement Image fusion Image matching Image processing Image segmentation Iris...00 Tongue print Footstep recognition Odour recognition Retinal recognition Emotion recognition Periocular recognition Handwriting recognition Ear...recognition Palmprint recognition Hand geometry DNA matching Vein matching Ear recognition Handwriting recognition Periocular recognition Emotion

Object recognition in images via a factor graph model

NASA Astrophysics Data System (ADS)

He, Yong; Wang, Long; Wu, Zhaolin; Zhang, Haisu

2018-04-01

Object recognition in images suffered from huge search space and uncertain object profile. Recently, the Bag-of- Words methods are utilized to solve these problems, especially the 2-dimension CRF(Conditional Random Field) model. In this paper we suggest the method based on a general and flexible fact graph model, which can catch the long-range correlation in Bag-of-Words by constructing a network learning framework contrasted from lattice in CRF. Furthermore, we explore a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for the factor graph model. Experimental results on Graz 02 dataset show that, the recognition performance of our method in precision and recall is better than a state-of-art method and the original CRF model, demonstrating the effectiveness of the proposed method.

Prediction of the thermal imaging minimum resolvable (circle) temperature difference with neural network application.

PubMed

Fang, Yi-Chin; Wu, Bo-Wen

2008-12-01

Thermal imaging is an important technology in both national defense and the private sector. An advantage of thermal imaging is its ability to be deployed while fully engaged in duties, not limited by weather or the brightness of indoor or outdoor conditions. However, in an outdoor environment, many factors, including atmospheric decay, target shape, great distance, fog, temperature out of range and diffraction limits can lead to bad image formation, which directly affects the accuracy of object recognition. The visual characteristics of the human eye mean that it has a much better capacity for picture recognition under normal conditions than artificial intelligence does. However, conditions of interference significantly reduce this capacity for picture recognition for instance, fatigue impairs human eyesight. Hence, psychological and physiological factors can affect the result when the human eye is adopted to measure MRTD (minimum resolvable temperature difference) and MRCTD (minimum resolvable circle temperature difference). This study explores thermal imaging recognition, and presents a method for effectively choosing the characteristic values and processing the images fully. Neural network technology is successfully applied to recognize thermal imaging and predict MRTD and MRCTD (Appendix A), exceeding thermal imaging recognition under fatigue and the limits of the human eye.

Implementation of sobel method to detect the seed rubber plant leaves

NASA Astrophysics Data System (ADS)

Suyanto; Munte, J.

2018-03-01

This research was conducted to develop a system that can identify and recognize the type of rubber tree based on the pattern of leaves of the plant. The steps research are started with the identification of the image data acquisition, image processing, image edge detection and identification method template matching. Edge detection is using Sobel edge detection. Pattern recognition would detect image as input and compared with other images in a database called templates. Experiments carried out in one phase, identification of the leaf edge, using a rubber plant leaf image 14 are superior and 5 for each type of test images (clones) of the plant. From the experimental results obtained by the recognition rate of 91.79%.

Neural network application for thermal image recognition of low-resolution objects

NASA Astrophysics Data System (ADS)

Fang, Yi-Chin; Wu, Bo-Wen

2007-02-01

In the ever-changing situation on a battle field, accurate recognition of a distant object is critical to a commander's decision-making and the general public's safety. Efficiently distinguishing between an enemy's armoured vehicles and ordinary civilian houses under all weather conditions has become an important research topic. This study presents a system for recognizing an armoured vehicle by distinguishing marks and contours. The characteristics of 12 different shapes and 12 characters are used to explore thermal image recognition under the circumstance of long distance and low resolution. Although the recognition capability of human eyes is superior to that of artificial intelligence under normal conditions, it tends to deteriorate substantially under long-distance and low-resolution scenarios. This study presents an effective method for choosing features and processing images. The artificial neural network technique is applied to further improve the probability of accurate recognition well beyond the limit of the recognition capability of human eyes.

Low-contrast underwater living fish recognition using PCANet

NASA Astrophysics Data System (ADS)

Sun, Xin; Yang, Jianping; Wang, Changgang; Dong, Junyu; Wang, Xinhua

2018-04-01

Quantitative and statistical analysis of ocean creatures is critical to ecological and environmental studies. And living fish recognition is one of the most essential requirements for fishery industry. However, light attenuation and scattering phenomenon are present in the underwater environment, which makes underwater images low-contrast and blurry. This paper tries to design a robust framework for accurate fish recognition. The framework introduces a two stage PCA Network to extract abstract features from fish images. On a real-world fish recognition dataset, we use a linear SVM classifier and set penalty coefficients to conquer data unbalanced issue. Feature visualization results show that our method can avoid the feature distortion in boundary regions of underwater image. Experiments results show that the PCA Network can extract discriminate features and achieve promising recognition accuracy. The framework improves the recognition accuracy of underwater living fishes and can be easily applied to marine fishery industry.

Textural characteristics of bone marrow blast nucleus images with different variants of acute lymphoblastic leukemia

NASA Astrophysics Data System (ADS)

Nikitaev, V. G.; Pronichev, A. N.; Polyakov, E. V.; Mozhenkova, A. V.; Tupitsin, N. N.; Frenkel, M. A.

2018-01-01

The paper describes the method of recognition of T - and B - variants of acute lymphoblastic leukemia in microscopic images of blood cells. The method is based on the use of texture characteristics of images. Experimental recognition accuracy evaluation is obtained from the sample of 38 patients (17 with T-ALL and 21 with B-ALL variants of acute lymphoblastic leukemia). The obtained results show the possibility of applying of the proposed approach to the differential diagnosis of T- and B- variants of acute lymphoblastic leukemia.

Facial Asymmetry-Based Age Group Estimation: Role in Recognizing Age-Separated Face Images.

PubMed

Sajid, Muhammad; Taj, Imtiaz Ahmad; Bajwa, Usama Ijaz; Ratyal, Naeem Iqbal

2018-04-23

Face recognition aims to establish the identity of a person based on facial characteristics. On the other hand, age group estimation is the automatic calculation of an individual's age range based on facial features. Recognizing age-separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age-separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation. In this paper, we propose an age-assisted face recognition approach to handle aging variations. Inspired by the observation that facial asymmetry is an age-dependent intrinsic facial feature, we first use asymmetric facial dimensions to estimate the age group of a given face image. Deeply learned asymmetric facial features are then extracted for face recognition using a deep convolutional neural network (dCNN). Finally, we integrate the knowledge learned from the age group estimation into the face recognition algorithm using the same dCNN. This integration results in a significant improvement in the overall performance compared to using the face recognition algorithm alone. The experimental results on two large facial aging datasets, the MORPH and FERET sets, show that the proposed age group estimation based on the face recognition approach yields superior performance compared to some existing state-of-the-art methods. © 2018 American Academy of Forensic Sciences.

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Indonesian Sign Language Number Recognition using SIFT Algorithm

NASA Astrophysics Data System (ADS)

Mahfudi, Isa; Sarosa, Moechammad; Andrie Asmara, Rosa; Azrino Gustalika, M.

2018-04-01

Indonesian sign language (ISL) is generally used for deaf individuals and poor people communication in communicating. They use sign language as their primary language which consists of 2 types of action: sign and finger spelling. However, not all people understand their sign language so that this becomes a problem for them to communicate with normal people. this problem also becomes a factor they are isolated feel from the social life. It needs a solution that can help them to be able to interacting with normal people. Many research that offers a variety of methods in solving the problem of sign language recognition based on image processing. SIFT (Scale Invariant Feature Transform) algorithm is one of the methods that can be used to identify an object. SIFT is claimed very resistant to scaling, rotation, illumination and noise. Using SIFT algorithm for Indonesian sign language recognition number result rate recognition to 82% with the use of a total of 100 samples image dataset consisting 50 sample for training data and 50 sample images for testing data. Change threshold value get affect the result of the recognition. The best value threshold is 0.45 with rate recognition of 94%.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

NASA Astrophysics Data System (ADS)

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution.

PubMed

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-06

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

PubMed Central

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining. PMID:28059147

A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

PubMed

Yuan, Tao; Zheng, Xinqi; Hu, Xuan; Zhou, Wei; Wang, Wei

2014-01-01

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

Face recognition in the thermal infrared domain

NASA Astrophysics Data System (ADS)

Kowalski, M.; Grudzień, A.; Palka, N.; Szustakowski, M.

2017-10-01

Biometrics refers to unique human characteristics. Each unique characteristic may be used to label and describe individuals and for automatic recognition of a person based on physiological or behavioural properties. One of the most natural and the most popular biometric trait is a face. The most common research methods on face recognition are based on visible light. State-of-the-art face recognition systems operating in the visible light spectrum achieve very high level of recognition accuracy under controlled environmental conditions. Thermal infrared imagery seems to be a promising alternative or complement to visible range imaging due to its relatively high resistance to illumination changes. A thermal infrared image of the human face presents its unique heat-signature and can be used for recognition. The characteristics of thermal images maintain advantages over visible light images, and can be used to improve algorithms of human face recognition in several aspects. Mid-wavelength or far-wavelength infrared also referred to as thermal infrared seems to be promising alternatives. We present the study on 1:1 recognition in thermal infrared domain. The two approaches we are considering are stand-off face verification of non-moving person as well as stop-less face verification on-the-move. The paper presents methodology of our studies and challenges for face recognition systems in the thermal infrared domain.

Author name recognition in degraded journal images

NASA Astrophysics Data System (ADS)

de Bodard de la Jacopière, Aliette; Likforman-Sulem, Laurence

2006-01-01

A method for extracting names in degraded documents is presented in this article. The documents targeted are images of photocopied scientific journals from various scientific domains. Due to the degradation, there is poor OCR recognition, and pieces of other articles appear on the sides of the image. The proposed approach relies on the combination of a low-level textual analysis and an image-based analysis. The textual analysis extracts robust typographic features, while the image analysis selects image regions of interest through anchor components. We report results on the University of Washington benchmark database.

Cepstrum based feature extraction method for fungus detection

NASA Astrophysics Data System (ADS)

Yorulmaz, Onur; Pearson, Tom C.; Çetin, A. Enis

2011-06-01

In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%.

Human gait recognition by pyramid of HOG feature on silhouette images

NASA Astrophysics Data System (ADS)

Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

2013-03-01

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

PubMed Central

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-01

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

A Method to Recognize Anatomical Site and Image Acquisition View in X-ray Images.

PubMed

Chang, Xiao; Mazur, Thomas; Li, H Harold; Yang, Deshan

2017-12-01

A method was developed to recognize anatomical site and image acquisition view automatically in 2D X-ray images that are used in image-guided radiation therapy. The purpose is to enable site and view dependent automation and optimization in the image processing tasks including 2D-2D image registration, 2D image contrast enhancement, and independent treatment site confirmation. The X-ray images for 180 patients of six disease sites (the brain, head-neck, breast, lung, abdomen, and pelvis) were included in this study with 30 patients each site and two images of orthogonal views each patient. A hierarchical multiclass recognition model was developed to recognize general site first and then specific site. Each node of the hierarchical model recognized the images using a feature extraction step based on principal component analysis followed by a binary classification step based on support vector machine. Given two images in known orthogonal views, the site recognition model achieved a 99% average F1 score across the six sites. If the views were unknown in the images, the average F1 score was 97%. If only one image was taken either with or without view information, the average F1 score was 94%. The accuracy of the site-specific view recognition models was 100%.

A content-based image retrieval method for optical colonoscopy images based on image recognition techniques

NASA Astrophysics Data System (ADS)

Nosato, Hirokazu; Sakanashi, Hidenori; Takahashi, Eiichi; Murakawa, Masahiro

2015-03-01

This paper proposes a content-based image retrieval method for optical colonoscopy images that can find images similar to ones being diagnosed. Optical colonoscopy is a method of direct observation for colons and rectums to diagnose bowel diseases. It is the most common procedure for screening, surveillance and treatment. However, diagnostic accuracy for intractable inflammatory bowel diseases, such as ulcerative colitis (UC), is highly dependent on the experience and knowledge of the medical doctor, because there is considerable variety in the appearances of colonic mucosa within inflammations with UC. In order to solve this issue, this paper proposes a content-based image retrieval method based on image recognition techniques. The proposed retrieval method can find similar images from a database of images diagnosed as UC, and can potentially furnish the medical records associated with the retrieved images to assist the UC diagnosis. Within the proposed method, color histogram features and higher order local auto-correlation (HLAC) features are adopted to represent the color information and geometrical information of optical colonoscopy images, respectively. Moreover, considering various characteristics of UC colonoscopy images, such as vascular patterns and the roughness of the colonic mucosa, we also propose an image enhancement method to highlight the appearances of colonic mucosa in UC. In an experiment using 161 UC images from 32 patients, we demonstrate that our method improves the accuracy of retrieving similar UC images.

Wavelet-based associative memory

NASA Astrophysics Data System (ADS)

Jones, Katharine J.

2004-04-01

Faces provide important characteristics of a person"s identification. In security checks, face recognition still remains the method in continuous use despite other approaches (i.e. fingerprints, voice recognition, pupil contraction, DNA scanners). With an associative memory, the output data is recalled directly using the input data. This can be achieved with a Nonlinear Holographic Associative Memory (NHAM). This approach can also distinguish between strongly correlated images and images that are partially or totally enclosed by others. Adaptive wavelet lifting has been used for Content-Based Image Retrieval. In this paper, adaptive wavelet lifting will be applied to face recognition to achieve an associative memory.

Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

NASA Astrophysics Data System (ADS)

Nikiforov, M. P.; Reukov, V. V.; Thompson, G. L.; Vertegel, A. A.; Guo, S.; Kalinin, S. V.; Jesse, S.

2009-10-01

Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

Blurred Palmprint Recognition Based on Stable-Feature Extraction Using a Vese–Osher Decomposition Model

PubMed Central

Hong, Danfeng; Su, Jian; Hong, Qinggen; Pan, Zhenkuan; Wang, Guodong

2014-01-01

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese–Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred–PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition. PMID:24992328

Blurred palmprint recognition based on stable-feature extraction using a Vese-Osher decomposition model.

PubMed

Hong, Danfeng; Su, Jian; Hong, Qinggen; Pan, Zhenkuan; Wang, Guodong

2014-01-01

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese-Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred-PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition.

Automatic recognition of light source from color negative films using sorting classification techniques

NASA Astrophysics Data System (ADS)

Sanger, Demas S.; Haneishi, Hideaki; Miyake, Yoichi

1995-08-01

This paper proposed a simple and automatic method for recognizing the light sources from various color negative film brands by means of digital image processing. First, we stretched the image obtained from a negative based on the standardized scaling factors, then extracted the dominant color component among red, green, and blue components of the stretched image. The dominant color component became the discriminator for the recognition. The experimental results verified that any one of the three techniques could recognize the light source from negatives of any film brands and all brands greater than 93.2 and 96.6% correct recognitions, respectively. This method is significant for the automation of color quality control in color reproduction from color negative film in mass processing and printing machine.

Toward noncooperative iris recognition: a classification approach using multiple signatures.

PubMed

Proença, Hugo; Alexandre, Luís A

2007-04-01

This paper focuses on noncooperative iris recognition, i.e., the capture of iris images at large distances, under less controlled lighting conditions, and without active participation of the subjects. This increases the probability of capturing very heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstructions and reflections). Current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, especially the false rejections, in these conditions. We propose an iris classification method that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule. Experiments show a substantial decrease, higher than 40 percent, of the false rejection rates in the recognition of noisy iris images.

Method for traffic-sign detection within a picture by color identification and external shape recognition

NASA Astrophysics Data System (ADS)

Falcoff, Daniel E.; Canali, Luis R.

1999-08-01

This work present one method aimed to individualization and recognition of vial signs in route and city. It is based fundamentally on the identification by means of color and form of the vial sing, located in the border of the route or street in city, and then recognition. To do so the obtained RGB image is processed, carrying out diverse filtrates in the sequence of input image, or intensifying the colors of the same ones otherwise, recognizing their silhouette and then segmenting the sign and comparing the symbology of them with the previously stored and classified database.

A new method of edge detection for object recognition

USGS Publications Warehouse

Maddox, Brian G.; Rhew, Benjamin

2004-01-01

Traditional edge detection systems function by returning every edge in an input image. This can result in a large amount of clutter and make certain vectorization algorithms less accurate. Accuracy problems can then have a large impact on automated object recognition systems that depend on edge information. A new method of directed edge detection can be used to limit the number of edges returned based on a particular feature. This results in a cleaner image that is easier for vectorization. Vectorized edges from this process could then feed an object recognition system where the edge data would also contain information as to what type of feature it bordered.

Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.

PubMed

Hwang, Wonjun; Wang, Haitao; Kim, Hyunwoo; Kee, Seok-Cheol; Kim, Junmo

2011-04-01

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.

Infrared and visible fusion face recognition based on NSCT domain

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Zhang, Shuai; Liu, Guodong; Xiong, Jinquan

2018-01-01

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

The UBIRIS.v2: a database of visible wavelength iris images captured on-the-move and at-a-distance.

PubMed

Proença, Hugo; Filipe, Sílvio; Santos, Ricardo; Oliveira, João; Alexandre, Luís A

2010-08-01

The iris is regarded as one of the most useful traits for biometric recognition and the dissemination of nationwide iris-based recognition systems is imminent. However, currently deployed systems rely on heavy imaging constraints to capture near infrared images with enough quality. Also, all of the publicly available iris image databases contain data correspondent to such imaging constraints and therefore are exclusively suitable to evaluate methods thought to operate on these type of environments. The main purpose of this paper is to announce the availability of the UBIRIS.v2 database, a multisession iris images database which singularly contains data captured in the visible wavelength, at-a-distance (between four and eight meters) and on on-the-move. This database is freely available for researchers concerned about visible wavelength iris recognition and will be useful in accessing the feasibility and specifying the constraints of this type of biometric recognition.

A novel rotational invariants target recognition method for rotating motion blurred images

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Gong, Meiling; Dong, Mingwei; Zeng, Yiliang; Zhang, Yuzhen

2017-11-01

The imaging of the image sensor is blurred due to the rotational motion of the carrier and reducing the target recognition rate greatly. Although the traditional mode that restores the image first and then identifies the target can improve the recognition rate, it takes a long time to recognize. In order to solve this problem, a rotating fuzzy invariants extracted model was constructed that recognizes target directly. The model includes three metric layers. The object description capability of metric algorithms that contain gray value statistical algorithm, improved round projection transformation algorithm and rotation-convolution moment invariants in the three metric layers ranges from low to high, and the metric layer with the lowest description ability among them is as the input which can eliminate non pixel points of target region from degenerate image gradually. Experimental results show that the proposed model can improve the correct target recognition rate of blurred image and optimum allocation between the computational complexity and function of region.

Implementation of age and gender recognition system for intelligent digital signage

NASA Astrophysics Data System (ADS)

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

2015-12-01

Intelligent digital signage systems transmit customized advertising and information by analyzing users and customers, unlike existing system that presented advertising in the form of broadcast without regard to type of customers. Currently, development of intelligent digital signage system has been pushed forward vigorously. In this study, we designed a system capable of analyzing gender and age of customers based on image obtained from camera, although there are many different methods for analyzing customers. We conducted age and gender recognition experiments using public database. The age/gender recognition experiments were performed through histogram matching method by extracting Local binary patterns (LBP) features after facial area on input image was normalized. The results of experiment showed that gender recognition rate was as high as approximately 97% on average. Age recognition was conducted based on categorization into 5 age classes. Age recognition rates for women and men were about 67% and 68%, respectively when that conducted separately for different gender.

Extending the Capture Volume of an Iris Recognition System Using Wavefront Coding and Super-Resolution.

PubMed

Hsieh, Sheng-Hsun; Li, Yung-Hui; Tien, Chung-Hao; Chang, Chin-Chen

2016-12-01

Iris recognition has gained increasing popularity over the last few decades; however, the stand-off distance in a conventional iris recognition system is too short, which limits its application. In this paper, we propose a novel hardware-software hybrid method to increase the stand-off distance in an iris recognition system. When designing the system hardware, we use an optimized wavefront coding technique to extend the depth of field. To compensate for the blurring of the image caused by wavefront coding, on the software side, the proposed system uses a local patch-based super-resolution method to restore the blurred image to its clear version. The collaborative effect of the new hardware design and software post-processing showed great potential in our experiment. The experimental results showed that such improvement cannot be achieved by using a hardware-or software-only design. The proposed system can increase the capture volume of a conventional iris recognition system by three times and maintain the system's high recognition rate.

Facial expression recognition based on weber local descriptor and sparse representation

NASA Astrophysics Data System (ADS)

Ouyang, Yan

2018-03-01

Automatic facial expression recognition has been one of the research hotspots in the area of computer vision for nearly ten years. During the decade, many state-of-the-art methods have been proposed which perform very high accurate rate based on the face images without any interference. Nowadays, many researchers begin to challenge the task of classifying the facial expression images with corruptions and occlusions and the Sparse Representation based Classification framework has been wildly used because it can robust to the corruptions and occlusions. Therefore, this paper proposed a novel facial expression recognition method based on Weber local descriptor (WLD) and Sparse representation. The method includes three parts: firstly the face images are divided into many local patches, and then the WLD histograms of each patch are extracted, finally all the WLD histograms features are composed into a vector and combined with SRC to classify the facial expressions. The experiment results on the Cohn-Kanade database show that the proposed method is robust to occlusions and corruptions.

Target Recognition Using Neural Networks for Model Deformation Measurements

NASA Technical Reports Server (NTRS)

Ross, Richard W.; Hibler, David L.

1999-01-01

Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. This paper describes a technique using a neural network and image processing technologies which increases the reliability of target recognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.

Single-pixel non-imaging object recognition by means of Fourier spectrum acquisition

NASA Astrophysics Data System (ADS)

Chen, Huichao; Shi, Jianhong; Liu, Xialin; Niu, Zhouzhou; Zeng, Guihua

2018-04-01

Single-pixel imaging has emerged over recent years as a novel imaging technique, which has significant application prospects. In this paper, we propose and experimentally demonstrate a scheme that can achieve single-pixel non-imaging object recognition by acquiring the Fourier spectrum. In an experiment, a four-step phase-shifting sinusoid illumination light is used to irradiate the object image, the value of the light intensity is measured with a single-pixel detection unit, and the Fourier coefficients of the object image are obtained by a differential measurement. The Fourier coefficients are first cast into binary numbers to obtain the hash value. We propose a new method of perceptual hashing algorithm, which is combined with a discrete Fourier transform to calculate the hash value. The hash distance is obtained by calculating the difference of the hash value between the object image and the contrast images. By setting an appropriate threshold, the object image can be quickly and accurately recognized. The proposed scheme realizes single-pixel non-imaging perceptual hashing object recognition by using fewer measurements. Our result might open a new path for realizing object recognition with non-imaging.

The recognition of graphical patterns invariant to geometrical transformation of the models

NASA Astrophysics Data System (ADS)

Ileană, Ioan; Rotar, Corina; Muntean, Maria; Ceuca, Emilian

2010-11-01

In case that a pattern recognition system is used for images recognition (in robot vision, handwritten recognition etc.), the system must have the capacity to identify an object indifferently of its size or position in the image. The problem of the invariance of recognition can be approached in some fundamental modes. One may apply the similarity criterion used in associative recall. The original pattern is replaced by a mathematical transform that assures some invariance (e.g. the value of two-dimensional Fourier transformation is translation invariant, the value of Mellin transformation is scale invariant). In a different approach the original pattern is represented through a set of features, each of them being coded indifferently of the position, orientation or position of the pattern. Generally speaking, it is easy to obtain invariance in relation with one transformation group, but is difficult to obtain simultaneous invariance at rotation, translation and scale. In this paper we analyze some methods to achieve invariant recognition of images, particularly for digit images. A great number of experiments are due and the conclusions are underplayed in the paper.

Logo image clustering based on advanced statistics

NASA Astrophysics Data System (ADS)

Wei, Yi; Kamel, Mohamed; He, Yiwei

2007-11-01

In recent years, there has been a growing interest in the research of image content description techniques. Among those, image clustering is one of the most frequently discussed topics. Similar to image recognition, image clustering is also a high-level representation technique. However it focuses on the coarse categorization rather than the accurate recognition. Based on wavelet transform (WT) and advanced statistics, the authors propose a novel approach that divides various shaped logo images into groups according to the external boundary of each logo image. Experimental results show that the presented method is accurate, fast and insensitive to defects.

Robust Point Set Matching for Partial Face Recognition.

PubMed

Weng, Renliang; Lu, Jiwen; Tan, Yap-Peng

2016-03-01

Over the past three decades, a number of face recognition methods have been proposed in computer vision, and most of them use holistic face images for person identification. In many real-world scenarios especially some unconstrained environments, human faces might be occluded by other objects, and it is difficult to obtain fully holistic face images for recognition. To address this, we propose a new partial face recognition approach to recognize persons of interest from their partial faces. Given a pair of gallery image and probe face patch, we first detect keypoints and extract their local textural features. Then, we propose a robust point set matching method to discriminatively match these two extracted local feature sets, where both the textural information and geometrical information of local features are explicitly used for matching simultaneously. Finally, the similarity of two faces is converted as the distance between these two aligned feature sets. Experimental results on four public face data sets show the effectiveness of the proposed approach.

Multi-layer cube sampling for liver boundary detection in PET-CT images.

PubMed

Liu, Xinxin; Yang, Jian; Song, Shuang; Song, Hong; Ai, Danni; Zhu, Jianjun; Jiang, Yurong; Wang, Yongtian

2018-06-01

Liver metabolic information is considered as a crucial diagnostic marker for the diagnosis of fever of unknown origin, and liver recognition is the basis of automatic diagnosis of metabolic information extraction. However, the poor quality of PET and CT images is a challenge for information extraction and target recognition in PET-CT images. The existing detection method cannot meet the requirement of liver recognition in PET-CT images, which is the key problem in the big data analysis of PET-CT images. A novel texture feature descriptor called multi-layer cube sampling (MLCS) is developed for liver boundary detection in low-dose CT and PET images. The cube sampling feature is proposed for extracting more texture information, which uses a bi-centric voxel strategy. Neighbour voxels are divided into three regions by the centre voxel and the reference voxel in the histogram, and the voxel distribution information is statistically classified as texture feature. Multi-layer texture features are also used to improve the ability and adaptability of target recognition in volume data. The proposed feature is tested on the PET and CT images for liver boundary detection. For the liver in the volume data, mean detection rate (DR) and mean error rate (ER) reached 95.15 and 7.81% in low-quality PET images, and 83.10 and 21.08% in low-contrast CT images. The experimental results demonstrated that the proposed method is effective and robust for liver boundary detection.

Automatic recognition of fundamental tissues on histology images of the human cardiovascular system.

PubMed

Mazo, Claudia; Trujillo, Maria; Alegre, Enrique; Salazar, Liliana

2016-10-01

Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nuclei's features - shape and spatial projection - and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissue's function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Image recognition and consistency of response

NASA Astrophysics Data System (ADS)

Haygood, Tamara M.; Ryan, John; Liu, Qing Mary A.; Bassett, Roland; Brennan, Patrick C.

2012-02-01

Purpose: To investigate the connection between conscious recognition of an image previously encountered in an experimental setting and consistency of response to the experimental question.
Materials and Methods: Twenty-four radiologists viewed 40 frontal chest radiographs and gave their opinion as to the position of a central venous catheter. One-to-three days later they again viewed 40 frontal chest radiographs and again gave their opinion as to the position of the central venous catheter. Half of the radiographs in the second set were repeated images from the first set and half were new. The radiologists were asked of each image whether it had been included in the first set. For this study, we are evaluating only the 20 repeated images. We used the Kruskal-Wallis test and Fisher's exact test to determine the relationship between conscious recognition of a previously interpreted image and consistency in interpretation of the image.
Results. There was no significant correlation between recognition of the image and consistency in response regarding the position of the central venous catheter. In fact, there was a trend in the opposite direction, with radiologists being slightly more likely to give a consistent response with respect to images they did not recognize than with respect to those they did recognize.
Conclusion: Radiologists' recognition of previously-encountered images in an observer-performance study does not noticeably color their interpretation on the second encounter.

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

PubMed

Ming, Yue; Wang, Guangchao; Fan, Chunxiao

2015-01-01

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

Generating virtual training samples for sparse representation of face images and face recognition

NASA Astrophysics Data System (ADS)

Du, Yong; Wang, Yu

2016-03-01

There are many challenges in face recognition. In real-world scenes, images of the same face vary with changing illuminations, different expressions and poses, multiform ornaments, or even altered mental status. Limited available training samples cannot convey these possible changes in the training phase sufficiently, and this has become one of the restrictions to improve the face recognition accuracy. In this article, we view the multiplication of two images of the face as a virtual face image to expand the training set and devise a representation-based method to perform face recognition. The generated virtual samples really reflect some possible appearance and pose variations of the face. By multiplying a training sample with another sample from the same subject, we can strengthen the facial contour feature and greatly suppress the noise. Thus, more human essential information is retained. Also, uncertainty of the training data is simultaneously reduced with the increase of the training samples, which is beneficial for the training phase. The devised representation-based classifier uses both the original and new generated samples to perform the classification. In the classification phase, we first determine K nearest training samples for the current test sample by calculating the Euclidean distances between the test sample and training samples. Then, a linear combination of these selected training samples is used to represent the test sample, and the representation result is used to classify the test sample. The experimental results show that the proposed method outperforms some state-of-the-art face recognition methods.

Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

Improving the recognition of fingerprint biometric system using enhanced image fusion

NASA Astrophysics Data System (ADS)

Alsharif, Salim; El-Saba, Aed; Stripathi, Reshma

2010-04-01

Fingerprints recognition systems have been widely used by financial institutions, law enforcement, border control, visa issuing, just to mention few. Biometric identifiers can be counterfeited, but considered more reliable and secure compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of a fingerprint recognition system in terms of accuracy and security. This paper presents digital enhancement and fusion approaches that improve the biometric of the fingerprint recognition system. It is a two-step approach. In the first step raw fingerprint images are enhanced using high-frequency-emphasis filtering (HFEF). The second step is a simple linear fusion process between the raw images and the HFEF ones. It is shown that the proposed approach increases the verification and identification of the fingerprint biometric recognition system, where any improvement is justified using the correlation performance metrics of the matching algorithm.

Recognition of rotated images using the multi-valued neuron and rotation-invariant 2D Fourier descriptors

NASA Astrophysics Data System (ADS)

Aizenberg, Evgeni; Bigio, Irving J.; Rodriguez-Diaz, Eladio

2012-03-01

The Fourier descriptors paradigm is a well-established approach for affine-invariant characterization of shape contours. In the work presented here, we extend this method to images, and obtain a 2D Fourier representation that is invariant to image rotation. The proposed technique retains phase uniqueness, and therefore structural image information is not lost. Rotation-invariant phase coefficients were used to train a single multi-valued neuron (MVN) to recognize satellite and human face images rotated by a wide range of angles. Experiments yielded 100% and 96.43% classification rate for each data set, respectively. Recognition performance was additionally evaluated under effects of lossy JPEG compression and additive Gaussian noise. Preliminary results show that the derived rotation-invariant features combined with the MVN provide a promising scheme for efficient recognition of rotated images.

Face recognition by applying wavelet subband representation and kernel associative memory.

PubMed

Zhang, Bai-Ling; Zhang, Haihong; Ge, Shuzhi Sam

2004-01-01

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Learning discriminative features from RGB-D images for gender and ethnicity identification

NASA Astrophysics Data System (ADS)

Azzakhnini, Safaa; Ballihi, Lahoucine; Aboutajdine, Driss

2016-11-01

The development of sophisticated sensor technologies gave rise to an interesting variety of data. With the appearance of affordable devices, such as the Microsoft Kinect, depth-maps and three-dimensional data became easily accessible. This attracted many computer vision researchers seeking to exploit this information in classification and recognition tasks. In this work, the problem of face classification in the context of RGB images and depth information (RGB-D images) is addressed. The purpose of this paper is to study and compare some popular techniques for gender recognition and ethnicity classification to understand how much depth data can improve the quality of recognition. Furthermore, we investigate which combination of face descriptors, feature selection methods, and learning techniques is best suited to better exploit RGB-D images. The experimental results show that depth data improve the recognition accuracy for gender and ethnicity classification applications in many use cases.

Recognition and Matching of Clustered Mature Litchi Fruits Using Binocular Charge-Coupled Device (CCD) Color Cameras

PubMed Central

Wang, Chenglin; Tang, Yunchao; Zou, Xiangjun; Luo, Lufeng; Chen, Xiong

2017-01-01

Recognition and matching of litchi fruits are critical steps for litchi harvesting robots to successfully grasp litchi. However, due to the randomness of litchi growth, such as clustered growth with uncertain number of fruits and random occlusion by leaves, branches and other fruits, the recognition and matching of the fruit become a challenge. Therefore, this study firstly defined mature litchi fruit as three clustered categories. Then an approach for recognition and matching of clustered mature litchi fruit was developed based on litchi color images acquired by binocular charge-coupled device (CCD) color cameras. The approach mainly included three steps: (1) calibration of binocular color cameras and litchi image acquisition; (2) segmentation of litchi fruits using four kinds of supervised classifiers, and recognition of the pre-defined categories of clustered litchi fruit using a pixel threshold method; and (3) matching the recognized clustered fruit using a geometric center-based matching method. The experimental results showed that the proposed recognition method could be robust against the influences of varying illumination and occlusion conditions, and precisely recognize clustered litchi fruit. In the tested 432 clustered litchi fruits, the highest and lowest average recognition rates were 94.17% and 92.00% under sunny back-lighting and partial occlusion, and sunny front-lighting and non-occlusion conditions, respectively. From 50 pairs of tested images, the highest and lowest matching success rates were 97.37% and 91.96% under sunny back-lighting and non-occlusion, and sunny front-lighting and partial occlusion conditions, respectively. PMID:29112177

The software peculiarities of pattern recognition in track detectors

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

Starkov, N.

The different kinds of nuclear track recognition algorithms are represented. Several complicated samples of use them in physical experiments are considered. The some processing methods of complicated images are described.

Face recognition system and method using face pattern words and face pattern bytes

DOEpatents

Zheng, Yufeng

2014-12-23

The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

Neural networks for data compression and invariant image recognition

NASA Technical Reports Server (NTRS)

Gardner, Sheldon

1989-01-01

An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

Scattering Removal for Finger-Vein Image Restoration

PubMed Central

Yang, Jinfeng; Zhang, Ben; Shi, Yihua

2012-01-01

Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy. PMID:22737028

Augmented reality system

NASA Astrophysics Data System (ADS)

Lin, Chien-Liang; Su, Yu-Zheng; Hung, Min-Wei; Huang, Kuo-Cheng

2010-08-01

In recent years, Augmented Reality (AR)[1][2][3] is very popular in universities and research organizations. The AR technology has been widely used in Virtual Reality (VR) fields, such as sophisticated weapons, flight vehicle development, data model visualization, virtual training, entertainment and arts. AR has characteristics to enhance the display output as a real environment with specific user interactive functions or specific object recognitions. It can be use in medical treatment, anatomy training, precision instrument casting, warplane guidance, engineering and distance robot control. AR has a lot of vantages than VR. This system developed combines sensors, software and imaging algorithms to make users feel real, actual and existing. Imaging algorithms include gray level method, image binarization method, and white balance method in order to make accurate image recognition and overcome the effects of light.

Face recognition using slow feature analysis and contourlet transform

NASA Astrophysics Data System (ADS)

Wang, Yuehao; Peng, Lingling; Zhe, Fuchuan

2018-04-01

In this paper we propose a novel face recognition approach based on slow feature analysis (SFA) in contourlet transform domain. This method firstly use contourlet transform to decompose the face image into low frequency and high frequency part, and then takes technological advantages of slow feature analysis for facial feature extraction. We named the new method combining the slow feature analysis and contourlet transform as CT-SFA. The experimental results on international standard face database demonstrate that the new face recognition method is effective and competitive.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Static sign language recognition using 1D descriptors and neural networks

NASA Astrophysics Data System (ADS)

Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César

2012-10-01

A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

Bidirectional Modulation of Recognition Memory

PubMed Central

Ho, Jonathan W.; Poeta, Devon L.; Jacobson, Tara K.; Zolnik, Timothy A.; Neske, Garrett T.; Connors, Barry W.

2015-01-01

Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects. For example, animals and humans with perirhinal damage are unable to distinguish familiar from novel objects in recognition memory tasks. In the normal brain, perirhinal neurons respond to novelty and familiarity by increasing or decreasing firing rates. Recent work also implicates oscillatory activity in the low-beta and low-gamma frequency bands in sensory detection, perception, and recognition. Using optogenetic methods in a spontaneous object exploration (SOR) task, we altered recognition memory performance in rats. In the SOR task, normal rats preferentially explore novel images over familiar ones. We modulated exploratory behavior in this task by optically stimulating channelrhodopsin-expressing perirhinal neurons at various frequencies while rats looked at novel or familiar 2D images. Stimulation at 30–40 Hz during looking caused rats to treat a familiar image as if it were novel by increasing time looking at the image. Stimulation at 30–40 Hz was not effective in increasing exploration of novel images. Stimulation at 10–15 Hz caused animals to treat a novel image as familiar by decreasing time looking at the image, but did not affect looking times for images that were already familiar. We conclude that optical stimulation of PER at different frequencies can alter visual recognition memory bidirectionally. SIGNIFICANCE STATEMENT Recognition of novelty and familiarity are important for learning, memory, and decision making. Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects, but how novelty and familiarity are encoded and transmitted in the brain is not known. Perirhinal neurons respond to novelty and familiarity by changing firing rates, but recent work suggests that brain oscillations may also be important for recognition. In this study, we showed that stimulation of the PER could increase or decrease exploration of novel and familiar images depending on the frequency of stimulation. Our findings suggest that optical stimulation of PER at specific frequencies can predictably alter recognition memory. PMID:26424881

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.

PubMed

Li, Heng; Su, Xiaofan; Wang, Jing; Kan, Han; Han, Tingting; Zeng, Yajie; Chai, Xinyu

2018-01-01

Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision. We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects. i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene. The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. Copyright © 2017 Elsevier B.V. All rights reserved.

Cherry recognition in natural environment based on the vision of picking robot

NASA Astrophysics Data System (ADS)

Zhang, Qirong; Chen, Shanxiong; Yu, Tingzhong; Wang, Yan

2017-04-01

In order to realize the automatic recognition of cherry in the natural environment, this paper designed a robot vision system recognition method. The first step of this method is to pre-process the cherry image by median filtering. The second step is to identify the colour of the cherry through the 0.9R-G colour difference formula, and then use the Otsu algorithm for threshold segmentation. The third step is to remove noise by using the area threshold. The fourth step is to remove the holes in the cherry image by morphological closed and open operation. The fifth step is to obtain the centroid and contour of cherry by using the smallest external rectangular and the Hough transform. Through this recognition process, we can successfully identify 96% of the cherry without blocking and adhesion.

Self-organized Evaluation of Dynamic Hand Gestures for Sign Language Recognition

NASA Astrophysics Data System (ADS)

Buciu, Ioan; Pitas, Ioannis

Two main theories exist with respect to face encoding and representation in the human visual system (HVS). The first one refers to the dense (holistic) representation of the face, where faces have "holon"-like appearance. The second one claims that a more appropriate face representation is given by a sparse code, where only a small fraction of the neural cells corresponding to face encoding is activated. Theoretical and experimental evidence suggest that the HVS performs face analysis (encoding, storing, face recognition, facial expression recognition) in a structured and hierarchical way, where both representations have their own contribution and goal. According to neuropsychological experiments, it seems that encoding for face recognition, relies on holistic image representation, while a sparse image representation is used for facial expression analysis and classification. From the computer vision perspective, the techniques developed for automatic face and facial expression recognition fall into the same two representation types. Like in Neuroscience, the techniques which perform better for face recognition yield a holistic image representation, while those techniques suitable for facial expression recognition use a sparse or local image representation. The proposed mathematical models of image formation and encoding try to simulate the efficient storing, organization and coding of data in the human cortex. This is equivalent with embedding constraints in the model design regarding dimensionality reduction, redundant information minimization, mutual information minimization, non-negativity constraints, class information, etc. The presented techniques are applied as a feature extraction step followed by a classification method, which also heavily influences the recognition results.

Uyghur face recognition method combining 2DDCT with POEM

NASA Astrophysics Data System (ADS)

Yi, Lihamu; Ya, Ermaimaiti

2017-11-01

In this paper, in light of the reduced recognition rate and poor robustness of Uyghur face under illumination and partial occlusion, a Uyghur face recognition method combining Two Dimension Discrete Cosine Transform (2DDCT) with Patterns Oriented Edge Magnitudes (POEM) was proposed. Firstly, the Uyghur face images were divided into 8×8 block matrix, and the Uyghur face images after block processing were converted into frequency-domain status using 2DDCT; secondly, the Uyghur face images were compressed to exclude non-sensitive medium frequency parts and non-high frequency parts, so it can reduce the feature dimensions necessary for the Uyghur face images, and further reduce the amount of computation; thirdly, the corresponding POEM histograms of the Uyghur face images were obtained by calculating the feature quantity of POEM; fourthly, the POEM histograms were cascaded together as the texture histogram of the center feature point to obtain the texture features of the Uyghur face feature points; finally, classification of the training samples was carried out using deep learning algorithm. The simulation experiment results showed that the proposed algorithm further improved the recognition rate of the self-built Uyghur face database, and greatly improved the computing speed of the self-built Uyghur face database, and had strong robustness.

Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications

NASA Astrophysics Data System (ADS)

Fernández, Ariel; Ferrari, José A.

2017-05-01

Pattern recognition and feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital-only methods. We explore an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a pupil mask implemented on a high-contrast spatial light modulator for orientation/shape variation of the template. Real-time can also be achieved. In addition, by thresholding of the GHT and optically inverse transforming, the previously detected features of interest can be extracted.

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

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

Chang, X; Yang, D

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was usedmore » to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.« less

Three-dimensional imaging of artificial fingerprint by optical coherence tomography

NASA Astrophysics Data System (ADS)

Larin, Kirill V.; Cheng, Yezeng

2008-03-01

Fingerprint recognition is one of the popular used methods of biometrics. However, due to the surface topography limitation, fingerprint recognition scanners are easily been spoofed, e.g. using artificial fingerprint dummies. Thus, biometric fingerprint identification devices need to be more accurate and secure to deal with different fraudulent methods including dummy fingerprints. Previously, we demonstrated that Optical Coherence Tomography (OCT) images revealed the presence of the artificial fingerprints (made from different household materials, such as cement and liquid silicone rubber) at all times, while the artificial fingerprints easily spoofed the commercial fingerprint reader. Also we demonstrated that an analysis of the autocorrelation of the OCT images could be used in automatic recognition systems. Here, we exploited the three-dimensional (3D) imaging of the artificial fingerprint by OCT to generate vivid 3D image for both the artificial fingerprint layer and the real fingerprint layer beneath. With the reconstructed 3D image, it could not only point out whether there exists an artificial material, which is intended to spoof the scanner, above the real finger, but also could provide the hacker's fingerprint. The results of these studies suggested that Optical Coherence Tomography could be a powerful real-time noninvasive method for accurate identification of artificial fingerprints real fingerprints as well.

Object Recognition using Feature- and Color-Based Methods

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu; Stubberud, Allen

2008-01-01

An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

Robust recognition of degraded machine-printed characters using complementary similarity measure and error-correction learning

NASA Astrophysics Data System (ADS)

Hagita, Norihiro; Sawaki, Minako

1995-03-01

Most conventional methods in character recognition extract geometrical features such as stroke direction, connectivity of strokes, etc., and compare them with reference patterns in a stored dictionary. Unfortunately, geometrical features are easily degraded by blurs, stains and the graphical background designs used in Japanese newspaper headlines. This noise must be removed before recognition commences, but no preprocessing method is completely accurate. This paper proposes a method for recognizing degraded characters and characters printed on graphical background designs. This method is based on the binary image feature method and uses binary images as features. A new similarity measure, called the complementary similarity measure, is used as a discriminant function. It compares the similarity and dissimilarity of binary patterns with reference dictionary patterns. Experiments are conducted using the standard character database ETL-2 which consists of machine-printed Kanji, Hiragana, Katakana, alphanumeric, an special characters. The results show that this method is much more robust against noise than the conventional geometrical feature method. It also achieves high recognition rates of over 92% for characters with textured foregrounds, over 98% for characters with textured backgrounds, over 98% for outline fonts, and over 99% for reverse contrast characters.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition

PubMed Central

Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods. PMID:27807443

Research and Development of Target Recognition and Location Crawling Platform based on Binocular Vision

NASA Astrophysics Data System (ADS)

Xu, Weidong; Lei, Zhu; Yuan, Zhang; Gao, Zhenqing

2018-03-01

The application of visual recognition technology in industrial robot crawling and placing operation is one of the key tasks in the field of robot research. In order to improve the efficiency and intelligence of the material sorting in the production line, especially to realize the sorting of the scattered items, the robot target recognition and positioning crawling platform based on binocular vision is researched and developed. The images were collected by binocular camera, and the images were pretreated. Harris operator was used to identify the corners of the images. The Canny operator was used to identify the images. Hough-chain code recognition was used to identify the images. The target image in the image, obtain the coordinates of each vertex of the image, calculate the spatial position and posture of the target item, and determine the information needed to capture the movement and transmit it to the robot control crawling operation. Finally, In this paper, we use this method to experiment the wrapping problem in the express sorting process The experimental results show that the platform can effectively solve the problem of sorting of loose parts, so as to achieve the purpose of efficient and intelligent sorting.

Pollen Image Recognition Based on DGDB-LBP Descriptor

NASA Astrophysics Data System (ADS)

Han, L. P.; Xie, Y. H.

2018-01-01

In this paper, we propose DGDB-LBP, a local binary pattern descriptor based on the pixel blocks in the dominant gradient direction. Differing from traditional LBP and its variants, DGDB-LBP encodes by comparing the main gradient magnitude of each block rather than the single pixel value or the average of pixel blocks, in doing so, it reduces the influence of noise on pollen images and eliminates redundant and non-informative features. In order to fully describe the texture features of pollen images and analyze it under multi-scales, we propose a new sampling strategy, which uses three types of operators to extract the radial, angular and multiple texture features under different scales. Considering that the pollen images have some degree of rotation under the microscope, we propose the adaptive encoding direction, which is determined by the texture distribution of local region. Experimental results on the Pollenmonitor dataset show that the average correct recognition rate of our method is superior to other pollen recognition methods in recent years.

Sunspot drawings handwritten character recognition method based on deep learning

NASA Astrophysics Data System (ADS)

Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

2016-05-01

High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

Deep learning approach to bacterial colony classification.

PubMed

Zieliński, Bartosz; Plichta, Anna; Misztal, Krzysztof; Spurek, Przemysław; Brzychczy-Włoch, Monika; Ochońska, Dorota

2017-01-01

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.

Heuristic Analysis Model of Nitrided Layers’ Formation Consisting of the Image Processing and Analysis and Elements of Artificial Intelligence

PubMed Central

Wójcicki, Tomasz; Nowicki, Michał

2016-01-01

The article presents a selected area of research and development concerning the methods of material analysis based on the automatic image recognition of the investigated metallographic sections. The objectives of the analyses of the materials for gas nitriding technology are described. The methods of the preparation of nitrided layers, the steps of the process and the construction and operation of devices for gas nitriding are given. We discuss the possibility of using the methods of digital images processing in the analysis of the materials, as well as their essential task groups: improving the quality of the images, segmentation, morphological transformations and image recognition. The developed analysis model of the nitrided layers formation, covering image processing and analysis techniques, as well as selected methods of artificial intelligence are presented. The model is divided into stages, which are formalized in order to better reproduce their actions. The validation of the presented method is performed. The advantages and limitations of the developed solution, as well as the possibilities of its practical use, are listed. PMID:28773389

An application of viola jones method for face recognition for absence process efficiency

NASA Astrophysics Data System (ADS)

Rizki Damanik, Rudolfo; Sitanggang, Delima; Pasaribu, Hendra; Siagian, Hendrik; Gulo, Frisman

2018-04-01

Absence was a list of documents that the company used to record the attendance time of each employee. The most common problem in a fingerprint machine is the identification of a slow sensor or a sensor not recognizing a finger. The employees late to work because they get difficulties at fingerprint system, they need about 3 – 5 minutes to absence when the condition of finger is wet or not fit. To overcome this problem, this research tried to utilize facial recognition for attendance process. The method used for facial recognition was Viola Jones. Through the processing phase of the RGB face image was converted into a histogram equalization face image for the next stage of recognition. The result of this research was the absence process could be done less than 1 second with a maximum slope of ± 700 and a distance of 20-200 cm. After implement facial recognition the process of absence is more efficient, just take less 1 minute to absence.

Handwritten-word spotting using biologically inspired features.

PubMed

van der Zant, Tijn; Schomaker, Lambert; Haak, Koen

2008-11-01

For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language and collection. We propose a biologically inspired whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.

A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier

PubMed Central

Jaafar, Haryati; Ibrahim, Salwani; Ramli, Dzati Athiar

2015-01-01

Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%. PMID:26113861

An improved silhouette for human pose estimation

NASA Astrophysics Data System (ADS)

Hawes, Anthony H.; Iftekharuddin, Khan M.

2017-08-01

We propose a novel method for analyzing images that exploits the natural lines of a human poses to find areas where self-occlusion could be present. Errors caused by self-occlusion cause several modern human pose estimation methods to mis-identify body parts, which reduces the performance of most action recognition algorithms. Our method is motivated by the observation that, in several cases, occlusion can be reasoned using only boundary lines of limbs. An intelligent edge detection algorithm based on the above principle could be used to augment the silhouette with information useful for pose estimation algorithms and push forward progress on occlusion handling for human action recognition. The algorithm described is applicable to computer vision scenarios involving 2D images and (appropriated flattened) 3D images.

Salient man-made structure detection in infrared images

NASA Astrophysics Data System (ADS)

Li, Dong-jie; Zhou, Fu-gen; Jin, Ting

2013-09-01

Target detection, segmentation and recognition is a hot research topic in the field of image processing and pattern recognition nowadays, among which salient area or object detection is one of core technologies of precision guided weapon. Many theories have been raised in this paper; we detect salient objects in a series of input infrared images by using the classical feature integration theory and Itti's visual attention system. In order to find the salient object in an image accurately, we present a new method to solve the edge blur problem by calculating and using the edge mask. We also greatly improve the computing speed by improving the center-surround differences method. Unlike the traditional algorithm, we calculate the center-surround differences through rows and columns separately. Experimental results show that our method is effective in detecting salient object accurately and rapidly.

Iris recognition: on the segmentation of degraded images acquired in the visible wavelength.

PubMed

Proença, Hugo

2010-08-01

Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.

Pattern recognition for passive polarimetric data using nonparametric classifiers

NASA Astrophysics Data System (ADS)

Thilak, Vimal; Saini, Jatinder; Voelz, David G.; Creusere, Charles D.

2005-08-01

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.

Automatic gang graffiti recognition and interpretation

NASA Astrophysics Data System (ADS)

Parra, Albert; Boutin, Mireille; Delp, Edward J.

2017-09-01

One of the roles of emergency first responders (e.g., police and fire departments) is to prevent and protect against events that can jeopardize the safety and well-being of a community. In the case of criminal gang activity, tools are needed for finding, documenting, and taking the necessary actions to mitigate the problem or issue. We describe an integrated mobile-based system capable of using location-based services, combined with image analysis, to track and analyze gang activity through the acquisition, indexing, and recognition of gang graffiti images. This approach uses image analysis methods for color recognition, image segmentation, and image retrieval and classification. A database of gang graffiti images is described that includes not only the images but also metadata related to the images, such as date and time, geoposition, gang, gang member, colors, and symbols. The user can then query the data in a useful manner. We have implemented these features both as applications for Android and iOS hand-held devices and as a web-based interface.

Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

PubMed

Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Research for Key Techniques of Geophysical Recognition System of Hydrocarbon-induced Magnetic Anomalies Based on Hydrocarbon Seepage Theory

NASA Astrophysics Data System (ADS)

Zhang, L.; Hao, T.; Zhao, B.

2009-12-01

Hydrocarbon seepage effects can cause magnetic alteration zones in near surface, and the magnetic anomalies induced by the alteration zones can thus be used to locate oil-gas potential regions. In order to reduce the inaccuracy and multi-resolution of the hydrocarbon anomalies recognized only by magnetic data, and to meet the requirement of integrated management and sythetic analysis of multi-source geoscientfic data, it is necessary to construct a recognition system that integrates the functions of data management, real-time processing, synthetic evaluation, and geologic mapping. In this paper research for the key techniques of the system is discussed. Image processing methods can be applied to potential field images so as to make it easier for visual interpretation and geological understanding. For gravity or magnetic images, the anomalies with identical frequency-domain characteristics but different spatial distribution will reflect differently in texture and relevant textural statistics. Texture is a description of structural arrangements and spatial variation of a dataset or an image, and has been applied in many research fields. Textural analysis is a procedure that extracts textural features by image processing methods and thus obtains a quantitative or qualitative description of texture. When the two kinds of anomalies have no distinct difference in amplitude or overlap in frequency spectrum, they may be distinguishable due to their texture, which can be considered as textural contrast. Therefore, for the recognition system we propose a new “magnetic spots” recognition method based on image processing techniques. The method can be divided into 3 major steps: firstly, separate local anomalies caused by shallow, relatively small sources from the total magnetic field, and then pre-process the local magnetic anomaly data by image processing methods such that magnetic anomalies can be expressed as points, lines and polygons with spatial correlation, which includes histogram-equalization based image display, object recognition and extraction; then, mine the spatial characteristics and correlations of the magnetic anomalies using textural statistics and analysis, and study the features of known anomalous objects (closures, hydrocarbon-bearing structures, igneous rocks, etc.) in the same research area; finally, classify the anomalies, cluster them according to their similarity, and predict hydrocarbon induced “magnetic spots” combined with geologic, drilling and rock core data. The system uses the ArcGIS as the secondary development platform, inherits the basic functions of the ArcGIS, and develops two main sepecial functional modules, the module for conventional potential-field data processing methods and the module for feature extraction and enhancement based on image processing and analysis techniques. The system can be applied to realize the geophysical detection and recognition of near-surface hydrocarbon seepage anomalies, provide technical support for locating oil-gas potential regions, and promote geophysical data processing and interpretation to advance more efficiently.

Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models.

PubMed

Cho, Woon; Jang, Jinbeum; Koschan, Andreas; Abidi, Mongi A; Paik, Joonki

2016-11-28

A fundamental limitation of hyperspectral imaging is the inter-band misalignment correlated with subject motion during data acquisition. One way of resolving this problem is to assess the alignment quality of hyperspectral image cubes derived from the state-of-the-art alignment methods. In this paper, we present an automatic selection framework for the optimal alignment method to improve the performance of face recognition. Specifically, we develop two qualitative prediction models based on: 1) a principal curvature map for evaluating the similarity index between sequential target bands and a reference band in the hyperspectral image cube as a full-reference metric; and 2) the cumulative probability of target colors in the HSV color space for evaluating the alignment index of a single sRGB image rendered using all of the bands of the hyperspectral image cube as a no-reference metric. We verify the efficacy of the proposed metrics on a new large-scale database, demonstrating a higher prediction accuracy in determining improved alignment compared to two full-reference and five no-reference image quality metrics. We also validate the ability of the proposed framework to improve hyperspectral face recognition.

TU-FG-209-12: Treatment Site and View Recognition in X-Ray Images with Hierarchical Multiclass Recognition Models

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

Chang, X; Mazur, T; Yang, D

Purpose: To investigate an approach of automatically recognizing anatomical sites and imaging views (the orientation of the image acquisition) in 2D X-ray images. Methods: A hierarchical (binary tree) multiclass recognition model was developed to recognize the treatment sites and views in x-ray images. From top to bottom of the tree, the treatment sites are grouped hierarchically from more general to more specific. Each node in the hierarchical model was designed to assign images to one of two categories of anatomical sites. The binary image classification function of each node in the hierarchical model is implemented by using a PCA transformationmore » and a support vector machine (SVM) model. The optimal PCA transformation matrices and SVM models are obtained by learning from a set of sample images. Alternatives of the hierarchical model were developed to support three scenarios of site recognition that may happen in radiotherapy clinics, including two or one X-ray images with or without view information. The performance of the approach was tested with images of 120 patients from six treatment sites – brain, head-neck, breast, lung, abdomen and pelvis – with 20 patients per site and two views (AP and RT) per patient. Results: Given two images in known orthogonal views (AP and RT), the hierarchical model achieved a 99% average F1 score to recognize the six sites. Site specific view recognition models have 100 percent accuracy. The computation time to process a new patient case (preprocessing, site and view recognition) is 0.02 seconds. Conclusion: The proposed hierarchical model of site and view recognition is effective and computationally efficient. It could be useful to automatically and independently confirm the treatment sites and views in daily setup x-ray 2D images. It could also be applied to guide subsequent image processing tasks, e.g. site and view dependent contrast enhancement and image registration. The senior author received research grants from ViewRay Inc. and Varian Medical System.« less

Reconstructing Face Image from the Thermal Infrared Spectrum to the Visible Spectrum †

PubMed Central

Kresnaraman, Brahmastro; Deguchi, Daisuke; Takahashi, Tomokazu; Mekada, Yoshito; Ide, Ichiro; Murase, Hiroshi

2016-01-01

During the night or in poorly lit areas, thermal cameras are a better choice instead of normal cameras for security surveillance because they do not rely on illumination. A thermal camera is able to detect a person within its view, but identification from only thermal information is not an easy task. The purpose of this paper is to reconstruct the face image of a person from the thermal spectrum to the visible spectrum. After the reconstruction, further image processing can be employed, including identification/recognition. Concretely, we propose a two-step thermal-to-visible-spectrum reconstruction method based on Canonical Correlation Analysis (CCA). The reconstruction is done by utilizing the relationship between images in both thermal infrared and visible spectra obtained by CCA. The whole image is processed in the first step while the second step processes patches in an image. Results show that the proposed method gives satisfying results with the two-step approach and outperforms comparative methods in both quality and recognition evaluations. PMID:27110781

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

PubMed Central

Nie, Haitao; Long, Kehui; Ma, Jun; Yue, Dan; Liu, Jinguo

2015-01-01

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes. PMID:25714094

A Horizontal Tilt Correction Method for Ship License Numbers Recognition

NASA Astrophysics Data System (ADS)

Liu, Baolong; Zhang, Sanyuan; Hong, Zhenjie; Ye, Xiuzi

2018-02-01

An automatic ship license numbers (SLNs) recognition system plays a significant role in intelligent waterway transportation systems since it can be used to identify ships by recognizing the characters in SLNs. Tilt occurs frequently in many SLNs because the monitors and the ships usually have great vertical or horizontal angles, which decreases the accuracy and robustness of a SLNs recognition system significantly. In this paper, we present a horizontal tilt correction method for SLNs. For an input tilt SLN image, the proposed method accomplishes the correction task through three main steps. First, a MSER-based characters’ center-points computation algorithm is designed to compute the accurate center-points of the characters contained in the input SLN image. Second, a L 1- L 2 distance-based straight line is fitted to the computed center-points using M-estimator algorithm. The tilt angle is estimated at this stage. Finally, based on the computed tilt angle, an affine transformation rotation is conducted to rotate and to correct the input SLN horizontally. At last, the proposed method is tested on 200 tilt SLN images, the proposed method is proved to be effective with a tilt correction rate of 80.5%.

Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

NASA Astrophysics Data System (ADS)

Chude-Olisah, Chollette C.; Sulong, Ghazali; Chude-Okonkwo, Uche A. K.; Hashim, Siti Z. M.

2014-12-01

Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.

Application of automatic threshold in dynamic target recognition with low contrast

NASA Astrophysics Data System (ADS)

Miao, Hua; Guo, Xiaoming; Chen, Yu

2014-11-01

Hybrid photoelectric joint transform correlator can realize automatic real-time recognition with high precision through the combination of optical devices and electronic devices. When recognizing targets with low contrast using photoelectric joint transform correlator, because of the difference of attitude, brightness and grayscale between target and template, only four to five frames of dynamic targets can be recognized without any processing. CCD camera is used to capture the dynamic target images and the capturing speed of CCD is 25 frames per second. Automatic threshold has many advantages like fast processing speed, effectively shielding noise interference, enhancing diffraction energy of useful information and better reserving outline of target and template, so this method plays a very important role in target recognition with optical correlation method. However, the automatic obtained threshold by program can not achieve the best recognition results for dynamic targets. The reason is that outline information is broken to some extent. Optimal threshold is obtained by manual intervention in most cases. Aiming at the characteristics of dynamic targets, the processing program of improved automatic threshold is finished by multiplying OTSU threshold of target and template by scale coefficient of the processed image, and combining with mathematical morphology. The optimal threshold can be achieved automatically by improved automatic threshold processing for dynamic low contrast target images. The recognition rate of dynamic targets is improved through decreased background noise effect and increased correlation information. A series of dynamic tank images with the speed about 70 km/h are adapted as target images. The 1st frame of this series of tanks can correlate only with the 3rd frame without any processing. Through OTSU threshold, the 80th frame can be recognized. By automatic threshold processing of the joint images, this number can be increased to 89 frames. Experimental results show that the improved automatic threshold processing has special application value for the recognition of dynamic target with low contrast.

An optimized content-aware image retargeting method: toward expanding the perceived visual field of the high-density retinal prosthesis recipients

NASA Astrophysics Data System (ADS)

Li, Heng; Zeng, Yajie; Lu, Zhuofan; Cao, Xiaofei; Su, Xiaofan; Sui, Xiaohong; Wang, Jing; Chai, Xinyu

2018-04-01

Objective. Retinal prosthesis devices have shown great value in restoring some sight for individuals with profoundly impaired vision, but the visual acuity and visual field provided by prostheses greatly limit recipients’ visual experience. In this paper, we employ computer vision approaches to seek to expand the perceptible visual field in patients implanted potentially with a high-density retinal prosthesis while maintaining visual acuity as much as possible. Approach. We propose an optimized content-aware image retargeting method, by introducing salient object detection based on color and intensity-difference contrast, aiming to remap important information of a scene into a small visual field and preserve their original scale as much as possible. It may improve prosthetic recipients’ perceived visual field and aid in performing some visual tasks (e.g. object detection and object recognition). To verify our method, psychophysical experiments, detecting object number and recognizing objects, are conducted under simulated prosthetic vision. As control, we use three other image retargeting techniques, including Cropping, Scaling, and seam-assisted shrinkability. Main results. Results show that our method outperforms in preserving more key features and has significantly higher recognition accuracy in comparison with other three image retargeting methods under the condition of small visual field and low-resolution. Significance. The proposed method is beneficial to expand the perceived visual field of prosthesis recipients and improve their object detection and recognition performance. It suggests that our method may provide an effective option for image processing module in future high-density retinal implants.

Application of machine learning methods for traffic signs recognition

NASA Astrophysics Data System (ADS)

Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.

2018-02-01

This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.

Road Signs Detection and Recognition Utilizing Images and 3d Point Cloud Acquired by Mobile Mapping System

NASA Astrophysics Data System (ADS)

Li, Y. H.; Shinohara, T.; Satoh, T.; Tachibana, K.

2016-06-01

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

Recognition of upper airway and surrounding structures at MRI in pediatric PCOS and OSAS

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, J. K.; Odhner, D.; Sin, Sanghun; Arens, Raanan

2013-03-01

Obstructive Sleep Apnea Syndrome (OSAS) is common in obese children with risk being 4.5 fold compared to normal control subjects. Polycystic Ovary Syndrome (PCOS) has recently been shown to be associated with OSAS that may further lead to significant cardiovascular and neuro-cognitive deficits. We are investigating image-based biomarkers to understand the architectural and dynamic changes in the upper airway and the surrounding hard and soft tissue structures via MRI in obese teenage children to study OSAS. At the previous SPIE conferences, we presented methods underlying Fuzzy Object Models (FOMs) for Automatic Anatomy Recognition (AAR) based on CT images of the thorax and the abdomen. The purpose of this paper is to demonstrate that the AAR approach is applicable to a different body region and image modality combination, namely in the study of upper airway structures via MRI. FOMs were built hierarchically, the smaller sub-objects forming the offspring of larger parent objects. FOMs encode the uncertainty and variability present in the form and relationships among the objects over a study population. Totally 11 basic objects (17 including composite) were modeled. Automatic recognition for the best pose of FOMs in a given image was implemented by using four methods - a one-shot method that does not require search, another three searching methods that include Fisher Linear Discriminate (FLD), a b-scale energy optimization strategy, and optimum threshold recognition method. In all, 30 multi-fold cross validation experiments based on 15 patient MRI data sets were carried out to assess the accuracy of recognition. The results indicate that the objects can be recognized with an average location error of less than 5 mm or 2-3 voxels. Then the iterative relative fuzzy connectedness (IRFC) algorithm was adopted for delineation of the target organs based on the recognized results. The delineation results showed an overall FP and TP volume fraction of 0.02 and 0.93.

Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

PubMed

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features

NASA Astrophysics Data System (ADS)

Wijaya, I. Gede Pasek Suta; Uchimura, Keiichi; Hu, Zhencheng

Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L2 and Lq metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.

Ear recognition from one sample per person.

PubMed

Chen, Long; Mu, Zhichun; Zhang, Baoqing; Zhang, Yi

2015-01-01

Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP) in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP) available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC) ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

[Application of image recognition technology in census of national traditional Chinese medicine resources].

PubMed

Zhang, Xiao-Bo; Ge, Xiao-Guang; Jin, Yan; Shi, Ting-Ting; Wang, Hui; Li, Meng; Jing, Zhi-Xian; Guo, Lan-Ping; Huang, Lu-Qi

2017-11-01

With the development of computer and image processing technology, image recognition technology has been applied to the national medicine resources census work at all stages.Among them: ①In the preparatory work, in order to establish a unified library of traditional Chinese medicine resources, using text recognition technology based on paper materials, be the assistant in the digitalization of various categories related to Chinese medicine resources; to determine the representative area and plots of the survey from each census team, based on the satellite remote sensing image and vegetation map and other basic data, using remote sensing image classification and other technical methods to assist in determining the key investigation area. ②In the process of field investigation, to obtain the planting area of Chinese herbal medicine was accurately, we use the decision tree model, spectral feature and object-oriented method were used to assist the regional identification and area estimation of Chinese medicinal materials.③In the process of finishing in the industry, in order to be able to relatively accurately determine the type of Chinese medicine resources in the region, based on the individual photos of the plant, the specimens and the name of the use of image recognition techniques, to assist the statistical summary of the types of traditional Chinese medicine resources. ④In the application of the results of transformation, based on the pharmaceutical resources and individual samples of medicinal herbs, the development of Chinese medicine resources to identify APP and authentic herbs 3D display system, assisted the identification of Chinese medicine resources and herbs identification characteristics. The introduction of image recognition technology in the census of Chinese medicine resources, assisting census personnel to carry out related work, not only can reduce the workload of the artificial, improve work efficiency, but also improve the census results of information technology and sharing application ability. With the deepening of the work of Chinese medicine resources census, image recognition technology in the relevant work will also play its unique role. Copyright© by the Chinese Pharmaceutical Association.

Image-algebraic design of multispectral target recognition algorithms

NASA Astrophysics Data System (ADS)

Schmalz, Mark S.; Ritter, Gerhard X.

1994-06-01

In this paper, we discuss methods for multispectral ATR (Automated Target Recognition) of small targets that are sensed under suboptimal conditions, such as haze, smoke, and low light levels. In particular, we discuss our ongoing development of algorithms and software that effect intelligent object recognition by selecting ATR filter parameters according to ambient conditions. Our algorithms are expressed in terms of IA (image algebra), a concise, rigorous notation that unifies linear and nonlinear mathematics in the image processing domain. IA has been implemented on a variety of parallel computers, with preprocessors available for the Ada and FORTRAN languages. An image algebra C++ class library has recently been made available. Thus, our algorithms are both feasible implementationally and portable to numerous machines. Analyses emphasize the aspects of image algebra that aid the design of multispectral vision algorithms, such as parameterized templates that facilitate the flexible specification of ATR filters.

An automatic iris occlusion estimation method based on high-dimensional density estimation.

PubMed

Li, Yung-Hui; Savvides, Marios

2013-04-01

Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation.

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

PubMed Central

Pham, Tuan D.

2014-01-01

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction. PMID:25157744

Modal-Power-Based Haptic Motion Recognition

NASA Astrophysics Data System (ADS)

Kasahara, Yusuke; Shimono, Tomoyuki; Kuwahara, Hiroaki; Sato, Masataka; Ohnishi, Kouhei

Motion recognition based on sensory information is important for providing assistance to human using robots. Several studies have been carried out on motion recognition based on image information. However, in the motion of humans contact with an object can not be evaluated precisely by image-based recognition. This is because the considering force information is very important for describing contact motion. In this paper, a modal-power-based haptic motion recognition is proposed; modal power is considered to reveal information on both position and force. Modal power is considered to be one of the defining features of human motion. A motion recognition algorithm based on linear discriminant analysis is proposed to distinguish between similar motions. Haptic information is extracted using a bilateral master-slave system. Then, the observed motion is decomposed in terms of primitive functions in a modal space. The experimental results show the effectiveness of the proposed method.

Real-time image restoration for iris recognition systems.

PubMed

Kang, Byung Jun; Park, Kang Ryoung

2007-12-01

In the field of biometrics, it has been reported that iris recognition techniques have shown high levels of accuracy because unique patterns of the human iris, which has very many degrees of freedom, are used. However, because conventional iris cameras have small depth-of-field (DOF) areas, input iris images can easily be blurred, which can lead to lower recognition performance, since iris patterns are transformed by the blurring caused by optical defocusing. To overcome these problems, an autofocusing camera can be used. However, this inevitably increases the cost, size, and complexity of the system. Therefore, we propose a new real-time iris image-restoration method, which can increase the camera's DOF without requiring any additional hardware. This paper presents five novelties as compared to previous works: 1) by excluding eyelash and eyelid regions, it is possible to obtain more accurate focus scores from input iris images; 2) the parameter of the point spread function (PSF) can be estimated in terms of camera optics and measured focus scores; therefore, parameter estimation is more accurate than it has been in previous research; 3) because the PSF parameter can be obtained by using a predetermined equation, iris image restoration can be done in real-time; 4) by using a constrained least square (CLS) restoration filter that considers noise, performance can be greatly enhanced; and 5) restoration accuracy can also be enhanced by estimating the weight value of the noise-regularization term of the CLS filter according to the amount of image blurring. Experimental results showed that iris recognition errors when using the proposed restoration method were greatly reduced as compared to those results achieved without restoration or those achieved using previous iris-restoration methods.

Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision.

PubMed

Wang, Jing; Li, Heng; Fu, Weizhen; Chen, Yao; Li, Liming; Lyu, Qing; Han, Tingting; Chai, Xinyu

2016-01-01

Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Grayscale image segmentation for real-time traffic sign recognition: the hardware point of view

NASA Astrophysics Data System (ADS)

Cao, Tam P.; Deng, Guang; Elton, Darrell

2009-02-01

In this paper, we study several grayscale-based image segmentation methods for real-time road sign recognition applications on an FPGA hardware platform. The performance of different image segmentation algorithms in different lighting conditions are initially compared using PC simulation. Based on these results and analysis, suitable algorithms are implemented and tested on a real-time FPGA speed sign detection system. Experimental results show that the system using segmented images uses significantly less hardware resources on an FPGA while maintaining comparable system's performance. The system is capable of processing 60 live video frames per second.

Comparative study of methods for recognition of an unknown person's action from a video sequence

NASA Astrophysics Data System (ADS)

Hori, Takayuki; Ohya, Jun; Kurumisawa, Jun

2009-02-01

This paper proposes a Tensor Decomposition Based method that can recognize an unknown person's action from a video sequence, where the unknown person is not included in the database (tensor) used for the recognition. The tensor consists of persons, actions and time-series image features. For the observed unknown person's action, one of the actions stored in the tensor is assumed. Using the motion signature obtained from the assumption, the unknown person's actions are synthesized. The actions of one of the persons in the tensor are replaced by the synthesized actions. Then, the core tensor for the replaced tensor is computed. This process is repeated for the actions and persons. For each iteration, the difference between the replaced and original core tensors is computed. The assumption that gives the minimal difference is the action recognition result. For the time-series image features to be stored in the tensor and to be extracted from the observed video sequence, the human body silhouette's contour shape based feature is used. To show the validity of our proposed method, our proposed method is experimentally compared with Nearest Neighbor rule and Principal Component analysis based method. Experiments using 33 persons' seven kinds of action show that our proposed method achieves better recognition accuracies for the seven actions than the other methods.

Target recognition and scene interpretation in image/video understanding systems based on network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2004-08-01

Vision is only a part of a system that converts visual information into knowledge structures. These structures drive the vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, which is an interpretation of visual information in terms of these knowledge models. These mechanisms provide a reliable recognition if the object is occluded or cannot be recognized as a whole. It is hard to split the entire system apart, and reliable solutions to the target recognition problems are possible only within the solution of a more generic Image Understanding Problem. Brain reduces informational and computational complexities, using implicit symbolic coding of features, hierarchical compression, and selective processing of visual information. Biologically inspired Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are parts of a single mechanism, is the most feasible for such models. It converts visual information into relational Network-Symbolic structures, avoiding artificial precise computations of 3-dimensional models. Network-Symbolic Transformations derive abstract structures, which allows for invariant recognition of an object as exemplar of a class. Active vision helps creating consistent models. Attention, separation of figure from ground and perceptual grouping are special kinds of network-symbolic transformations. Such Image/Video Understanding Systems will be reliably recognizing targets.

Study on recognition algorithm for paper currency numbers based on neural network

NASA Astrophysics Data System (ADS)

Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

2008-12-01

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

Face recognition based on matching of local features on 3D dynamic range sequences

NASA Astrophysics Data System (ADS)

Echeagaray-Patrón, B. A.; Kober, Vitaly

2016-09-01

3D face recognition has attracted attention in the last decade due to improvement of technology of 3D image acquisition and its wide range of applications such as access control, surveillance, human-computer interaction and biometric identification systems. Most research on 3D face recognition has focused on analysis of 3D still data. In this work, a new method for face recognition using dynamic 3D range sequences is proposed. Experimental results are presented and discussed using 3D sequences in the presence of pose variation. The performance of the proposed method is compared with that of conventional face recognition algorithms based on descriptors.

Foveation: an alternative method to simultaneously preserve privacy and information in face images

NASA Astrophysics Data System (ADS)

Alonso, Víctor E.; Enríquez-Caldera, Rogerio; Sucar, Luis Enrique

2017-03-01

This paper presents a real-time foveation technique proposed as an alternative method for image obfuscation while simultaneously preserving privacy in face deidentification. Relevance of the proposed technique is discussed through a comparative study of the most common distortions methods in face images and an assessment on performance and effectiveness of privacy protection. All the different techniques presented here are evaluated when they go through a face recognition software. Evaluating the data utility preservation was carried out under gender and facial expression classification. Results on quantifying the tradeoff between privacy protection and image information preservation at different obfuscation levels are presented. Comparative results using the facial expression subset of the FERET database show that the technique achieves a good tradeoff between privacy and awareness with 30% of recognition rate and a classification accuracy as high as 88% obtained from the common figures of merit using the privacy-awareness map.

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

PubMed

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

PubMed Central

Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

Gesture recognition by instantaneous surface EMG images.

PubMed

Geng, Weidong; Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Li, Jiajun

2016-11-15

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a myoelectric signal may rapidly fluctuate between voltages above and below zero. Here, we present that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with sEMG signals at a specific instant. We introduce the concept of an sEMG image spatially composed from high-density sEMG and verify our findings from a computational perspective with experiments on gesture recognition based on sEMG images with a classification scheme of a deep convolutional network. Without any windowed features, the resultant recognition accuracy of an 8-gesture within-subject test reached 89.3% on a single frame of sEMG image and reached 99.0% using simple majority voting over 40 frames with a 1,000 Hz sampling rate. Experiments on the recognition of 52 gestures of NinaPro database and 27 gestures of CSL-HDEMG database also validated that our approach outperforms state-of-the-arts methods. Our findings are a starting point for the development of more fluid and natural muscle-computer interfaces with very little observational latency. For example, active prostheses and exoskeletons based on high-density electrodes could be controlled with instantaneous responses.

Using eye movements as an index of implicit face recognition in autism spectrum disorder.

PubMed

Hedley, Darren; Young, Robyn; Brewer, Neil

2012-10-01

Individuals with an autism spectrum disorder (ASD) typically show impairment on face recognition tasks. Performance has usually been assessed using overt, explicit recognition tasks. Here, a complementary method involving eye tracking was used to examine implicit face recognition in participants with ASD and in an intelligence quotient-matched non-ASD control group. Differences in eye movement indices between target and foil faces were used as an indicator of implicit face recognition. Explicit face recognition was assessed using old-new discrimination and reaction time measures. Stimuli were faces of studied (target) or unfamiliar (foil) persons. Target images at test were either identical to the images presented at study or altered by changing the lighting, pose, or by masking with visual noise. Participants with ASD performed worse than controls on the explicit recognition task. Eye movement-based measures, however, indicated that implicit recognition may not be affected to the same degree as explicit recognition. Autism Res 2012, 5: 363-379. © 2012 International Society for Autism Research, Wiley Periodicals, Inc. © 2012 International Society for Autism Research, Wiley Periodicals, Inc.

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

Color model and method for video fire flame and smoke detection using Fisher linear discriminant

NASA Astrophysics Data System (ADS)

Wei, Yuan; Jie, Li; Jun, Fang; Yongming, Zhang

2013-02-01

Video fire detection is playing an increasingly important role in our life. But recent research is often based on a traditional RGB color model used to analyze the flame, which may be not the optimal color space for fire recognition. It is worse when we research smoke simply using gray images instead of color ones. We clarify the importance of color information for fire detection. We present a fire discriminant color (FDC) model for flame or smoke recognition based on color images. The FDC models aim to unify fire color image representation and fire recognition task into one framework. With the definition of between-class scatter matrices and within-class scatter matrices of Fisher linear discriminant, the proposed models seek to obtain one color-space-transform matrix and a discriminate projection basis vector by maximizing the ratio of these two scatter matrices. First, an iterative basic algorithm is designed to get one-component color space transformed from RGB. Then, a general algorithm is extended to generate three-component color space for further improvement. Moreover, we propose a method for video fire detection based on the models using the kNN classifier. To evaluate the recognition performance, we create a database including flame, smoke, and nonfire images for training and testing. The test experiments show that the proposed model achieves a flame verification rate receiver operating characteristic (ROC I) of 97.5% at a false alarm rate (FAR) of 1.06% and a smoke verification rate (ROC II) of 91.5% at a FAR of 1.2%, and lots of fire video experiments demonstrate that our method reaches a high accuracy for fire recognition.

Recognition of degraded handwritten digits using dynamic Bayesian networks

NASA Astrophysics Data System (ADS)

Likforman-Sulem, Laurence; Sigelle, Marc

2007-01-01

We investigate in this paper the application of dynamic Bayesian networks (DBNs) to the recognition of handwritten digits. The main idea is to couple two separate HMMs into various architectures. First, a vertical HMM and a horizontal HMM are built observing the evolving streams of image columns and image rows respectively. Then, two coupled architectures are proposed to model interactions between these two streams and to capture the 2D nature of character images. Experiments performed on the MNIST handwritten digit database show that coupled architectures yield better recognition performances than non-coupled ones. Additional experiments conducted on artificially degraded (broken) characters demonstrate that coupled architectures better cope with such degradation than non coupled ones and than discriminative methods such as SVMs.

3D palmprint data fast acquisition and recognition

NASA Astrophysics Data System (ADS)

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

2014-11-01

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

Iris Matching Based on Personalized Weight Map.

PubMed

Dong, Wenbo; Sun, Zhenan; Tan, Tieniu

2011-09-01

Iris recognition typically involves three steps, namely, iris image preprocessing, feature extraction, and feature matching. The first two steps of iris recognition have been well studied, but the last step is less addressed. Each human iris has its unique visual pattern and local image features also vary from region to region, which leads to significant differences in robustness and distinctiveness among the feature codes derived from different iris regions. However, most state-of-the-art iris recognition methods use a uniform matching strategy, where features extracted from different regions of the same person or the same region for different individuals are considered to be equally important. This paper proposes a personalized iris matching strategy using a class-specific weight map learned from the training images of the same iris class. The weight map can be updated online during the iris recognition procedure when the successfully recognized iris images are regarded as the new training data. The weight map reflects the robustness of an encoding algorithm on different iris regions by assigning an appropriate weight to each feature code for iris matching. Such a weight map trained by sufficient iris templates is convergent and robust against various noise. Extensive and comprehensive experiments demonstrate that the proposed personalized iris matching strategy achieves much better iris recognition performance than uniform strategies, especially for poor quality iris images.

Finger crease pattern recognition using Legendre moments and principal component analysis

NASA Astrophysics Data System (ADS)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis.

PubMed

Cheng, Yezeng; Larin, Kirill V

2006-12-20

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis

NASA Astrophysics Data System (ADS)

Cheng, Yezeng; Larin, Kirill V.

2006-12-01

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Context-dependent logo matching and recognition.

PubMed

Sahbi, Hichem; Ballan, Lamberto; Serra, Giuseppe; Del Bimbo, Alberto

2013-03-01

We contribute, through this paper, to the design of a novel variational framework able to match and recognize multiple instances of multiple reference logos in image archives. Reference logos and test images are seen as constellations of local features (interest points, regions, etc.) and matched by minimizing an energy function mixing: 1) a fidelity term that measures the quality of feature matching, 2) a neighborhood criterion that captures feature co-occurrence/geometry, and 3) a regularization term that controls the smoothness of the matching solution. We also introduce a detection/recognition procedure and study its theoretical consistency. Finally, we show the validity of our method through extensive experiments on the challenging MICC-Logos dataset. Our method overtakes, by 20%, baseline as well as state-of-the-art matching/recognition procedures.

A New Experiment on Bengali Character Recognition

NASA Astrophysics Data System (ADS)

Barman, Sumana; Bhattacharyya, Debnath; Jeon, Seung-Whan; Kim, Tai-Hoon; Kim, Haeng-Kon

This paper presents a method to use View based approach in Bangla Optical Character Recognition (OCR) system providing reduced data set to the ANN classification engine rather than the traditional OCR methods. It describes how Bangla characters are processed, trained and then recognized with the use of a Backpropagation Artificial neural network. This is the first published account of using a segmentation-free optical character recognition system for Bangla using a view based approach. The methodology presented here assumes that the OCR pre-processor has presented the input images to the classification engine described here. The size and the font face used to render the characters are also significant in both training and classification. The images are first converted into greyscale and then to binary images; these images are then scaled to a fit a pre-determined area with a fixed but significant number of pixels. The feature vectors are then formed extracting the characteristics points, which in this case is simply a series of 0s and 1s of fixed length. Finally, an artificial neural network is chosen for the training and classification process.

Applications of geostatistics and Markov models for logo recognition

NASA Astrophysics Data System (ADS)

Pham, Tuan

2003-01-01

Spatial covariances based on geostatistics are extracted as representative features of logo or trademark images. These spatial covariances are different from other statistical features for image analysis in that the structural information of an image is independent of the pixel locations and represented in terms of spatial series. We then design a classifier in the sense of hidden Markov models to make use of these geostatistical sequential data to recognize the logos. High recognition rates are obtained from testing the method against a public-domain logo database.

Sparse and redundant representations for inverse problems and recognition

NASA Astrophysics Data System (ADS)

Patel, Vishal M.

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented.

Flightspeed Integral Image Analysis Toolkit

NASA Technical Reports Server (NTRS)

Thompson, David R.

2009-01-01

The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles

Facial expression recognition under partial occlusion based on fusion of global and local features

NASA Astrophysics Data System (ADS)

Wang, Xiaohua; Xia, Chen; Hu, Min; Ren, Fuji

2018-04-01

Facial expression recognition under partial occlusion is a challenging research. This paper proposes a novel framework for facial expression recognition under occlusion by fusing the global and local features. In global aspect, first, information entropy are employed to locate the occluded region. Second, principal Component Analysis (PCA) method is adopted to reconstruct the occlusion region of image. After that, a replace strategy is applied to reconstruct image by replacing the occluded region with the corresponding region of the best matched image in training set, Pyramid Weber Local Descriptor (PWLD) feature is then extracted. At last, the outputs of SVM are fitted to the probabilities of the target class by using sigmoid function. For the local aspect, an overlapping block-based method is adopted to extract WLD features, and each block is weighted adaptively by information entropy, Chi-square distance and similar block summation methods are then applied to obtain the probabilities which emotion belongs to. Finally, fusion at the decision level is employed for the data fusion of the global and local features based on Dempster-Shafer theory of evidence. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the effectiveness and fault tolerance of this method.

Image Classification for Web Genre Identification

DTIC Science & Technology

2012-01-01

recognition and landscape detection using the computer vision toolkit OpenCV1. For facial recognition , we researched the possibilities of using the...method for connecting these names with a face/personal photo and logo respectively. [2] METHODOLOGY For this project, we focused primarily on facial

Introduction to computer image processing

NASA Technical Reports Server (NTRS)

Moik, J. G.

1973-01-01

Theoretical backgrounds and digital techniques for a class of image processing problems are presented. Image formation in the context of linear system theory, image evaluation, noise characteristics, mathematical operations on image and their implementation are discussed. Various techniques for image restoration and image enhancement are presented. Methods for object extraction and the problem of pictorial pattern recognition and classification are discussed.

A Benchmark and Comparative Study of Video-Based Face Recognition on COX Face Database.

PubMed

Huang, Zhiwu; Shan, Shiguang; Wang, Ruiping; Zhang, Haihong; Lao, Shihong; Kuerban, Alifu; Chen, Xilin

2015-12-01

Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX(1) Face DB. Specifically, we make three contributions. First, we collect and release a largescale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V, and V2V). Second, for benchmarking the three scenarios designed on our database, we review and experimentally compare a number of existing set-based methods. Third, we further propose a novel Point-to-Set Correlation Learning (PSCL) method, and experimentally show that it can be used as a promising baseline method for V2S/S2V face recognition on COX Face DB. Extensive experimental results clearly demonstrate that video-based face recognition needs more efforts, and our COX Face DB is a good benchmark database for evaluation.

A multi-view face recognition system based on cascade face detector and improved Dlib

NASA Astrophysics Data System (ADS)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Apparatus and method for identification and recognition of an item with ultrasonic patterns from item subsurface micro-features

DOEpatents

Perkins, Richard W.; Fuller, James L.; Doctor, Steven R.; Good, Morris S.; Heasler, Patrick G.; Skorpik, James R.; Hansen, Norman H.

1995-01-01

The present invention is a means and method for identification and recognition of an item by ultrasonic imaging of material microfeatures and/or macrofeatures within the bulk volume of a material. The invention is based upon ultrasonic interrogation and imaging of material microfeatures within the body of material by accepting only reflected ultrasonic energy from a preselected plane or volume within the material. An initial interrogation produces an identification reference. Subsequent new scans are statistically compared to the identification reference for making a match/non-match decision.

Apparatus and method for identification and recognition of an item with ultrasonic patterns from item subsurface micro-features

DOEpatents

Perkins, R.W.; Fuller, J.L.; Doctor, S.R.; Good, M.S.; Heasler, P.G.; Skorpik, J.R.; Hansen, N.H.

1995-09-26

The present invention is a means and method for identification and recognition of an item by ultrasonic imaging of material microfeatures and/or macrofeatures within the bulk volume of a material. The invention is based upon ultrasonic interrogation and imaging of material microfeatures within the body of material by accepting only reflected ultrasonic energy from a preselected plane or volume within the material. An initial interrogation produces an identification reference. Subsequent new scans are statistically compared to the identification reference for making a match/non-match decision. 15 figs.

Compensation for the signal processing characteristics of ultrasound B-mode scanners in adaptive speckle reduction.

PubMed

Crawford, D C; Bell, D S; Bamber, J C

1993-01-01

A systematic method to compensate for nonlinear amplification of individual ultrasound B-scanners has been investigated in order to optimise performance of an adaptive speckle reduction (ASR) filter for a wide range of clinical ultrasonic imaging equipment. Three potential methods have been investigated: (1) a method involving an appropriate selection of the speckle recognition feature was successful when the scanner signal processing executes simple logarithmic compressions; (2) an inverse transform (decompression) of the B-mode image was effective in correcting for the measured characteristics of image data compression when the algorithm was implemented in full floating point arithmetic; (3) characterising the behaviour of the statistical speckle recognition feature under conditions of speckle noise was found to be the method of choice for implementation of the adaptive speckle reduction algorithm in limited precision integer arithmetic. In this example, the statistical features of variance and mean were investigated. The third method may be implemented on commercially available fast image processing hardware and is also better suited for transfer into dedicated hardware to facilitate real-time adaptive speckle reduction. A systematic method is described for obtaining ASR calibration data from B-mode images of a speckle producing phantom.

Infrared face recognition based on LBP histogram and KW feature selection

NASA Astrophysics Data System (ADS)

Xie, Zhihua

2014-07-01

The conventional LBP-based feature as represented by the local binary pattern (LBP) histogram still has room for performance improvements. This paper focuses on the dimension reduction of LBP micro-patterns and proposes an improved infrared face recognition method based on LBP histogram representation. To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).

Artificial Immune System for Recognizing Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2005-01-01

A method of recognizing or classifying patterns is based on an artificial immune system (AIS), which includes an algorithm and a computational model of nonlinear dynamics inspired by the behavior of a biological immune system. The method has been proposed as the theoretical basis of the computational portion of a star-tracking system aboard a spacecraft. In that system, a newly acquired star image would be treated as an antigen that would be matched by an appropriate antibody (an entry in a star catalog). The method would enable rapid convergence, would afford robustness in the face of noise in the star sensors, would enable recognition of star images acquired in any sensor or spacecraft orientation, and would not make an excessive demand on the computational resources of a typical spacecraft. Going beyond the star-tracking application, the AIS-based pattern-recognition method is potentially applicable to pattern- recognition and -classification processes for diverse purposes -- for example, reconnaissance, detecting intruders, and mining data.

Research and implementation of finger-vein recognition algorithm

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Improvement in the Accuracy of Matching by Different Feature Subspaces in Traffic Sign Recognition

NASA Astrophysics Data System (ADS)

Ihara, Arihito; Fujiyoshi, Hironobu; Takaki, Masanari; Kumon, Hiroaki; Tamatsu, Yukimasa

A technique for recognizing traffic signs from an image taken with an in-vehicle camera has already been proposed as driver's drive assist. SIFT feature is used for traffic sign recognition, because it is robust to changes in scaling and rotating of the traffic sign. However, it is difficult to process in real-time because the computation cost of the SIFT feature extraction and matching is expensive. This paper presents a method of traffic sign recognition based on keypoint classifier by AdaBoost using PCA-SIFT features in different feature subspaces. Each subspace is constructed from gradients of traffic sign images and general images respectively. A detected keypoint is projected to both subspaces, and then the AdaBoost employs to classy into whether the keypoint is on the traffic sign or not. Experimental results show that the computation cost for keypoint matching can be reduced to about 1/2 compared with the conventional method.

Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

PubMed

Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

2015-09-01

In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Local structure-based image decomposition for feature extraction with applications to face recognition.

PubMed

Qian, Jianjun; Yang, Jian; Xu, Yong

2013-09-01

This paper presents a robust but simple image feature extraction method, called image decomposition based on local structure (IDLS). It is assumed that in the local window of an image, the macro-pixel (patch) of the central pixel, and those of its neighbors, are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images (also called structure images) according to a local structure feature vector. All the structure images, after being down-sampled for dimensionality reduction, are concatenated into one super-vector. Fisher linear discriminant analysis is then used to provide a low-dimensional, compact, and discriminative representation for each super-vector. The proposed method is applied to face recognition and examined using our real-world face image database, NUST-RWFR, and five popular, publicly available, benchmark face image databases (AR, Extended Yale B, PIE, FERET, and LFW). Experimental results show the performance advantages of IDLS over state-of-the-art algorithms.

On techniques for angle compensation in nonideal iris recognition.

PubMed

Schuckers, Stephanie A C; Schmid, Natalia A; Abhyankar, Aditya; Dorairaj, Vivekanand; Boyce, Christopher K; Hornak, Lawrence A

2007-10-01

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

Exhibits Recognition System for Combining Online Services and Offline Services

NASA Astrophysics Data System (ADS)

Ma, He; Liu, Jianbo; Zhang, Yuan; Wu, Xiaoyu

2017-10-01

In order to achieve a more convenient and accurate digital museum navigation, we have developed a real-time and online-to-offline museum exhibits recognition system using image recognition method based on deep learning. In this paper, the client and server of the system are separated and connected through the HTTP. Firstly, by using the client app in the Android mobile phone, the user can take pictures and upload them to the server. Secondly, the features of the picture are extracted using the deep learning network in the server. With the help of the features, the pictures user uploaded are classified with a well-trained SVM. Finally, the classification results are sent to the client and the detailed exhibition’s introduction corresponding to the classification results are shown in the client app. Experimental results demonstrate that the recognition accuracy is close to 100% and the computing time from the image uploading to the exhibit information show is less than 1S. By means of exhibition image recognition algorithm, our implemented exhibits recognition system can combine online detailed exhibition information to the user in the offline exhibition hall so as to achieve better digital navigation.

Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yu, Yongtao; Li, Jonathan; Wen, Chenglu; Guan, Haiyan; Luo, Huan; Wang, Cheng

2016-03-01

This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping.

PubMed

Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

2015-07-27

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

Recognition of Time Stamps on Full-Disk Hα Images Using Machine Learning Methods

NASA Astrophysics Data System (ADS)

Xu, Y.; Huang, N.; Jing, J.; Liu, C.; Wang, H.; Fu, G.

2016-12-01

Observation and understanding of the physics of the 11-year solar activity cycle and 22-year magnetic cycle are among the most important research topics in solar physics. The solar cycle is responsible for magnetic field and particle fluctuation in the near-earth environment that have been found increasingly important in affecting the living of human beings in the modern era. A systematic study of large-scale solar activities, as made possible by our rich data archive, will further help us to understand the global-scale magnetic fields that are closely related to solar cycles. The long-time-span data archive includes both full-disk and high-resolution Hα images. Prior to the widely use of CCD cameras in 1990s, 35-mm films were the major media to store images. The research group at NJIT recently finished the digitization of film data obtained by the National Solar Observatory (NSO) and Big Bear Solar Observatory (BBSO) covering the period of 1953 to 2000. The total volume of data exceeds 60 TB. To make this huge database scientific valuable, some processing and calibration are required. One of the most important steps is to read the time stamps on all of the 14 million images, which is almost impossible to be done manually. We implemented three different methods to recognize the time stamps automatically, including Optical Character Recognition (OCR), Classification Tree and TensorFlow. The latter two are known as machine learning algorithms which are very popular now a day in pattern recognition area. We will present some sample images and the results of clock recognition from all three methods.

Multi-modal imaging predicts memory performance in normal aging and cognitive decline.

PubMed

Walhovd, K B; Fjell, A M; Dale, A M; McEvoy, L K; Brewer, J; Karow, D S; Salmon, D P; Fennema-Notestine, C

2010-07-01

This study (n=161) related morphometric MR imaging, FDG-PET and APOE genotype to memory scores in normal controls (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD). Stepwise regression analyses focused on morphometric and metabolic characteristics of the episodic memory network: hippocampus, entorhinal, parahippocampal, retrosplenial, posterior cingulate, precuneus, inferior parietal, and lateral orbitofrontal cortices. In NC, hippocampal metabolism predicted learning; entorhinal metabolism predicted recognition; and hippocampal metabolism predicted recall. In MCI, thickness of the entorhinal and precuneus cortices predicted learning, while parahippocampal metabolism predicted recognition. In AD, posterior cingulate cortical thickness predicted learning, while APOE genotype predicted recognition. In the total sample, hippocampal volume and metabolism, cortical thickness of the precuneus, and inferior parietal metabolism predicted learning; hippocampal volume and metabolism, parahippocampal thickness and APOE genotype predicted recognition. Imaging methods appear complementary and differentially sensitive to memory in health and disease. Medial temporal and parietal metabolism and morphometry best explained memory variance. Medial temporal characteristics were related to learning, recall and recognition, while parietal structures only predicted learning. Copyright 2008. Published by Elsevier Inc.

Fourier transform magnitudes are unique pattern recognition templates.

PubMed

Gardenier, P H; McCallum, B C; Bates, R H

1986-01-01

Fourier transform magnitudes are commonly used in the generation of templates in pattern recognition applications. We report on recent advances in Fourier phase retrieval which are relevant to pattern recognition. We emphasise in particular that the intrinsic form of a finite, positive image is, in general, uniquely related to the magnitude of its Fourier transform. We state conditions under which the Fourier phase can be reconstructed from samples of the Fourier magnitude, and describe a method of achieving this. Computational examples of restoration of Fourier phase (and hence, by Fourier transformation, the intrinsic form of the image) from samples of the Fourier magnitude are also presented.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

PubMed

Sladojevic, Srdjan; Arsenovic, Marko; Anderla, Andras; Culibrk, Dubravko; Stefanovic, Darko

2016-01-01

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

PubMed Central

Sladojevic, Srdjan; Arsenovic, Marko; Culibrk, Dubravko; Stefanovic, Darko

2016-01-01

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

Intelligent form removal with character stroke preservation

NASA Astrophysics Data System (ADS)

Garris, Michael D.

1996-03-01

A new technique for intelligent form removal has been developed along with a new method for evaluating its impact on optical character recognition (OCR). All the dominant lines in the image are automatically detected using the Hough line transform and intelligently erased while simultaneously preserving overlapping character strokes by computing line width statistics and keying off of certain visual cues. This new method of form removal operates on loosely defined zones with no image deskewing. Any field in which the writer is provided a horizontal line to enter a response can be processed by this method. Several examples of processed fields are provided, including a comparison of results between the new method and a commercially available forms removal package. Even if this new form removal method did not improve character recognition accuracy, it is still a significant improvement to the technology because the requirement of a priori knowledge of the form's geometric details has been greatly reduced. This relaxes the recognition system's dependence on rigid form design, printing, and reproduction by automatically detecting and removing some of the physical structures (lines) on the form. Using the National Institute of Standards and Technology (NIST) public domain form-based handprint recognition system, the technique was tested on a large number of fields containing randomly ordered handprinted lowercase alphabets, as these letters (especially those with descenders) frequently touch and extend through the line along which they are written. Preserving character strokes improves overall lowercase recognition performance by 3%, which is a net improvement, but a single performance number like this doesn't communicate how the recognition process was really influenced. There is expected to be trade- offs with the introduction of any new technique into a complex recognition system. To understand both the improvements and the trade-offs, a new analysis was designed to compare the statistical distributions of individual confusion pairs between two systems. As OCR technology continues to improve, sophisticated analyses like this are necessary to reduce the errors remaining in complex recognition problems.

Detection of License Plate using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines Method

NASA Astrophysics Data System (ADS)

Astawa, INGA; Gusti Ngurah Bagus Caturbawa, I.; Made Sajayasa, I.; Dwi Suta Atmaja, I. Made Ari

2018-01-01

The license plate recognition usually used as part of system such as parking system. License plate detection considered as the most important step in the license plate recognition system. We propose methods that can be used to detect the vehicle plate on mobile phone. In this paper, we used Sliding Window, Histogram of Oriented Gradient (HOG), and Support Vector Machines (SVM) method to license plate detection so it will increase the detection level even though the image is not in a good quality. The image proceed by Sliding Window method in order to find plate position. Feature extraction in every window movement had been done by HOG and SVM method. Good result had shown in this research, which is 96% of accuracy.

Gesture recognition by instantaneous surface EMG images

PubMed Central

Geng, Weidong; Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Li, Jiajun

2016-01-01

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a myoelectric signal may rapidly fluctuate between voltages above and below zero. Here, we present that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with sEMG signals at a specific instant. We introduce the concept of an sEMG image spatially composed from high-density sEMG and verify our findings from a computational perspective with experiments on gesture recognition based on sEMG images with a classification scheme of a deep convolutional network. Without any windowed features, the resultant recognition accuracy of an 8-gesture within-subject test reached 89.3% on a single frame of sEMG image and reached 99.0% using simple majority voting over 40 frames with a 1,000 Hz sampling rate. Experiments on the recognition of 52 gestures of NinaPro database and 27 gestures of CSL-HDEMG database also validated that our approach outperforms state-of-the-arts methods. Our findings are a starting point for the development of more fluid and natural muscle-computer interfaces with very little observational latency. For example, active prostheses and exoskeletons based on high-density electrodes could be controlled with instantaneous responses. PMID:27845347

The recognition of ocean red tide with hyper-spectral-image based on EMD

NASA Astrophysics Data System (ADS)

Zhao, Wencang; Wei, Hongli; Shi, Changjiang; Ji, Guangrong

2008-05-01

A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition (EMD), from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general picture data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.

Gaze Estimation for Off-Angle Iris Recognition Based on the Biometric Eye Model

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

Karakaya, Mahmut; Barstow, Del R; Santos-Villalobos, Hector J

Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-angle iris recognition framework based on the ANONYMIZED biometric eye model. Gaze estimation is an important prerequisite step to correct an off-angle iris images. To achieve the accurate frontal reconstruction of an off-angle iris image, we first need to estimate the eye gaze direction frommore » elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.« less

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images.

PubMed

Gumaei, Abdu; Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-05-15

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang's method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

Syntactic methods of shape feature description and its application in analysis of medical images

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-02-01

The paper presents specialist algorithms of morphologic analysis of shapes of selected organs of abdominal cavity proposed in order to diagnose disease symptoms occurring in the main pancreatic ducts and upper segments of ureters. Analysis of the correct morphology of these structures has been conducted with the use of syntactic methods of pattern recognition. Its main objective is computer-aided support to early diagnosis of neoplastic lesions and pancreatitis based on images taken in the course of examination with the endoscopic retrograde cholangiopancreatography (ERCP) method and a diagnosis of morphological lesions in ureter based on kidney radiogram analysis. In the analysis of ERCP images, the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis. In the case of kidney radiogram analysis the aim is to diagnose local irregularity of ureter lumen. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of shape features description and context-free attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing into diagrams of widths of the examined structures.

Automatic Recognition of Road Signs

NASA Astrophysics Data System (ADS)

Inoue, Yasuo; Kohashi, Yuuichirou; Ishikawa, Naoto; Nakajima, Masato

2002-11-01

The increase in traffic accidents is becoming a serious social problem with the recent rapid traffic increase. In many cases, the driver"s carelessness is the primary factor of traffic accidents, and the driver assistance system is demanded for supporting driver"s safety. In this research, we propose the new method of automatic detection and recognition of road signs by image processing. The purpose of this research is to prevent accidents caused by driver"s carelessness, and call attention to a driver when the driver violates traffic a regulation. In this research, high accuracy and the efficient sign detecting method are realized by removing unnecessary information except for a road sign from an image, and detect a road sign using shape features. At first, the color information that is not used in road signs is removed from an image. Next, edges except for circular and triangle ones are removed to choose sign shape. In the recognition process, normalized cross correlation operation is carried out to the two-dimensional differentiation pattern of a sign, and the accurate and efficient method for detecting the road sign is realized. Moreover, the real-time operation in a software base was realized by holding down calculation cost, maintaining highly precise sign detection and recognition. Specifically, it becomes specifically possible to process by 0.1 sec(s)/frame using a general-purpose PC (CPU: Pentium4 1.7GHz). As a result of in-vehicle experimentation, our system could process on real time and has confirmed that detection and recognition of a sign could be performed correctly.

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Orientation estimation of anatomical structures in medical images for object recognition

NASA Astrophysics Data System (ADS)

Bağci, Ulaş; Udupa, Jayaram K.; Chen, Xinjian

2011-03-01

Recognition of anatomical structures is an important step in model based medical image segmentation. It provides pose estimation of objects and information about "where" roughly the objects are in the image and distinguishing them from other object-like entities. In,1 we presented a general method of model-based multi-object recognition to assist in segmentation (delineation) tasks. It exploits the pose relationship that can be encoded, via the concept of ball scale (b-scale), between the binary training objects and their associated grey images. The goal was to place the model, in a single shot, close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. Unlike position and scale parameters, we observe that orientation parameters require more attention when estimating the pose of the model as even small differences in orientation parameters can lead to inappropriate recognition. Motivated from the non-Euclidean nature of the pose information, we propose in this paper the use of non-Euclidean metrics to estimate orientation of the anatomical structures for more accurate recognition and segmentation. We statistically analyze and evaluate the following metrics for orientation estimation: Euclidean, Log-Euclidean, Root-Euclidean, Procrustes Size-and-Shape, and mean Hermitian metrics. The results show that mean Hermitian and Cholesky decomposition metrics provide more accurate orientation estimates than other Euclidean and non-Euclidean metrics.

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

PubMed Central

Tcheng, David K.; Nayak, Ashwin K.; Fowlkes, Charless C.; Punyasena, Surangi W.

2016-01-01

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems. PMID:26867017

Pattern recognition neural-net by spatial mapping of biology visual field

NASA Astrophysics Data System (ADS)

Lin, Xin; Mori, Masahiko

2000-05-01

The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

Effective Fingerprint Quality Estimation for Diverse Capture Sensors

PubMed Central

Xie, Shan Juan; Yoon, Sook; Shin, Jinwook; Park, Dong Sun

2010-01-01

Recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint recognition systems. The representative features to assess the quality of fingerprint images from different types of capture sensors are known to vary. In this paper, an effective quality estimation system that can be adapted for different types of capture sensors is designed by modifying and combining a set of features including orientation certainty, local orientation quality and consistency. The proposed system extracts basic features, and generates next level features which are applicable for various types of capture sensors. The system then uses the Support Vector Machine (SVM) classifier to determine whether or not an image should be accepted as input to the recognition system. The experimental results show that the proposed method can perform better than previous methods in terms of accuracy. In the meanwhile, the proposed method has an ability to eliminate residue images from the optical and capacitive sensors, and the coarse images from thermal sensors. PMID:22163632

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.

2.5D multi-view gait recognition based on point cloud registration.

PubMed

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-03-28

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM.

Aging and Emotion Recognition: Not Just a Losing Matter

PubMed Central

Sze, Jocelyn A.; Goodkind, Madeleine S.; Gyurak, Anett; Levenson, Robert W.

2013-01-01

Past studies on emotion recognition and aging have found evidence of age-related decline when emotion recognition was assessed by having participants detect single emotions depicted in static images of full or partial (e.g., eye region) faces. These tests afford good experimental control but do not capture the dynamic nature of real-world emotion recognition, which is often characterized by continuous emotional judgments and dynamic multi-modal stimuli. Research suggests that older adults often perform better under conditions that better mimic real-world social contexts. We assessed emotion recognition in young, middle-aged, and older adults using two traditional methods (single emotion judgments of static images of faces and eyes) and an additional method in which participants made continuous emotion judgments of dynamic, multi-modal stimuli (videotaped interactions between young, middle-aged, and older couples). Results revealed an age by test interaction. Largely consistent with prior research, we found some evidence that older adults performed worse than young adults when judging single emotions from images of faces (for sad and disgust faces only) and eyes (for older eyes only), with middle-aged adults falling in between. In contrast, older adults did better than young adults on the test involving continuous emotion judgments of dyadic interactions, with middle-aged adults falling in between. In tests in which target stimuli differed in age, emotion recognition was not facilitated by an age match between participant and target. These findings are discussed in terms of theoretical and methodological implications for the study of aging and emotional processing. PMID:22823183

Research of Face Recognition with Fisher Linear Discriminant

NASA Astrophysics Data System (ADS)

Rahim, R.; Afriliansyah, T.; Winata, H.; Nofriansyah, D.; Ratnadewi; Aryza, S.

2018-01-01

Face identification systems are developing rapidly, and these developments drive the advancement of biometric-based identification systems that have high accuracy. However, to develop a good face recognition system and to have high accuracy is something that’s hard to find. Human faces have diverse expressions and attribute changes such as eyeglasses, mustache, beard and others. Fisher Linear Discriminant (FLD) is a class-specific method that distinguishes facial image images into classes and also creates distance between classes and intra classes so as to produce better classification.

Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods

NASA Astrophysics Data System (ADS)

Cyganek, Boguslaw; Smolka, Bogdan

2015-02-01

In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Object recognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.

Robust kernel collaborative representation for face recognition

NASA Astrophysics Data System (ADS)

Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

2015-05-01

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation.

PubMed

Zhang, Lei; Zeng, Zhi; Ji, Qiang

2011-09-01

Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chain-like CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex real-world problems.

Fragrant pear sexuality recognition with machine vision

NASA Astrophysics Data System (ADS)

Ma, Benxue; Ying, Yibin

2006-10-01

In this research, a method to identify Kuler fragrant pear's sexuality with machine vision was developed. Kuler fragrant pear has male pear and female pear. They have an obvious difference in favor. To detect the sexuality of Kuler fragrant pear, images of fragrant pear were acquired by CCD color camera. Before feature extraction, some preprocessing is conducted on the acquired images to remove noise and unnecessary contents. Color feature, perimeter feature and area feature of fragrant pear bottom image were extracted by digital image processing technique. And the fragrant pear sexuality was determined by complexity obtained from perimeter and area. In this research, using 128 Kurle fragrant pears as samples, good recognition rate between the male pear and the female pear was obtained for Kurle pear's sexuality detection (82.8%). Result shows this method could detect male pear and female pear with a good accuracy.

Adaptive pseudo-color enhancement method of weld radiographic images based on HSI color space and self-transformation of pixels.

PubMed

Jiang, Hongquan; Zhao, Yalin; Gao, Jianmin; Gao, Zhiyong

2017-06-01

The radiographic testing (RT) image of a steam turbine manufacturing enterprise has the characteristics of low gray level, low contrast, and blurriness, which lead to a substandard image quality. Moreover, it is not conducive for human eyes to detect and evaluate defects. This study proposes an adaptive pseudo-color enhancement method for weld radiographic images based on the hue, saturation, and intensity (HSI) color space and the self-transformation of pixels to solve these problems. First, the pixel's self-transformation is performed to the pixel value of the original RT image. The function value after the pixel's self-transformation is assigned to the HSI components in the HSI color space. Thereafter, the average intensity of the enhanced image is adaptively adjusted to 0.5 according to the intensity of the original image. Moreover, the hue range and interval can be adjusted according to personal habits. Finally, the HSI components after the adaptive adjustment can be transformed to display in the red, green, and blue color space. Numerous weld radiographic images from a steam turbine manufacturing enterprise are used to validate the proposed method. The experimental results show that the proposed pseudo-color enhancement method can improve image definition and make the target and background areas distinct in weld radiographic images. The enhanced images will be more conducive for defect recognition. Moreover, the image enhanced using the proposed method conforms to the human eye visual properties, and the effectiveness of defect recognition and evaluation can be ensured.

Adaptive pseudo-color enhancement method of weld radiographic images based on HSI color space and self-transformation of pixels

NASA Astrophysics Data System (ADS)

Jiang, Hongquan; Zhao, Yalin; Gao, Jianmin; Gao, Zhiyong

2017-06-01

The radiographic testing (RT) image of a steam turbine manufacturing enterprise has the characteristics of low gray level, low contrast, and blurriness, which lead to a substandard image quality. Moreover, it is not conducive for human eyes to detect and evaluate defects. This study proposes an adaptive pseudo-color enhancement method for weld radiographic images based on the hue, saturation, and intensity (HSI) color space and the self-transformation of pixels to solve these problems. First, the pixel's self-transformation is performed to the pixel value of the original RT image. The function value after the pixel's self-transformation is assigned to the HSI components in the HSI color space. Thereafter, the average intensity of the enhanced image is adaptively adjusted to 0.5 according to the intensity of the original image. Moreover, the hue range and interval can be adjusted according to personal habits. Finally, the HSI components after the adaptive adjustment can be transformed to display in the red, green, and blue color space. Numerous weld radiographic images from a steam turbine manufacturing enterprise are used to validate the proposed method. The experimental results show that the proposed pseudo-color enhancement method can improve image definition and make the target and background areas distinct in weld radiographic images. The enhanced images will be more conducive for defect recognition. Moreover, the image enhanced using the proposed method conforms to the human eye visual properties, and the effectiveness of defect recognition and evaluation can be ensured.

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

PubMed Central

Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

2015-01-01

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work. PMID:26225994

Image pattern recognition supporting interactive analysis and graphical visualization

NASA Technical Reports Server (NTRS)

Coggins, James M.

1992-01-01

Image Pattern Recognition attempts to infer properties of the world from image data. Such capabilities are crucial for making measurements from satellite or telescope images related to Earth and space science problems. Such measurements can be the required product itself, or the measurements can be used as input to a computer graphics system for visualization purposes. At present, the field of image pattern recognition lacks a unified scientific structure for developing and evaluating image pattern recognition applications. The overall goal of this project is to begin developing such a structure. This report summarizes results of a 3-year research effort in image pattern recognition addressing the following three principal aims: (1) to create a software foundation for the research and identify image pattern recognition problems in Earth and space science; (2) to develop image measurement operations based on Artificial Visual Systems; and (3) to develop multiscale image descriptions for use in interactive image analysis.

Research on Coordinate Transformation Method of Gb-Sar Image Supported by 3d Laser Scanning Technology

NASA Astrophysics Data System (ADS)

Wang, P.; Xing, C.

2018-04-01

In the image plane of GB-SAR, identification of deformation distribution is usually carried out by artificial interpretation. This method requires analysts to have adequate experience of radar imaging and target recognition, otherwise it can easily cause false recognition of deformation target or region. Therefore, it is very meaningful to connect two-dimensional (2D) plane coordinate system with the common three-dimensional (3D) terrain coordinate system. To improve the global accuracy and reliability of the transformation from 2D coordinates of GB-SAR images to local 3D coordinates, and overcome the limitation of traditional similarity transformation parameter estimation method, 3D laser scanning data is used to assist the transformation of GB-SAR image coordinates. A straight line fitting method for calculating horizontal angle was proposed in this paper. After projection into a consistent imaging plane, we can calculate horizontal rotation angle by using the linear characteristics of the structure in radar image and the 3D coordinate system. Aided by external elevation information by 3D laser scanning technology, we completed the matching of point clouds and pixels on the projection plane according to the geometric projection principle of GB-SAR imaging realizing the transformation calculation of GB-SAR image coordinates to local 3D coordinates. Finally, the effectiveness of the method is verified by the GB-SAR deformation monitoring experiment on the high slope of Geheyan dam.

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter.

PubMed

Ye, Tao; Zhou, Fuqiang

2015-04-10

When imaged by detectors, space targets (including satellites and debris) and background stars have similar point-spread functions, and both objects appear to change as detectors track targets. Therefore, traditional tracking methods cannot separate targets from stars and cannot directly recognize targets in 2D images. Consequently, we propose an autonomous space target recognition and tracking approach using a star sensor technique and a Kalman filter (KF). A two-step method for subpixel-scale detection of star objects (including stars and targets) is developed, and the combination of the star sensor technique and a KF is used to track targets. The experimental results show that the proposed method is adequate for autonomously recognizing and tracking space targets.

Image dependency in the recognition of newly learnt faces.

PubMed

Longmore, Christopher A; Santos, Isabel M; Silva, Carlos F; Hall, Abi; Faloyin, Dipo; Little, Emily

2017-05-01

Research investigating the effect of lighting and viewpoint changes on unfamiliar and newly learnt faces has revealed that such recognition is highly image dependent and that changes in either of these leads to poor recognition accuracy. Three experiments are reported to extend these findings by examining the effect of apparent age on the recognition of newly learnt faces. Experiment 1 investigated the ability to generalize to novel ages of a face after learning a single image. It was found that recognition was best for the learnt image with performance falling the greater the dissimilarity between the study and test images. Experiments 2 and 3 examined whether learning two images aids subsequent recognition of a novel image. The results indicated that interpolation between two studied images (Experiment 2) provided some additional benefit over learning a single view, but that this did not extend to extrapolation (Experiment 3). The results from all studies suggest that recognition was driven primarily by pictorial codes and that the recognition of faces learnt from a limited number of sources operates on stored images of faces as opposed to more abstract, structural, representations.

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

The biometric-based module of smart grid system

NASA Astrophysics Data System (ADS)

Engel, E.; Kovalev, I. V.; Ermoshkina, A.

2015-10-01

Within Smart Grid concept the flexible biometric-based module base on Principal Component Analysis (PCA) and selective Neural Network is developed. The formation of the selective Neural Network the biometric-based module uses the method which includes three main stages: preliminary processing of the image, face localization and face recognition. Experiments on the Yale face database show that (i) selective Neural Network exhibits promising classification capability for face detection, recognition problems; and (ii) the proposed biometric-based module achieves near real-time face detection, recognition speed and the competitive performance, as compared to some existing subspaces-based methods.

Cross spectral, active and passive approach to face recognition for improved performance

NASA Astrophysics Data System (ADS)

Grudzien, A.; Kowalski, M.; Szustakowski, M.

2017-08-01

Biometrics is a technique for automatic recognition of a person based on physiological or behavior characteristics. Since the characteristics used are unique, biometrics can create a direct link between a person and identity, based on variety of characteristics. The human face is one of the most important biometric modalities for automatic authentication. The most popular method of face recognition which relies on processing of visual information seems to be imperfect. Thermal infrared imagery may be a promising alternative or complement to visible range imaging due to its several reasons. This paper presents an approach of combining both methods.

Support vector machine-based facial-expression recognition method combining shape and appearance

NASA Astrophysics Data System (ADS)

Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

2010-11-01

Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

Facial recognition in education system

NASA Astrophysics Data System (ADS)

Krithika, L. B.; Venkatesh, K.; Rathore, S.; Kumar, M. Harish

2017-11-01

Human beings exploit emotions comprehensively for conveying messages and their resolution. Emotion detection and face recognition can provide an interface between the individuals and technologies. The most successful applications of recognition analysis are recognition of faces. Many different techniques have been used to recognize the facial expressions and emotion detection handle varying poses. In this paper, we approach an efficient method to recognize the facial expressions to track face points and distances. This can automatically identify observer face movements and face expression in image. This can capture different aspects of emotion and facial expressions.

Gait recognition based on integral outline

NASA Astrophysics Data System (ADS)

Ming, Guan; Fang, Lv

2017-02-01

Biometric identification technology replaces traditional security technology, which has become a trend, and gait recognition also has become a hot spot of research because its feature is difficult to imitate and theft. This paper presents a gait recognition system based on integral outline of human body. The system has three important aspects: the preprocessing of gait image, feature extraction and classification. Finally, using a method of polling to evaluate the performance of the system, and summarizing the problems existing in the gait recognition and the direction of development in the future.

Why the long face? The importance of vertical image structure for biological "barcodes" underlying face recognition.

PubMed

Spence, Morgan L; Storrs, Katherine R; Arnold, Derek H

2014-07-29

Humans are experts at face recognition. The mechanisms underlying this complex capacity are not fully understood. Recently, it has been proposed that face recognition is supported by a coarse-scale analysis of visual information contained in horizontal bands of contrast distributed along the vertical image axis-a biological facial "barcode" (Dakin & Watt, 2009). A critical prediction of the facial barcode hypothesis is that the distribution of image contrast along the vertical axis will be more important for face recognition than image distributions along the horizontal axis. Using a novel paradigm involving dynamic image distortions, a series of experiments are presented examining famous face recognition impairments from selectively disrupting image distributions along the vertical or horizontal image axes. Results show that disrupting the image distribution along the vertical image axis is more disruptive for recognition than matched distortions along the horizontal axis. Consistent with the facial barcode hypothesis, these results suggest that human face recognition relies disproportionately on appropriately scaled distributions of image contrast along the vertical image axis. © 2014 ARVO.

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

PubMed

Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

2014-06-01

To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Pattern recognition and feature extraction with an optical Hough transform

NASA Astrophysics Data System (ADS)

Fernández, Ariel

2016-09-01

Pattern recognition and localization along with feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for the recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital- only methods. Starting from the integral representation of the GHT, it is possible to device an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a rotating pupil mask for orientation variation, implemented on a high-contrast spatial light modulator (SLM). Real-time (as limited by the frame rate of the device used to capture the GHT) can also be achieved, allowing for the processing of video sequences. Besides, by thresholding of the GHT (with the aid of another SLM) and inverse transforming (which is optically achieved in the incoherent system under appropriate focusing setting), the previously detected features of interest can be extracted.

Automatic image orientation detection via confidence-based integration of low-level and semantic cues.

PubMed

Luo, Jiebo; Boutell, Matthew

2005-05-01

Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.

Enhanced facial texture illumination normalization for face recognition.

PubMed

Luo, Yong; Guan, Ye-Peng

2015-08-01

An uncontrolled lighting condition is one of the most critical challenges for practical face recognition applications. An enhanced facial texture illumination normalization method is put forward to resolve this challenge. An adaptive relighting algorithm is developed to improve the brightness uniformity of face images. Facial texture is extracted by using an illumination estimation difference algorithm. An anisotropic histogram-stretching algorithm is proposed to minimize the intraclass distance of facial skin and maximize the dynamic range of facial texture distribution. Compared with the existing methods, the proposed method can more effectively eliminate the redundant information of facial skin and illumination. Extensive experiments show that the proposed method has superior performance in normalizing illumination variation and enhancing facial texture features for illumination-insensitive face recognition.

Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication.

PubMed

Dhir, L; Habib, N E; Monro, D M; Rakshit, S

2010-06-01

The purpose of this study was to investigate the effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication. Prospective non-comparative cohort study. Images of 15 subjects were captured before (enrolment), and 5, 10, and 15 min after instillation of mydriatics before routine cataract surgery. After cataract surgery, images were captured 2 weeks thereafter. Enrolled and test images (after pupillary dilation and after cataract surgery) were segmented to extract the iris. This was then unwrapped onto a rectangular format for normalization and a novel method using the Discrete Cosine Transform was applied to encode the image into binary bits. The numerical difference between two iris codes (Hamming distance, HD) was calculated. The HD between identification and enrolment codes was used as a score and was compared with a confidence threshold for specific equipment, giving a match or non-match result. The Correct Recognition Rate (CRR) and Equal Error Rates (EERs) were calculated to analyse overall system performance. After cataract surgery, perfect identification and verification was achieved, with zero false acceptance rate, zero false rejection rate, and zero EER. After pupillary dilation, non-elastic deformation occurs and a CRR of 86.67% and EER of 9.33% were obtained. Conventional circle-based localization methods are inadequate. Matching reliability decreases considerably with increase in pupillary dilation. Cataract surgery has no effect on iris pattern recognition, whereas pupil dilation may be used to defeat an iris-based authentication system.

Contributions of feature shapes and surface cues to the recognition of facial expressions.

PubMed

Sormaz, Mladen; Young, Andrew W; Andrews, Timothy J

2016-10-01

Theoretical accounts of face processing often emphasise feature shapes as the primary visual cue to the recognition of facial expressions. However, changes in facial expression also affect the surface properties of the face. In this study, we investigated whether this surface information can also be used in the recognition of facial expression. First, participants identified facial expressions (fear, anger, disgust, sadness, happiness) from images that were manipulated such that they varied mainly in shape or mainly in surface properties. We found that the categorization of facial expression is possible in either type of image, but that different expressions are relatively dependent on surface or shape properties. Next, we investigated the relative contributions of shape and surface information to the categorization of facial expressions. This employed a complementary method that involved combining the surface properties of one expression with the shape properties from a different expression. Our results showed that the categorization of facial expressions in these hybrid images was equally dependent on the surface and shape properties of the image. Together, these findings provide a direct demonstration that both feature shape and surface information make significant contributions to the recognition of facial expressions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

PubMed Central

2014-01-01

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. PMID:24693243

Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

PubMed

Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; Chen, Huiling; He, Fei; Pang, Yutong

2014-01-01

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

2.5D Multi-View Gait Recognition Based on Point Cloud Registration

PubMed Central

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-01-01

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM. PMID:24686727

Emotion-independent face recognition

NASA Astrophysics Data System (ADS)

De Silva, Liyanage C.; Esther, Kho G. P.

2000-12-01

Current face recognition techniques tend to work well when recognizing faces under small variations in lighting, facial expression and pose, but deteriorate under more extreme conditions. In this paper, a face recognition system to recognize faces of known individuals, despite variations in facial expression due to different emotions, is developed. The eigenface approach is used for feature extraction. Classification methods include Euclidean distance, back propagation neural network and generalized regression neural network. These methods yield 100% recognition accuracy when the training database is representative, containing one image representing the peak expression for each emotion of each person apart from the neutral expression. The feature vectors used for comparison in the Euclidean distance method and for training the neural network must be all the feature vectors of the training set. These results are obtained for a face database consisting of only four persons.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

PubMed Central

Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-01-01

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used. PMID:29762519

[Analysis and research of brain-computer interface experiments for imaging left-right hands movement].

PubMed

Wu, Yazhou; He, Qinghua; Huang, Hua; Zhang, Ling; Zhuo, Yu; Xie, Qi; Wu, Baoming

2008-10-01

This is a research carried out to explore a pragmatic way of BCI based imaging movement, i. e. to extract the feature of EEG for reflecting different thinking by searching suitable methods of signal extraction and recognition algorithm processing, to boost the recognition rate of communication for BCI system, and finally to establish a substantial theory and experimental support for BCI application. In this paper, different mental tasks for imaging left-right hands movement from 6 subjects were studied in three different time sections (hint keying at 2s, 1s and 0s after appearance of arrow). Then we used wavelet analysis and Feed-forward Back-propagation Neural Network (BP-NN) method for processing and analyzing the experimental data of off-line. Delay time delta t2, delta t1 and delta t0 for all subjects in the three different time sections were analyzed. There was significant difference between delta to and delta t2 or delta t1 (P<0.05), but no significant difference was noted between delta t2 and delta t1 (P>0.05). The average results of recognition rate were 65%, 86.67% and 72%, respectively. There were obviously different features for imaging left-right hands movement about 0.5-1s before actual movement; these features displayed significant difference. We got higher recognition rate of communication under the hint keying at about 1s after the appearance of arrow. These showed the feasibility of using the feature signals extracted from the project as the external control signals for BCI system, and demon strated that the project provided new ideas and methods for feature extraction and classification of mental tasks for BCI.

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

PubMed Central

Thai, Duy Hoang; Huckemann, Stephan; Gottschlich, Carsten

2016-01-01

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available. PMID:27171150

Using virtual data for training deep model for hand gesture recognition

NASA Astrophysics Data System (ADS)

Nikolaev, E. I.; Dvoryaninov, P. V.; Lensky, Y. Y.; Drozdovsky, N. S.

2018-05-01

Deep learning has shown real promise for the classification efficiency for hand gesture recognition problems. In this paper, the authors present experimental results for a deeply-trained model for hand gesture recognition through the use of hand images. The authors have trained two deep convolutional neural networks. The first architecture produces the hand position as a 2D-vector by input hand image. The second one predicts the hand gesture class for the input image. The first proposed architecture produces state of the art results with an accuracy rate of 89% and the second architecture with split input produces accuracy rate of 85.2%. In this paper, the authors also propose using virtual data for training a supervised deep model. Such technique is aimed to avoid using original labelled images in the training process. The interest of this method in data preparation is motivated by the need to overcome one of the main challenges of deep supervised learning: using a copious amount of labelled data during training.

Body-Earth Mover's Distance: A Matching-Based Approach for Sleep Posture Recognition.

PubMed

Xu, Xiaowei; Lin, Feng; Wang, Aosen; Hu, Yu; Huang, Ming-Chun; Xu, Wenyao

2016-10-01

Sleep posture is a key component in sleep quality assessment and pressure ulcer prevention. Currently, body pressure analysis has been a popular method for sleep posture recognition. In this paper, a matching-based approach, Body-Earth Mover's Distance (BEMD), for sleep posture recognition is proposed. BEMD treats pressure images as weighted 2D shapes, and combines EMD and Euclidean distance for similarity measure. Compared with existing work, sleep posture recognition is achieved with posture similarity rather than multiple features for specific postures. A pilot study is performed with 14 persons for six different postures. The experimental results show that the proposed BEMD can achieve 91.21% accuracy, which outperforms the previous method with an improvement of 8.01%.

Exploration of mineral resource deposits based on analysis of aerial and satellite image data employing artificial intelligence methods

NASA Astrophysics Data System (ADS)

Osipov, Gennady

2013-04-01

We propose a solution to the problem of exploration of various mineral resource deposits, determination of their forms / classification of types (oil, gas, minerals, gold, etc.) with the help of satellite photography of the region of interest. Images received from satellite are processed and analyzed to reveal the presence of specific signs of deposits of various minerals. Course of data processing and making forecast can be divided into some stages: Pre-processing of images. Normalization of color and luminosity characteristics, determination of the necessary contrast level and integration of a great number of separate photos into a single map of the region are performed. Construction of semantic map image. Recognition of bitmapped image and allocation of objects and primitives known to system are realized. Intelligent analysis. At this stage acquired information is analyzed with the help of a knowledge base, which contain so-called "attention landscapes" of experts. Used methods of recognition and identification of images: a) combined method of image recognition, b)semantic analysis of posterized images, c) reconstruction of three-dimensional objects from bitmapped images, d)cognitive technology of processing and interpretation of images. This stage is fundamentally new and it distinguishes suggested technology from all others. Automatic registration of allocation of experts` attention - registration of so-called "attention landscape" of experts - is the base of the technology. Landscapes of attention are, essentially, highly effective filters that cut off unnecessary information and emphasize exactly the factors used by an expert for making a decision. The technology based on denoted principles involves the next stages, which are implemented in corresponding program agents. Training mode -> Creation of base of ophthalmologic images (OI) -> Processing and making generalized OI (GOI) -> Mode of recognition and interpretation of unknown images. Training mode includes noncontact registration of eye motion, reconstruction of "attention landscape" fixed by the expert, recording the comments of the expert who is a specialist in the field of images` interpretation, and transfer this information into knowledge base.Creation of base of ophthalmologic images (OI) includes making semantic contacts from great number of OI based on analysis of OI and expert's comments.Processing of OI and making generalized OI (GOI) is realized by inductive logic algorithms and consists in synthesis of structural invariants of OI. The mode of recognition and interpretation of unknown images consists of several stages, which include: comparison of unknown image with the base of structural invariants of OI; revealing of structural invariants in unknown images; ynthesis of interpretive message of the structural invariants base and OI base (the experts` comments stored in it). We want to emphasize that the training mode does not assume special involvement of experts to teach the system - it is realized in the process of regular experts` work on image interpretation and it becomes possible after installation of a special apparatus for non contact registration of experts` attention. Consequently, the technology, which principles is described there, provides fundamentally new effective solution to the problem of exploration of mineral resource deposits based on computer analysis of aerial and satellite image data.

Human activities recognition by head movement using partial recurrent neural network

NASA Astrophysics Data System (ADS)

Tan, Henry C. C.; Jia, Kui; De Silva, Liyanage C.

2003-06-01

Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

Integration of multispectral face recognition and multi-PTZ camera automated surveillance for security applications

NASA Astrophysics Data System (ADS)

Chen, Chung-Hao; Yao, Yi; Chang, Hong; Koschan, Andreas; Abidi, Mongi

2013-06-01

Due to increasing security concerns, a complete security system should consist of two major components, a computer-based face-recognition system and a real-time automated video surveillance system. A computerbased face-recognition system can be used in gate access control for identity authentication. In recent studies, multispectral imaging and fusion of multispectral narrow-band images in the visible spectrum have been employed and proven to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. Thus, we present an automated method that specifies the optimal spectral ranges under the given illumination. Experimental results verify the consistent performance of our algorithm via the observation that an identical set of spectral band images is selected under all tested conditions. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images. In addition, once a person is authorized to enter a restricted area, we still need to continuously monitor his/her activities for the sake of security. Because pantilt-zoom (PTZ) cameras are capable of covering a panoramic area and maintaining high resolution imagery for real-time behavior understanding, researches in automated surveillance systems with multiple PTZ cameras have become increasingly important. Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and orientation among multiple PTZ cameras. To overcome this limitation, we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model. This reduces the dependence on the knowledge of intrinsic parameters of PTZ camera and relative positions. Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy as compared to Chen and Wang's method [18].

Target recognition of log-polar ladar range images using moment invariants

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jie; Yu, Haoyong

2017-01-01

The ladar range image has received considerable attentions in the automatic target recognition field. However, previous research does not cover target recognition using log-polar ladar range images. Therefore, we construct a target recognition system based on log-polar ladar range images in this paper. In this system combined moment invariants and backpropagation neural network are selected as shape descriptor and shape classifier, respectively. In order to fully analyze the effect of log-polar sampling pattern on recognition result, several comparative experiments based on simulated and real range images are carried out. Eventually, several important conclusions are drawn: (i) if combined moments are computed directly by log-polar range images, translation, rotation and scaling invariant properties of combined moments will be invalid (ii) when object is located in the center of field of view, recognition rate of log-polar range images is less sensitive to the changing of field of view (iii) as object position changes from center to edge of field of view, recognition performance of log-polar range images will decline dramatically (iv) log-polar range images has a better noise robustness than Cartesian range images. Finally, we give a suggestion that it is better to divide field of view into recognition area and searching area in the real application.

Fuzzy recognition of noncompact musical objects

NASA Astrophysics Data System (ADS)

Cristobal Salas, Alfredo; Tchernykh, Andrei

1997-03-01

This article describes and compares some techniques to extract attributes from black and white images which contain musical objects. The inertia moment, the central moments and the wavelet transform methods are used to describe the images. Two supervised neural networks are applied to classify the images: backpropagation and fuzzy backpropagation. The results are compared.

Sub-word image clustering in Farsi printed books

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2015-02-01

Most OCR systems are designed for the recognition of a single page. In case of unfamiliar font faces, low quality papers and degraded prints, the performance of these products drops sharply. However, an OCR system can use redundancy of word occurrences in large documents to improve recognition results. In this paper, we propose a sub-word image clustering method for the applications dealing with large printed documents. We assume that the whole document is printed by a unique unknown font with low quality print. Our proposed method finds clusters of equivalent sub-word images with an incremental algorithm. Due to the low print quality, we propose an image matching algorithm for measuring the distance between two sub-word images, based on Hamming distance and the ratio of the area to the perimeter of the connected components. We built a ground-truth dataset of more than 111000 sub-word images to evaluate our method. All of these images were extracted from an old Farsi book. We cluster all of these sub-words, including isolated letters and even punctuation marks. Then all centers of created clusters are labeled manually. We show that all sub-words of the book can be recognized with more than 99.7% accuracy by assigning the label of each cluster center to all of its members.

Multi-scale Imaging of Cellular and Sub-cellular Structures using Scanning Probe Recognition Microscopy.

NASA Astrophysics Data System (ADS)

Chen, Q.; Rice, A. F.

2005-03-01

Scanning Probe Recognition Microscopy is a new scanning probe capability under development within our group to reliably return to and directly interact with a specific nanobiological feature of interest. In previous work, we have successfully recognized and classified tubular versus globular biological objects from experimental atomic force microscope images using a method based on normalized central moments [ref. 1]. In this paper we extend this work to include recognition schemes appropriate for cellular and sub-cellular structures. Globular cells containing tubular actin filaments are under investigation. Thus there are differences in external/internal shapes and scales. Continuous Wavelet Transform with a differential Gaussian mother wavelet is employed for multi- scale analysis. [ref. 1] Q. Chen, V. Ayres and L. Udpa, ``Biological Investigation Using Scanning Probe Recognition Microscopy,'' Proceedings 3rd IEEE Conference on Nanotechnology, vol. 2, p 863-865 (2003).

Finger vein recognition based on personalized weight maps.

PubMed

Yang, Gongping; Xiao, Rongyang; Yin, Yilong; Yang, Lu

2013-09-10

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs). The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition.

Finger Vein Recognition Based on Personalized Weight Maps

PubMed Central

Yang, Gongping; Xiao, Rongyang; Yin, Yilong; Yang, Lu

2013-01-01

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs). The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition. PMID:24025556

Human Expertise Helps Computer Classify Images

NASA Technical Reports Server (NTRS)

Rorvig, Mark E.

1991-01-01

Two-domain method of computational classification of images requires less computation than other methods for computational recognition, matching, or classification of images or patterns. Does not require explicit computational matching of features, and incorporates human expertise without requiring translation of mental processes of classification into language comprehensible to computer. Conceived to "train" computer to analyze photomicrographs of microscope-slide specimens of leucocytes from human peripheral blood to distinguish between specimens from healthy and specimens from traumatized patients.

Evaluating a voice recognition system: finding the right product for your department.

PubMed

Freeh, M; Dewey, M; Brigham, L

2001-06-01

The Department of Radiology at the University of Utah Health Sciences Center has been in the process of transitioning from the traditional film-based department to a digital imaging department for the past 2 years. The department is now transitioning from the traditional method of dictating reports (dictation by radiologist to transcription to review and signing by radiologist) to a voice recognition system. The transition to digital operations will not be complete until we have the ability to directly interface the dictation process with the image review process. Voice recognition technology has advanced to the level where it can and should be an integral part of the new way of working in radiology and is an integral part of an efficient digital imaging department. The transition to voice recognition requires the task of identifying the product and the company that will best meet a department's needs. This report introduces the methods we used to evaluate the vendors and the products available as we made our purchasing decision. We discuss our evaluation method and provide a checklist that can be used by other departments to assist with their evaluation process. The criteria used in the evaluation process fall into the following major categories: user operations, technical infrastructure, medical dictionary, system interfaces, service support, cost, and company strength. Conclusions drawn from our evaluation process will be detailed, with the intention being to shorten the process for others as they embark on a similar venture. As more and more organizations investigate the many products and services that are now being offered to enhance the operations of a radiology department, it becomes increasingly important that solid methods are used to most effectively evaluate the new products. This report should help others complete the task of evaluating a voice recognition system and may be adaptable to other products as well.

Locally linear regression for pose-invariant face recognition.

PubMed

Chai, Xiujuan; Shan, Shiguang; Chen, Xilin; Gao, Wen

2007-07-01

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.

Retina vascular network recognition

NASA Astrophysics Data System (ADS)

Tascini, Guido; Passerini, Giorgio; Puliti, Paolo; Zingaretti, Primo

1993-09-01

The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.

Predicting tool life in turning operations using neural networks and image processing

NASA Astrophysics Data System (ADS)

Mikołajczyk, T.; Nowicki, K.; Bustillo, A.; Yu Pimenov, D.

2018-05-01

A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

Sparse Feature Extraction for Pose-Tolerant Face Recognition.

PubMed

Abiantun, Ramzi; Prabhu, Utsav; Savvides, Marios

2014-10-01

Automatic face recognition performance has been steadily improving over years of research, however it remains significantly affected by a number of factors such as illumination, pose, expression, resolution and other factors that can impact matching scores. The focus of this paper is the pose problem which remains largely overlooked in most real-world applications. Specifically, we focus on one-to-one matching scenarios where a query face image of a random pose is matched against a set of gallery images. We propose a method that relies on two fundamental components: (a) A 3D modeling step to geometrically correct the viewpoint of the face. For this purpose, we extend a recent technique for efficient synthesis of 3D face models called 3D Generic Elastic Model. (b) A sparse feature extraction step using subspace modeling and ℓ1-minimization to induce pose-tolerance in coefficient space. This in return enables the synthesis of an equivalent frontal-looking face, which can be used towards recognition. We show significant performance improvements in verification rates compared to commercial matchers, and also demonstrate the resilience of the proposed method with respect to degrading input quality. We find that the proposed technique is able to match non-frontal images to other non-frontal images of varying angles.

Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

2001-03-01

This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

Analysis Of The IJCNN 2011 UTL Challenge

DTIC Science & Technology

2012-01-13

large datasets from various application domains: handwriting recognition, image recognition, video processing, text processing, and ecology. The goal...validation and final evaluation sets consist of 4096 examples each. Dataset Domain Features Sparsity Devel. Transf. AVICENNA Handwriting 120 0% 150205...documents [3]. Transfer learning methods could accelerate the application of handwriting recognizers to historical manuscript by reducing the need for

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images.

PubMed

Niioka, Hirohiko; Asatani, Satoshi; Yoshimura, Aina; Ohigashi, Hironori; Tagawa, Seiichi; Miyake, Jun

2018-01-01

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

Textual blocks rectification method based on fast Hough transform analysis in identity documents recognition

NASA Astrophysics Data System (ADS)

Bezmaternykh, P. V.; Nikolaev, D. P.; Arlazarov, V. L.

2018-04-01

Textual blocks rectification or slant correction is an important stage of document image processing in OCR systems. This paper considers existing methods and introduces an approach for the construction of such algorithms based on Fast Hough Transform analysis. A quality measurement technique is proposed and obtained results are shown for both printed and handwritten textual blocks processing as a part of an industrial system of identity documents recognition on mobile devices.

A DFT-Based Method of Feature Extraction for Palmprint Recognition

NASA Astrophysics Data System (ADS)

Choge, H. Kipsang; Karungaru, Stephen G.; Tsuge, Satoru; Fukumi, Minoru

Over the last quarter century, research in biometric systems has developed at a breathtaking pace and what started with the focus on the fingerprint has now expanded to include face, voice, iris, and behavioral characteristics such as gait. Palmprint is one of the most recent additions, and is currently the subject of great research interest due to its inherent uniqueness, stability, user-friendliness and ease of acquisition. This paper describes an effective and procedurally simple method of palmprint feature extraction specifically for palmprint recognition, although verification experiments are also conducted. This method takes advantage of the correspondences that exist between prominent palmprint features or objects in the spatial domain with those in the frequency or Fourier domain. Multi-dimensional feature vectors are formed by extracting a GA-optimized set of points from the 2-D Fourier spectrum of the palmprint images. The feature vectors are then used for palmprint recognition, before and after dimensionality reduction via the Karhunen-Loeve Transform (KLT). Experiments performed using palmprint images from the ‘PolyU Palmprint Database’ indicate that using a compact set of DFT coefficients, combined with KLT and data preprocessing, produces a recognition accuracy of more than 98% and can provide a fast and effective technique for personal identification.

Aptamer-recognized carbohydrates on the cell membrane revealed by super-resolution microscopy.

PubMed

Jing, Yingying; Cai, Mingjun; Xu, Haijiao; Zhou, Lulu; Yan, Qiuyan; Gao, Jing; Wang, Hongda

2018-04-26

Carbohydrates are one of the most important components on the cell membrane, which participate in various physiological activities, and their aberrant expression is a consequence of pathological changes. In previous studies, carbohydrate analysis basically relied on lectins. However, discrimination between lectins still exists due to their multivalent character. Furthermore, the structures obtained by carbohydrate-lectin crosslinking confuse our direct observation to some extent. Fortunately, the emergence of aptamers, which are smaller and more flexible, has provided us an unprecedented choice. Herein, an aptamer recognition method with high precise localization was developed for imaging membrane-bound N-acetylgalactosamine (GalNAc). By using direct stochastic optical reconstruction microscopy (dSTORM), we compared this aptamer recognition method with the lectin recognition method for visualizing the detailed structure of GalNAc at the nanometer scale. The results indicated that GalNAc forms irregular clusters on the cell membrane with a resolution of 23 ± 7 nm by aptamer recognition. Additionally, when treated with N-acetylgalactosidase, the aptamer-recognized GalNAc shows a more significant decrease in cluster size and localization density, thus verifying better specificity of aptamers than lectins. Collectively, our study suggests that aptamers can act as perfect substitutes for lectins in carbohydrate labeling, which will be of great potential value in the field of super-resolution fluorescence imaging.

Learning Compact Binary Face Descriptor for Face Recognition.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Xiuzhuang; Zhou, Jie

2015-10-01

Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.

A Case Study on Attribute Recognition of Heated Metal Mark Image Using Deep Convolutional Neural Networks.

PubMed

Mao, Keming; Lu, Duo; E, Dazhi; Tan, Zhenhua

2018-06-07

Heated metal mark is an important trace to identify the cause of fire. However, traditional methods mainly focus on the knowledge of physics and chemistry for qualitative analysis and make it still a challenging problem. This paper presents a case study on attribute recognition of the heated metal mark image using computer vision and machine learning technologies. The proposed work is composed of three parts. Material is first generated. According to national standards, actual needs and feasibility, seven attributes are selected for research. Data generation and organization are conducted, and a small size benchmark dataset is constructed. A recognition model is then implemented. Feature representation and classifier construction methods are introduced based on deep convolutional neural networks. Finally, the experimental evaluation is carried out. Multi-aspect testings are performed with various model structures, data augments, training modes, optimization methods and batch sizes. The influence of parameters, recognitio efficiency and execution time are also analyzed. The results show that with a fine-tuned model, the recognition rate of attributes metal type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity are 0.925, 0.908, 0.835, 0.917, 0.928, 0.805 and 0.92, respectively. The proposed method recognizes the attribute of heated metal mark with preferable effect, and it can be used in practical application.

[Research progress of multi-model medical image fusion and recognition].

PubMed

Zhou, Tao; Lu, Huiling; Chen, Zhiqiang; Ma, Jingxian

2013-10-01

Medical image fusion and recognition has a wide range of applications, such as focal location, cancer staging and treatment effect assessment. Multi-model medical image fusion and recognition are analyzed and summarized in this paper. Firstly, the question of multi-model medical image fusion and recognition is discussed, and its advantage and key steps are discussed. Secondly, three fusion strategies are reviewed from the point of algorithm, and four fusion recognition structures are discussed. Thirdly, difficulties, challenges and possible future research direction are discussed.

Off-Angle Iris Correction Methods

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

Santos-Villalobos, Hector J; Thompson, Joseph T; Karakaya, Mahmut

In many real world iris recognition systems obtaining consistent frontal images is problematic do to inexperienced or uncooperative users, untrained operators, or distracting environments. As a result many collected images are unusable by modern iris matchers. In this chapter we present four methods for correcting off-angle iris images to appear frontal which makes them compatible with existing iris matchers. The methods include an affine correction, a retraced model of the human eye, measured displacements, and a genetic algorithm optimized correction. The affine correction represents a simple way to create an iris image that appears frontal but it does not accountmore » for refractive distortions of the cornea. The other method account for refraction. The retraced model simulates the optical properties of the cornea. The other two methods are data driven. The first uses optical flow to measure the displacements of the iris texture when compared to frontal images of the same subject. The second uses a genetic algorithm to learn a mapping that optimizes the Hamming Distance scores between off-angle and frontal images. In this paper we hypothesize that the biological model presented in our earlier work does not adequately account for all variations in eye anatomy and therefore the two data-driven approaches should yield better performance. Results are presented using the commercial VeriEye matcher that show that the genetic algorithm method clearly improves over prior work and makes iris recognition possible up to 50 degrees off-angle.« less

Scene text recognition in mobile applications by character descriptor and structure configuration.

PubMed

Yi, Chucai; Tian, Yingli

2014-07-01

Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This paper proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods.

Spoofing detection on facial images recognition using LBP and GLCM combination

NASA Astrophysics Data System (ADS)

Sthevanie, F.; Ramadhani, K. N.

2018-03-01

The challenge for the facial based security system is how to detect facial image falsification such as facial image spoofing. Spoofing occurs when someone try to pretend as a registered user to obtain illegal access and gain advantage from the protected system. This research implements facial image spoofing detection method by analyzing image texture. The proposed method for texture analysis combines the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) method. The experimental results show that spoofing detection using LBP and GLCM combination achieves high detection rate compared to that of using only LBP feature or GLCM feature.

Sorted Index Numbers for Privacy Preserving Face Recognition

NASA Astrophysics Data System (ADS)

Wang, Yongjin; Hatzinakos, Dimitrios

2009-12-01

This paper presents a novel approach for changeable and privacy preserving face recognition. We first introduce a new method of biometric matching using the sorted index numbers (SINs) of feature vectors. Since it is impossible to recover any of the exact values of the original features, the transformation from original features to the SIN vectors is noninvertible. To address the irrevocable nature of biometric signals whilst obtaining stronger privacy protection, a random projection-based method is employed in conjunction with the SIN approach to generate changeable and privacy preserving biometric templates. The effectiveness of the proposed method is demonstrated on a large generic data set, which contains images from several well-known face databases. Extensive experimentation shows that the proposed solution may improve the recognition accuracy.

Robust image matching via ORB feature and VFC for mismatch removal

NASA Astrophysics Data System (ADS)

Ma, Tao; Fu, Wenxing; Fang, Bin; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

Modeling of biologically motivated self-learning equivalent-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for image fragments clustering and recognition

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

2018-03-01

The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference array) and fragments with diferent dimensions for clustering is carried out. The experiments, using the software environment Mathcad showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. The experimental results show that such models can be successfully used for auto- and hetero-associative recognition. Also they can be used to explain some mechanisms, known as "the reinforcementinhibition concept". Also we demonstrate a real model experiments, which confirm that the nonlinear processing by equivalent function allow to determine the neuron-winners and customize the weight matrix. At the end of the report, we will show how to use the obtained results and to propose new more efficient hardware architecture of SL_EC_RMNS based on matrix-tensor multipliers. Also we estimate the parameters and performance of such architectures.

Designing a composite correlation filter based on iterative optimization of training images for distortion invariant face recognition

NASA Astrophysics Data System (ADS)

Wang, Q.; Elbouz, M.; Alfalou, A.; Brosseau, C.

2017-06-01

We present a novel method to optimize the discrimination ability and noise robustness of composite filters. This method is based on the iterative preprocessing of training images which can extract boundary and detailed feature information of authentic training faces, thereby improving the peak-to-correlation energy (PCE) ratio of authentic faces and to be immune to intra-class variance and noise interference. By adding the training images directly, one can obtain a composite template with high discrimination ability and robustness for face recognition task. The proposed composite correlation filter does not involve any complicated mathematical analysis and computation which are often required in the design of correlation algorithms. Simulation tests have been conducted to check the effectiveness and feasibility of our proposal. Moreover, to assess robustness of composite filters using receiver operating characteristic (ROC) curves, we devise a new method to count the true positive and false positive rates for which the difference between PCE and threshold is involved.

Fuzzy based finger vein recognition with rotation invariant feature matching

NASA Astrophysics Data System (ADS)

Ezhilmaran, D.; Joseph, Rose Bindu

2017-11-01

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

Advances in the processing of policromat images as diagnostic method to determine white spot syndrome virus in white shrimp (Litopenaeus vannamei)

NASA Astrophysics Data System (ADS)

Chavez-Sanchez, Cristina M.; Alvarez-Borrego, Josue; Montoya-Rodriguez, L.; Garcia-Gasca, A.; Fajer Avila, Emma J.; Pacheco-Marges, R.

2004-10-01

White spot syndrome (WSSV) is a viral disease which affects many crustacean species including commercial shrimps. Adequate, precise and quick methods to diagnose on time the presence of the disease in order to apply different strategies to avoid the dispersion and to reduce mortalities is necessary. Histopathology is an important diagnostic method. However, histopathology has the problem that requires time to prepare the histological slides and time to arrive to some diagnosis because this depend on the nature of the tissues, the pathogen(s) to find, the number of organisms, number of slides to analyze and the skill of the technician. This paper try to demonstrate the sensibility of one digital system of processing and recognition of images using color correlation with phase filters, to identify inclusion bodies of WSSV. Infected tissues were processed to obtain histological slides and to verify that the inclusion bodies observed were of WSV, in situ hybridization were carried out. The sensibility results of the recognition of the inclusion bodies of WSSV with the color correlation program was 86.1%. The highest percentage of recognition was in nervous system and tegument glands with 100%. The values in the stomach epithelium and heart tissue was 78.45% of recognition. Tissues with the lowest recognition values were lymphoid organ and hematopoietic tissue. It is necessary further studies to increase the sensibility and to obtain the specificity.

Reading recognition of pointer meter based on pattern recognition and dynamic three-points on a line

NASA Astrophysics Data System (ADS)

Zhang, Yongqiang; Ding, Mingli; Fu, Wuyifang; Li, Yongqiang

2017-03-01

Pointer meters are frequently applied to industrial production for they are directly readable. They should be calibrated regularly to ensure the precision of the readings. Currently the method of manual calibration is most frequently adopted to accomplish the verification of the pointer meter, and professional skills and subjective judgment may lead to big measurement errors and poor reliability and low efficiency, etc. In the past decades, with the development of computer technology, the skills of machine vision and digital image processing have been applied to recognize the reading of the dial instrument. In terms of the existing recognition methods, all the parameters of dial instruments are supposed to be the same, which is not the case in practice. In this work, recognition of pointer meter reading is regarded as an issue of pattern recognition. We obtain the features of a small area around the detected point, make those features as a pattern, divide those certified images based on Gradient Pyramid Algorithm, train a classifier with the support vector machine (SVM) and complete the pattern matching of the divided mages. Then we get the reading of the pointer meter precisely under the theory of dynamic three points make a line (DTPML), which eliminates the error caused by tiny differences of the panels. Eventually, the result of the experiment proves that the proposed method in this work is superior to state-of-the-art works.

New method of contour image processing based on the formalism of spiral light beams

NASA Astrophysics Data System (ADS)

Volostnikov, Vladimir G.; Kishkin, S. A.; Kotova, S. P.

2013-07-01

The possibility of applying the mathematical formalism of spiral light beams to the problems of contour image recognition is theoretically studied. The advantages and disadvantages of the proposed approach are evaluated; the results of numerical modelling are presented.

Recognition of children on age-different images: Facial morphology and age-stable features.

PubMed

Caplova, Zuzana; Compassi, Valentina; Giancola, Silvio; Gibelli, Daniele M; Obertová, Zuzana; Poppa, Pasquale; Sala, Remo; Sforza, Chiarella; Cattaneo, Cristina

2017-07-01

The situation of missing children is one of the most emotional social issues worldwide. The search for and identification of missing children is often hampered, among others, by the fact that the facial morphology of long-term missing children changes as they grow. Nowadays, the wide coverage by surveillance systems potentially provides image material for comparisons with images of missing children that may facilitate identification. The aim of study was to identify whether facial features are stable in time and can be utilized for facial recognition by comparing facial images of children at different ages as well as to test the possible use of moles in recognition. The study was divided into two phases (1) morphological classification of facial features using an Anthropological Atlas; (2) algorithm developed in MATLAB® R2014b for assessing the use of moles as age-stable features. The assessment of facial features by Anthropological Atlases showed high mismatch percentages among observers. On average, the mismatch percentages were lower for features describing shape than for those describing size. The nose tip cleft and the chin dimple showed the best agreement between observers regarding both categorization and stability over time. Using the position of moles as a reference point for recognition of the same person on age-different images seems to be a useful method in terms of objectivity and it can be concluded that moles represent age-stable facial features that may be considered for preliminary recognition. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

Embedded Palmprint Recognition System Using OMAP 3530

PubMed Central

Shen, Linlin; Wu, Shipei; Zheng, Songhao; Ji, Zhen

2012-01-01

We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance. PMID:22438721

Embedded palmprint recognition system using OMAP 3530.

PubMed

Shen, Linlin; Wu, Shipei; Zheng, Songhao; Ji, Zhen

2012-01-01

We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the central pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance.

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor.

PubMed

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-03-23

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works.

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor

PubMed Central

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-01-01

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works. PMID:29570690

Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

PubMed

Yi, Jizheng; Mao, Xia; Chen, Lijiang; Xue, Yuli; Rovetta, Alberto; Caleanu, Catalin-Daniel

2015-01-01

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.

Class Energy Image Analysis for Video Sensor-Based Gait Recognition: A Review

PubMed Central

Lv, Zhuowen; Xing, Xianglei; Wang, Kejun; Guan, Donghai

2015-01-01

Gait is a unique perceptible biometric feature at larger distances, and the gait representation approach plays a key role in a video sensor-based gait recognition system. Class Energy Image is one of the most important gait representation methods based on appearance, which has received lots of attentions. In this paper, we reviewed the expressions and meanings of various Class Energy Image approaches, and analyzed the information in the Class Energy Images. Furthermore, the effectiveness and robustness of these approaches were compared on the benchmark gait databases. We outlined the research challenges and provided promising future directions for the field. To the best of our knowledge, this is the first review that focuses on Class Energy Image. It can provide a useful reference in the literature of video sensor-based gait representation approach. PMID:25574935

An Interactive Procedure to Preserve the Desired Edges during the Image Processing of Noise Reduction

NASA Astrophysics Data System (ADS)

Hsu, Chih-Yu; Huang, Hsuan-Yu; Lee, Lin-Tsang

2010-12-01

The paper propose a new procedure including four stages in order to preserve the desired edges during the image processing of noise reduction. A denoised image can be obtained from a noisy image at the first stage of the procedure. At the second stage, an edge map can be obtained by the Canny edge detector to find the edges of the object contours. Manual modification of an edge map at the third stage is optional to capture all the desired edges of the object contours. At the final stage, a new method called Edge Preserved Inhomogeneous Diffusion Equation (EPIDE) is used to smooth the noisy images or the previously denoised image at the first stage for achieving the edge preservation. The Optical Character Recognition (OCR) results in the experiments show that the proposed procedure has the best recognition result because of the capability of edge preservation.

Component-based subspace linear discriminant analysis method for face recognition with one training sample

NASA Astrophysics Data System (ADS)

Huang, Jian; Yuen, Pong C.; Chen, Wen-Sheng; Lai, J. H.

2005-05-01

Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging.

Automatic anatomy recognition in post-tonsillectomy MR images of obese children with OSAS

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, Jayaram K.; Odhner, Dewey; Sin, Sanghun; Arens, Raanan

2015-03-01

Automatic Anatomy Recognition (AAR) is a recently developed approach for the automatic whole body wide organ segmentation. We previously tested that methodology on image cases with some pathology where the organs were not distorted significantly. In this paper, we present an advancement of AAR to handle organs which may have been modified or resected by surgical intervention. We focus on MRI of the neck in pediatric Obstructive Sleep Apnea Syndrome (OSAS). The proposed method consists of an AAR step followed by support vector machine techniques to detect the presence/absence of organs. The AAR step employs a hierarchical organization of the organs for model building. For each organ, a fuzzy model over a population is built. The model of the body region is then described in terms of the fuzzy models and a host of other descriptors which include parent to offspring relationship estimated over the population. Organs are recognized following the organ hierarchy by using an optimal threshold based search. The SVM step subsequently checks for evidence of the presence of organs. Experimental results show that AAR techniques can be combined with machine learning strategies within the AAR recognition framework for good performance in recognizing missing organs, in our case missing tonsils in post-tonsillectomy images as well as in simulating tonsillectomy images. The previous recognition performance is maintained achieving an organ localization accuracy of within 1 voxel when the organ is actually not removed. To our knowledge, no methods have been reported to date for handling significantly deformed or missing organs, especially in neck MRI.

Target recognition of ladar range images using slice image: comparison of four improved algorithms

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jingya; Wang, Liang; Zhai, Yu; Cheng, Yang

2017-07-01

Compared with traditional 3-D shape data, ladar range images possess properties of strong noise, shape degeneracy, and sparsity, which make feature extraction and representation difficult. The slice image is an effective feature descriptor to resolve this problem. We propose four improved algorithms on target recognition of ladar range images using slice image. In order to improve resolution invariance of the slice image, mean value detection instead of maximum value detection is applied in these four improved algorithms. In order to improve rotation invariance of the slice image, three new improved feature descriptors-which are feature slice image, slice-Zernike moments, and slice-Fourier moments-are applied to the last three improved algorithms, respectively. Backpropagation neural networks are used as feature classifiers in the last two improved algorithms. The performance of these four improved recognition systems is analyzed comprehensively in the aspects of the three invariances, recognition rate, and execution time. The final experiment results show that the improvements for these four algorithms reach the desired effect, the three invariances of feature descriptors are not directly related to the final recognition performance of recognition systems, and these four improved recognition systems have different performances under different conditions.

Geometric shapes inversion method of space targets by ISAR image segmentation

NASA Astrophysics Data System (ADS)

Huo, Chao-ying; Xing, Xiao-yu; Yin, Hong-cheng; Li, Chen-guang; Zeng, Xiang-yun; Xu, Gao-gui

2017-11-01

The geometric shape of target is an effective characteristic in the process of space targets recognition. This paper proposed a method of shape inversion of space target based on components segmentation from ISAR image. The Radon transformation, Hough transformation, K-means clustering, triangulation will be introduced into ISAR image processing. Firstly, we use Radon transformation and edge detection to extract space target's main body spindle and solar panel spindle from ISAR image. Then the targets' main body, solar panel, rectangular and circular antenna are segmented from ISAR image based on image detection theory. Finally, the sizes of every structural component are computed. The effectiveness of this method is verified using typical targets' simulation data.

Iris recognition via plenoptic imaging

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

Santos-Villalobos, Hector J.; Boehnen, Chris Bensing; Bolme, David S.

Iris recognition can be accomplished for a wide variety of eye images by using plenoptic imaging. Using plenoptic technology, it is possible to correct focus after image acquisition. One example technology reconstructs images having different focus depths and stitches them together, resulting in a fully focused image, even in an off-angle gaze scenario. Another example technology determines three-dimensional data for an eye and incorporates it into an eye model used for iris recognition processing. Another example technology detects contact lenses. Application of the technologies can result in improved iris recognition under a wide variety of scenarios.

Intelligent Detection of Structure from Remote Sensing Images Based on Deep Learning Method

NASA Astrophysics Data System (ADS)

Xin, L.

2018-04-01

Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

Fuzzy Set Methods for Object Recognition in Space Applications

NASA Technical Reports Server (NTRS)

Keller, James M. (Editor)

1992-01-01

Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms.

Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment

NASA Astrophysics Data System (ADS)

Madokoro, H.; Tsukada, M.; Sato, K.

2013-07-01

This paper presents an unsupervised learning-based object category formation and recognition method for mobile robot vision. Our method has the following features: detection of feature points and description of features using a scale-invariant feature transform (SIFT), selection of target feature points using one class support vector machines (OC-SVMs), generation of visual words using self-organizing maps (SOMs), formation of labels using adaptive resonance theory 2 (ART-2), and creation and classification of categories on a category map of counter propagation networks (CPNs) for visualizing spatial relations between categories. Classification results of dynamic images using time-series images obtained using two different-size robots and according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.

Painting recognition with smartphones equipped with inertial measurement unit

NASA Astrophysics Data System (ADS)

Masiero, Andrea; Guarnieri, Alberto; Pirotti, Francesco; Vettore, Antonio

2015-06-01

Recently, several works have been proposed in the literature to take advantage of the diffusion of smartphones to improve people experience during museum visits. The rationale is that of substituting traditional written/audio guides with interactive electronic guides usable on a mobile phone. Augmented reality systems are usually considered to make the use of such electronic guides more effective for the user. The main goal of such augmented reality system (i.e. providing the user with the information of his/her interest) is usually achieved by properly executing the following three tasks: recognizing the object of interest to the user, retrieving the most relevant information about it, properly presenting the retrieved information. This paper focuses on the first task: we consider the problem of painting recognition by means of measure- ments provided by a smartphone. We assume that the user acquires one image of the painting of interest with the standard camera of the device. This image is compared with a set of reference images of the museum objects in order to recognize the object of interest to the user. Since comparing images taken in different conditions can lead to unsatisfactory recognition results, the acquired image is typically properly transformed in order to improve the results of the recognition system: first, the system estimates the homography between properly matched features in the two images. Then, the user image is transformed accordingly to the estimated homography. Finally, it is compared with the reference one. This work proposes a novel method to exploit inertial measurement unit (IMU) measurements to improve the system performance, in particular in terms of computational load reduction: IMU measurements are exploited to reduce both the computational burden required to estimate the transformation to be applied to the user image, and the number of reference images to be compared with it.

Three-dimensional deformable-model-based localization and recognition of road vehicles.

PubMed

Zhang, Zhaoxiang; Tan, Tieniu; Huang, Kaiqi; Wang, Yunhong

2012-01-01

We address the problem of model-based object recognition. Our aim is to localize and recognize road vehicles from monocular images or videos in calibrated traffic scenes. A 3-D deformable vehicle model with 12 shape parameters is set up as prior information, and its pose is determined by three parameters, which are its position on the ground plane and its orientation about the vertical axis under ground-plane constraints. An efficient local gradient-based method is proposed to evaluate the fitness between the projection of the vehicle model and image data, which is combined into a novel evolutionary computing framework to estimate the 12 shape parameters and three pose parameters by iterative evolution. The recovery of pose parameters achieves vehicle localization, whereas the shape parameters are used for vehicle recognition. Numerous experiments are conducted in this paper to demonstrate the performance of our approach. It is shown that the local gradient-based method can evaluate accurately and efficiently the fitness between the projection of the vehicle model and the image data. The evolutionary computing framework is effective for vehicles of different types and poses is robust to all kinds of occlusion.

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

Sparse aperture 3D passive image sensing and recognition

NASA Astrophysics Data System (ADS)

Daneshpanah, Mehdi

The way we perceive, capture, store, communicate and visualize the world has greatly changed in the past century Novel three dimensional (3D) imaging and display systems are being pursued both in academic and industrial settings. In many cases, these systems have revolutionized traditional approaches and/or enabled new technologies in other disciplines including medical imaging and diagnostics, industrial metrology, entertainment, robotics as well as defense and security. In this dissertation, we focus on novel aspects of sparse aperture multi-view imaging systems and their application in quantum-limited object recognition in two separate parts. In the first part, two concepts are proposed. First a solution is presented that involves a generalized framework for 3D imaging using randomly distributed sparse apertures. Second, a method is suggested to extract the profile of objects in the scene through statistical properties of the reconstructed light field. In both cases, experimental results are presented that demonstrate the feasibility of the techniques. In the second part, the application of 3D imaging systems in sensing and recognition of objects is addressed. In particular, we focus on the scenario in which only 10s of photons reach the sensor from the object of interest, as opposed to hundreds of billions of photons in normal imaging conditions. At this level, the quantum limited behavior of light will dominate and traditional object recognition practices may fail. We suggest a likelihood based object recognition framework that incorporates the physics of sensing at quantum-limited conditions. Sensor dark noise has been modeled and taken into account. This framework is applied to 3D sensing of thermal objects using visible spectrum detectors. Thermal objects as cold as 250K are shown to provide enough signature photons to be sensed and recognized within background and dark noise with mature, visible band, image forming optics and detector arrays. The results suggest that one might not need to venture into exotic and expensive detector arrays and associated optics for sensing room-temperature thermal objects in complete darkness.

Detection of insect damage in almonds

NASA Astrophysics Data System (ADS)

Kim, Soowon; Schatzki, Thomas F.

1999-01-01

Pinhole insect damage in natural almonds is very difficult to detect on-line. Further, evidence exists relating insect damage to aflatoxin contamination. Hence, for quality and health reasons, methods to detect and remove such damaged nuts are of great importance in this study, we explored the possibility of using x-ray imaging to detect pinhole damage in almonds by insects. X-ray film images of about 2000 almonds and x-ray linescan images of only 522 pinhole damaged almonds were obtained. The pinhole damaged region appeared slightly darker than non-damaged region in x-ray negative images. A machine recognition algorithm was developed to detect these darker regions. The algorithm used the first order and the second order information to identify the damaged region. To reduce the possibility of false positive results due to germ region in high resolution images, germ detection and removal routines were also included. With film images, the algorithm showed approximately an 81 percent correct recognition ratio with only 1 percent false positives whereas line scan images correctly recognized 65 percent of pinholes with about 9 percent false positives. The algorithms was very fast and efficient requiring only minimal computation time. If implemented on line, theoretical throughput of this recognition system would be 66 nuts/second.

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 %.

[Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network].

PubMed

Wang, Yuanyuan; Zhou, Tao; Lu, Huiling; Wu, Cuiying; Yang, Pengfei

2017-08-01

The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.

Identification, recognition and misidentification syndromes: a psychoanalytical perspective

PubMed Central

Thibierge, Stéphane; Morin, Catherine

2013-01-01

Misidentification syndromes are currently often understood as cognitive disorders of either the “sense of uniqueness” (Margariti and Kontaxakis, 2006) or the recognition of people (Ellis and Lewis, 2001). It is however, necessary to consider how a normal “sense of uniqueness” or normal person recognition are acquired by normal or neurotic subjects. It will be shown here that the normal conditions of cognition can be considered as one of the possible forms of a complex structure and not as just a setting for our sense and perception data. The consistency and the permanency of the body image in neurosis is what permits the recognition of other people and ourselves as unique beings. This consistency and permanency are related to object repression, as shown by neurological disorders of body image (somatoparaphrenia), which cause the object to come to the foreground in the patient’s words (Thibierge and Morin, 2010). In misidentification syndromes, as in other psychotic syndromes, one can also observe damage to the specular image as well as an absence of object repression. This leads us to question whether, in the psychiatric disorders related to a damaged specular image, disorders of cognition can be studied and managed using the same methods as for neurotic patients. PMID:24298262

Real-time color/shape-based traffic signs acquisition and recognition system

NASA Astrophysics Data System (ADS)

Saponara, Sergio

2013-02-01

A real-time system is proposed to acquire from an automotive fish-eye CMOS camera the traffic signs, and provide their automatic recognition on the vehicle network. Differently from the state-of-the-art, in this work color-detection is addressed exploiting the HSI color space which is robust to lighting changes. Hence the first stage of the processing system implements fish-eye correction and RGB to HSI transformation. After color-based detection a noise deletion step is implemented and then, for the classification, a template-based correlation method is adopted to identify potential traffic signs, of different shapes, from acquired images. Starting from a segmented-image a matching with templates of the searched signs is carried out using a distance transform. These templates are organized hierarchically to reduce the number of operations and hence easing real-time processing for several types of traffic signs. Finally, for the recognition of the specific traffic sign, a technique based on extraction of signs characteristics and thresholding is adopted. Implemented on DSP platform the system recognizes traffic signs in less than 150 ms at a distance of about 15 meters from 640x480-pixel acquired images. Tests carried out with hundreds of images show a detection and recognition rate of about 93%.

Video-based noncooperative iris image segmentation.

PubMed

Du, Yingzi; Arslanturk, Emrah; Zhou, Zhi; Belcher, Craig

2011-02-01

In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

Hierarchical ensemble of global and local classifiers for face recognition.

PubMed

Su, Yu; Shan, Shiguang; Chen, Xilin; Gao, Wen

2009-08-01

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.

A special purpose knowledge-based face localization method

NASA Astrophysics Data System (ADS)

Hassanat, Ahmad; Jassim, Sabah

2008-04-01

This paper is concerned with face localization for visual speech recognition (VSR) system. Face detection and localization have got a great deal of attention in the last few years, because it is an essential pre-processing step in many techniques that handle or deal with faces, (e.g. age, face, gender, race and visual speech recognition). We shall present an efficient method for localization human's faces in video images captured on mobile constrained devices, under a wide variation in lighting conditions. We use a multiphase method that may include all or some of the following steps starting with image pre-processing, followed by a special purpose edge detection, then an image refinement step. The output image will be passed through a discrete wavelet decomposition procedure, and the computed LL sub-band at a certain level will be transformed into a binary image that will be scanned by using a special template to select a number of possible candidate locations. Finally, we fuse the scores from the wavelet step with scores determined by color information for the candidate location and employ a form of fuzzy logic to distinguish face from non-face locations. We shall present results of large number of experiments to demonstrate that the proposed face localization method is efficient and achieve high level of accuracy that outperforms existing general-purpose face detection methods.

MToS: A Tree of Shapes for Multivariate Images.

PubMed

Carlinet, Edwin; Géraud, Thierry

2015-12-01

The topographic map of a gray-level image, also called tree of shapes, provides a high-level hierarchical representation of the image contents. This representation, invariant to contrast changes and to contrast inversion, has been proved very useful to achieve many image processing and pattern recognition tasks. Its definition relies on the total ordering of pixel values, so this representation does not exist for color images, or more generally, multivariate images. Common workarounds, such as marginal processing, or imposing a total order on data, are not satisfactory and yield many problems. This paper presents a method to build a tree-based representation of multivariate images, which features marginally the same properties of the gray-level tree of shapes. Briefly put, we do not impose an arbitrary ordering on values, but we only rely on the inclusion relationship between shapes in the image definition domain. The interest of having a contrast invariant and self-dual representation of multivariate image is illustrated through several applications (filtering, segmentation, and object recognition) on different types of data: color natural images, document images, satellite hyperspectral imaging, multimodal medical imaging, and videos.

The Ilac-Project Supporting Ancient Coin Classification by Means of Image Analysis

NASA Astrophysics Data System (ADS)

Kavelar, A.; Zambanini, S.; Kampel, M.; Vondrovec, K.; Siegl, K.

2013-07-01

This paper presents the ILAC project, which aims at the development of an automated image-based classification system for ancient Roman Republican coins. The benefits of such a system are manifold: operating at the suture between computer vision and numismatics, ILAC can reduce the day-to-day workload of numismatists by assisting them in classification tasks and providing a preselection of suitable coin classes. This is especially helpful for large coin hoard findings comprising several thousands of coins. Furthermore, this system could be implemented in an online platform for hobby numismatists, allowing them to access background information about their coin collection by simply uploading a photo of obverse and reverse for the coin of interest. ILAC explores different computer vision techniques and their combinations for the use of image-based coin recognition. Some of these methods, such as image matching, use the entire coin image in the classification process, while symbol or legend recognition exploit certain characteristics of the coin imagery. An overview of the methods explored so far and the respective experiments is given as well as an outlook on the next steps of the project.

Permutation coding technique for image recognition systems.

PubMed

Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

2006-11-01

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Split-brain reveals separate but equal self-recognition in the two cerebral hemispheres.

PubMed

Uddin, Lucina Q; Rayman, Jan; Zaidel, Eran

2005-09-01

To assess the ability of the disconnected cerebral hemispheres to recognize images of the self, a split-brain patient (an individual who underwent complete cerebral commissurotomy to relieve intractable epilepsy) was tested using morphed self-face images presented to one visual hemifield (projecting to one hemisphere) at a time while making "self/other" judgments. The performance of the right and left hemispheres of this patient as assessed by a signal detection method was not significantly different, though a measure of bias did reveal hemispheric differences. The right and left hemispheres of this patient independently and equally possessed the ability to self-recognize, but only the right hemisphere could successfully recognize familiar others. This supports a modular concept of self-recognition and other-recognition, separately present in each cerebral hemisphere.

Application of abstract harmonic analysis to the high-speed recognition of images

NASA Technical Reports Server (NTRS)

Usikov, D. A.

1979-01-01

Methods are constructed for rapidly computing correlation functions using the theory of abstract harmonic analysis. The theory developed includes as a particular case the familiar Fourier transform method for a correlation function which makes it possible to find images which are independent of their translation in the plane. Two examples of the application of the general theory described are the search for images, independent of their rotation and scale, and the search for images which are independent of their translations and rotations in the plane.

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

PubMed

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

[Multi-Target Recognition of Internal and External Defects of Potato by Semi-Transmission Hyperspectral Imaging and Manifold Learning Algorithm].

PubMed

Huang, Tao; Li, Xiao-yu; Jin, Rui; Ku, Jing; Xu, Sen-miao; Xu, Meng-ling; Wu, Zhen-zhong; Kong, De-guo

2015-04-01

The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.

Efficient iris recognition by characterizing key local variations.

PubMed

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

2004-06-01

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

Scene Text Recognition using Similarity and a Lexicon with Sparse Belief Propagation

PubMed Central

Weinman, Jerod J.; Learned-Miller, Erik; Hanson, Allen R.

2010-01-01

Scene text recognition (STR) is the recognition of text anywhere in the environment, such as signs and store fronts. Relative to document recognition, it is challenging because of font variability, minimal language context, and uncontrolled conditions. Much information available to solve this problem is frequently ignored or used sequentially. Similarity between character images is often overlooked as useful information. Because of language priors, a recognizer may assign different labels to identical characters. Directly comparing characters to each other, rather than only a model, helps ensure that similar instances receive the same label. Lexicons improve recognition accuracy but are used post hoc. We introduce a probabilistic model for STR that integrates similarity, language properties, and lexical decision. Inference is accelerated with sparse belief propagation, a bottom-up method for shortening messages by reducing the dependency between weakly supported hypotheses. By fusing information sources in one model, we eliminate unrecoverable errors that result from sequential processing, improving accuracy. In experimental results recognizing text from images of signs in outdoor scenes, incorporating similarity reduces character recognition error by 19%, the lexicon reduces word recognition error by 35%, and sparse belief propagation reduces the lexicon words considered by 99.9% with a 12X speedup and no loss in accuracy. PMID:19696446

Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

PubMed

Terunuma, Toshiyuki; Tokui, Aoi; Sakae, Takeji

2018-03-01

Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tracking should be performed by controlling "importance recognition": the understanding that soft-tissue is an important tracking feature and bone structure is unimportant. We propose a new real-time tumor-contouring method that uses deep learning with importance recognition control. The novelty of the proposed method is the combination of the devised random overlay method and supervised deep learning to induce the recognition of structures in tumor contouring as important or unimportant. This method can be used for tumor contouring because it uses deep learning to perform image segmentation. Our results from a simulated fluoroscopy model showed accurate tracking of a low-visibility tumor with an error of approximately 1 mm, even if enhanced bone structure acted as an obstacle. A high similarity of approximately 0.95 on the Jaccard index was observed between the segmented and ground truth tumor regions. A short processing time of 25 ms was achieved. The results of this simulated fluoroscopy model support the feasibility of robust real-time tumor contouring with fluoroscopy. Further studies using clinical fluoroscopy are highly anticipated.

Recognition of Similar Shaped Handwritten Marathi Characters Using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Jane, Archana P.; Pund, Mukesh A.

2012-03-01

The growing need have handwritten Marathi character recognition in Indian offices such as passport, railways etc has made it vital area of a research. Similar shape characters are more prone to misclassification. In this paper a novel method is provided to recognize handwritten Marathi characters based on their features extraction and adaptive smoothing technique. Feature selections methods avoid unnecessary patterns in an image whereas adaptive smoothing technique form smooth shape of charecters.Combination of both these approaches leads to the better results. Previous study shows that, no one technique achieves 100% accuracy in handwritten character recognition area. This approach of combining both adaptive smoothing & feature extraction gives better results (approximately 75-100) and expected outcomes.

Development of a piecewise linear omnidirectional 3D image registration method

NASA Astrophysics Data System (ADS)

Bae, Hyunsoo; Kang, Wonjin; Lee, SukGyu; Kim, Youngwoo

2016-12-01

This paper proposes a new piecewise linear omnidirectional image registration method. The proposed method segments an image captured by multiple cameras into 2D segments defined by feature points of the image and then stitches each segment geometrically by considering the inclination of the segment in the 3D space. Depending on the intended use of image registration, the proposed method can be used to improve image registration accuracy or reduce the computation time in image registration because the trade-off between the computation time and image registration accuracy can be controlled for. In general, nonlinear image registration methods have been used in 3D omnidirectional image registration processes to reduce image distortion by camera lenses. The proposed method depends on a linear transformation process for omnidirectional image registration, and therefore it can enhance the effectiveness of the geometry recognition process, increase image registration accuracy by increasing the number of cameras or feature points of each image, increase the image registration speed by reducing the number of cameras or feature points of each image, and provide simultaneous information on shapes and colors of captured objects.

Improved automatic adjustment of density and contrast in FCR system using neural network

NASA Astrophysics Data System (ADS)

Takeo, Hideya; Nakajima, Nobuyoshi; Ishida, Masamitsu; Kato, Hisatoyo

1994-05-01

FCR system has an automatic adjustment of image density and contrast by analyzing the histogram of image data in the radiation field. Advanced image recognition methods proposed in this paper can improve the automatic adjustment performance, in which neural network technology is used. There are two methods. Both methods are basically used 3-layer neural network with back propagation. The image data are directly input to the input-layer in one method and the histogram data is input in the other method. The former is effective to the imaging menu such as shoulder joint in which the position of interest region occupied on the histogram changes by difference of positioning and the latter is effective to the imaging menu such as chest-pediatrics in which the histogram shape changes by difference of positioning. We experimentally confirm the validity of these methods (about the automatic adjustment performance) as compared with the conventional histogram analysis methods.

Vector coding of wavelet-transformed images

NASA Astrophysics Data System (ADS)

Zhou, Jun; Zhi, Cheng; Zhou, Yuanhua

1998-09-01

Wavelet, as a brand new tool in signal processing, has got broad recognition. Using wavelet transform, we can get octave divided frequency band with specific orientation which combines well with the properties of Human Visual System. In this paper, we discuss the classified vector quantization method for multiresolution represented image.

A Mathematical Framework for Image Analysis

DTIC Science & Technology

1991-08-01

The results reported here were derived from the research project ’A Mathematical Framework for Image Analysis ’ supported by the Office of Naval...Research, contract N00014-88-K-0289 to Brown University. A common theme for the work reported is the use of probabilistic methods for problems in image ... analysis and image reconstruction. Five areas of research are described: rigid body recognition using a decision tree/combinatorial approach; nonrigid

A face and palmprint recognition approach based on discriminant DCT feature extraction.

PubMed

Jing, Xiao-Yuan; Zhang, David

2004-12-01

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

Continuous Human Action Recognition Using Depth-MHI-HOG and a Spotter Model

PubMed Central

Eum, Hyukmin; Yoon, Changyong; Lee, Heejin; Park, Mignon

2015-01-01

In this paper, we propose a new method for spotting and recognizing continuous human actions using a vision sensor. The method is comprised of depth-MHI-HOG (DMH), action modeling, action spotting, and recognition. First, to effectively separate the foreground from background, we propose a method called DMH. It includes a standard structure for segmenting images and extracting features by using depth information, MHI, and HOG. Second, action modeling is performed to model various actions using extracted features. The modeling of actions is performed by creating sequences of actions through k-means clustering; these sequences constitute HMM input. Third, a method of action spotting is proposed to filter meaningless actions from continuous actions and to identify precise start and end points of actions. By employing the spotter model, the proposed method improves action recognition performance. Finally, the proposed method recognizes actions based on start and end points. We evaluate recognition performance by employing the proposed method to obtain and compare probabilities by applying input sequences in action models and the spotter model. Through various experiments, we demonstrate that the proposed method is efficient for recognizing continuous human actions in real environments. PMID:25742172

Vehicle license plate recognition in dense fog based on improved atmospheric scattering model

NASA Astrophysics Data System (ADS)

Tang, Chunming; Lin, Jun; Chen, Chunkai; Dong, Yancheng

2018-04-01

An effective method based on improved atmospheric scattering model is proposed in this paper to handle the problem of the vehicle license plate location and recognition in dense fog. Dense fog detection is performed firstly by the top-hat transformation and the vertical edge detection, and the moving vehicle image is separated from the traffic video image. After the vehicle image is decomposed into two layers: structure and texture layers, the glow layer is separated from the structure layer to get the background layer. Followed by performing the mean-pooling and the bicubic interpolation algorithm, the atmospheric light map of the background layer can be predicted, meanwhile the transmission of the background layer is estimated through the grayed glow layer, whose gray value is altered by linear mapping. Then, according to the improved atmospheric scattering model, the final restored image can be obtained by fusing the restored background layer and the optimized texture layer. License plate location is performed secondly by a series of morphological operations, connected domain analysis and various validations. Characters extraction is achieved according to the projection. Finally, an offline trained pattern classifier of hybrid discriminative restricted boltzmann machines (HDRBM) is applied to recognize the characters. Experimental results on thorough data sets are reported to demonstrate that the proposed method can achieve high recognition accuracy and works robustly in the dense fog traffic environment during 24h or one day.

A model based method for recognizing psoas major muscles in torso CT images

NASA Astrophysics Data System (ADS)

Kamiya, Naoki; Zhou, Xiangrong; Chen, Huayue; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Hoshi, Hiroaki; Fujita, Hiroshi

2010-03-01

In aging societies, it is important to analyze age-related hypokinesia. A psoas major muscle has many important functional capabilities such as capacity of balance and posture control. These functions can be measured by its cross sectional area (CSA), volume, and thickness. However, these values are calculated manually in the clinical situation. The purpose of our study is to propose an automated recognition method of psoas major muscles in X-ray torso CT images. The proposed recognition process involves three steps: 1) determination of anatomical points such as the origin and insertion of the psoas major muscle, 2) generation of a shape model for the psoas major muscle, and 3) recognition of the psoas major muscles by use of the shape model. The model was built using quadratic function, and was fit to the anatomical center line of psoas major muscle. The shape model was generated using 20 CT cases and tested by 20 other CT cases. The applied database consisted of 12 male and 8 female cases from the ages of 40's to 80's. The average value of Jaccard similarity coefficient (JSC) values employed in the evaluation was 0.7. Our experimental results indicated that the proposed method was effective for a volumetric analysis and could be possible to be used for a quantitative measurement of psoas major muscles in CT images.

Molecularly imprinted polymer based on chemiluminescence imaging for the chiral recognition of dansyl-phenylalanine.

PubMed

Wang, Li; Zhang, Zhujun; Huang, Lianggao

2008-03-01

A new molecularly imprinted polymer (MIP)-chemiluminescence (CL) imaging detection approach towards chiral recognition of dansyl-phenylalanine (Phe) is presented. The polymer microspheres were synthesized using precipitation polymerization with dansyl-L-Phe as template. Polymer microspheres were immobilized in microtiter plates (96 wells) using poly(vinyl alcohol) (PVA) as glue. The analyte was selectively adsorbed on the MIP microspheres. After washing, the bound fraction was quantified based on peroxyoxalate chemiluminescence (PO-CL) analysis. In the presence of dansyl-Phe, bis(2,4,6-trichlorophenyl)oxalate (TCPO) reacted with hydrogen peroxide (H2O2) to emit chemiluminescence. The signal was detected and quantified with a highly sensitive cooled charge-coupled device (CCD). Influencing factors were investigated and optimized in detail. Control experiments using capillary electrophoresis showed that there was no significant difference between the proposed method and the control method at a confidence level of 95%. The method can perform 96 independent measurements simultaneously in 30 min and the limits of detection (LODs) for dansyl-L-Phe and dansyl-D-Phe were 0.025 micromol L(-1) and 0.075 micromol L(-1) (3sigma), respectively. The relative standard deviation (RSD) for 11 parallel measurements of dansyl-L-Phe (0.78 micromol L(-1)) was 8%. The results show that MIP-based CL imaging can become a useful analytical technology for quick chiral recognition.

Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning

NASA Astrophysics Data System (ADS)

Zhao, Lei; Wang, Zengcai; Wang, Xiaojin; Qi, Yazhou; Liu, Qing; Zhang, Guoxin

2016-09-01

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

Local gradient Gabor pattern (LGGP) with applications in face recognition, cross-spectral matching, and soft biometrics

NASA Astrophysics Data System (ADS)

Chen, Cunjian; Ross, Arun

2013-05-01

Researchers in face recognition have been using Gabor filters for image representation due to their robustness to complex variations in expression and illumination. Numerous methods have been proposed to model the output of filter responses by employing either local or global descriptors. In this work, we propose a novel but simple approach for encoding Gradient information on Gabor-transformed images to represent the face, which can be used for identity, gender and ethnicity assessment. Extensive experiments on the standard face benchmark FERET (Visible versus Visible), as well as the heterogeneous face dataset HFB (Near-infrared versus Visible), suggest that the matching performance due to the proposed descriptor is comparable against state-of-the-art descriptor-based approaches in face recognition applications. Furthermore, the same feature set is used in the framework of a Collaborative Representation Classification (CRC) scheme for deducing soft biometric traits such as gender and ethnicity from face images in the AR, Morph and CAS-PEAL databases.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

PubMed

Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

PubMed Central

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases. PMID:26571112

Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

NASA Astrophysics Data System (ADS)

Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

Video based object representation and classification using multiple covariance matrices.

PubMed

Zhang, Yurong; Liu, Quan

2017-01-01

Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.

Personal authentication through dorsal hand vein patterns

NASA Astrophysics Data System (ADS)

Hsu, Chih-Bin; Hao, Shu-Sheng; Lee, Jen-Chun

2011-08-01

Biometric identification is an emerging technology that can solve security problems in our networked society. A reliable and robust personal verification approach using dorsal hand vein patterns is proposed in this paper. The characteristic of the approach needs less computational and memory requirements and has a higher recognition accuracy. In our work, the near-infrared charge-coupled device (CCD) camera is adopted as an input device for capturing dorsal hand vein images, it has the advantages of the low-cost and noncontact imaging. In the proposed approach, two finger-peaks are automatically selected as the datum points to define the region of interest (ROI) in the dorsal hand vein images. The modified two-directional two-dimensional principal component analysis, which performs an alternate two-dimensional PCA (2DPCA) in the column direction of images in the 2DPCA subspace, is proposed to exploit the correlation of vein features inside the ROI between images. The major advantage of the proposed method is that it requires fewer coefficients for efficient dorsal hand vein image representation and recognition. The experimental results on our large dorsal hand vein database show that the presented schema achieves promising performance (false reject rate: 0.97% and false acceptance rate: 0.05%) and is feasible for dorsal hand vein recognition.

A new FOD recognition algorithm based on multi-source information fusion and experiment analysis

NASA Astrophysics Data System (ADS)

Li, Yu; Xiao, Gang

2011-08-01

Foreign Object Debris (FOD) is a kind of substance, debris or article alien to an aircraft or system, which would potentially cause huge damage when it appears on the airport runway. Due to the airport's complex circumstance, quick and precise detection of FOD target on the runway is one of the important protections for airplane's safety. A multi-sensor system including millimeter-wave radar and Infrared image sensors is introduced and a developed new FOD detection and recognition algorithm based on inherent feature of FOD is proposed in this paper. Firstly, the FOD's location and coordinate can be accurately obtained by millimeter-wave radar, and then according to the coordinate IR camera will take target images and background images. Secondly, in IR image the runway's edges which are straight lines can be extracted by using Hough transformation method. The potential target region, that is, runway region, can be segmented from the whole image. Thirdly, background subtraction is utilized to localize the FOD target in runway region. Finally, in the detailed small images of FOD target, a new characteristic is discussed and used in target classification. The experiment results show that this algorithm can effectively reduce the computational complexity, satisfy the real-time requirement and possess of high detection and recognition probability.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Mobile-based text recognition from water quality devices

NASA Astrophysics Data System (ADS)

Dhakal, Shanti; Rahnemoonfar, Maryam

2015-03-01

Measuring water quality of bays, estuaries, and gulfs is a complicated and time-consuming process. YSI Sonde is an instrument used to measure water quality parameters such as pH, temperature, salinity, and dissolved oxygen. This instrument is taken to water bodies in a boat trip and researchers note down different parameters displayed by the instrument's display monitor. In this project, a mobile application is developed for Android platform that allows a user to take a picture of the YSI Sonde monitor, extract text from the image and store it in a file on the phone. The image captured by the application is first processed to remove perspective distortion. Probabilistic Hough line transform is used to identify lines in the image and the corner of the image is then obtained by determining the intersection of the detected horizontal and vertical lines. The image is warped using the perspective transformation matrix, obtained from the corner points of the source image and the destination image, hence, removing the perspective distortion. Mathematical morphology operation, black-hat is used to correct the shading of the image. The image is binarized using Otsu's binarization technique and is then passed to the Optical Character Recognition (OCR) software for character recognition. The extracted information is stored in a file on the phone and can be retrieved later for analysis. The algorithm was tested on 60 different images of YSI Sonde with different perspective features and shading. Experimental results, in comparison to ground-truth results, demonstrate the effectiveness of the proposed method.

A Survey on Sentiment Classification in Face Recognition

NASA Astrophysics Data System (ADS)

Qian, Jingyu

2018-01-01

Face recognition has been an important topic for both industry and academia for a long time. K-means clustering, autoencoder, and convolutional neural network, each representing a design idea for face recognition method, are three popular algorithms to deal with face recognition problems. It is worthwhile to summarize and compare these three different algorithms. This paper will focus on one specific face recognition problem-sentiment classification from images. Three different algorithms for sentiment classification problems will be summarized, including k-means clustering, autoencoder, and convolutional neural network. An experiment with the application of these algorithms on a specific dataset of human faces will be conducted to illustrate how these algorithms are applied and their accuracy. Finally, the three algorithms are compared based on the accuracy result.

Hypothesis Support Mechanism for Mid-Level Visual Pattern Recognition

NASA Technical Reports Server (NTRS)

Amador, Jose J (Inventor)

2007-01-01

A method of mid-level pattern recognition provides for a pose invariant Hough Transform by parametrizing pairs of points in a pattern with respect to at least two reference points, thereby providing a parameter table that is scale- or rotation-invariant. A corresponding inverse transform may be applied to test hypothesized matches in an image and a distance transform utilized to quantify the level of match.

Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Ind., June 29-July 1, 1976, Proceedings

NASA Technical Reports Server (NTRS)

1976-01-01

Papers are presented on the applicability of Landsat data to water management and control needs, IBIS, a geographic information system based on digital image processing and image raster datatype, and the Image Data Access Method (IDAM) for the Earth Resources Interactive Processing System. Attention is also given to the Prototype Classification and Mensuration System (PROCAMS) applied to agricultural data, the use of Landsat for water quality monitoring in North Carolina, and the analysis of geophysical remote sensing data using multivariate pattern recognition. The Illinois crop-acreage estimation experiment, the Pacific Northwest Resources Inventory Demonstration, and the effects of spatial misregistration on multispectral recognition are also considered. Individual items are announced in this issue.

Dactyl Alphabet Gesture Recognition in a Video Sequence Using Microsoft Kinect

NASA Astrophysics Data System (ADS)

Artyukhin, S. G.; Mestetskiy, L. M.

2015-05-01

This paper presents an efficient framework for solving the problem of static gesture recognition based on data obtained from the web cameras and depth sensor Kinect (RGB-D - data). Each gesture given by a pair of images: color image and depth map. The database store gestures by it features description, genereated by frame for each gesture of the alphabet. Recognition algorithm takes as input a video sequence (a sequence of frames) for marking, put in correspondence with each frame sequence gesture from the database, or decide that there is no suitable gesture in the database. First, classification of the frame of the video sequence is done separately without interframe information. Then, a sequence of successful marked frames in equal gesture is grouped into a single static gesture. We propose a method combined segmentation of frame by depth map and RGB-image. The primary segmentation is based on the depth map. It gives information about the position and allows to get hands rough border. Then, based on the color image border is specified and performed analysis of the shape of the hand. Method of continuous skeleton is used to generate features. We propose a method of skeleton terminal branches, which gives the opportunity to determine the position of the fingers and wrist. Classification features for gesture is description of the position of the fingers relative to the wrist. The experiments were carried out with the developed algorithm on the example of the American Sign Language. American Sign Language gesture has several components, including the shape of the hand, its orientation in space and the type of movement. The accuracy of the proposed method is evaluated on the base of collected gestures consisting of 2700 frames.

Learning target masks in infrared linescan imagery

NASA Astrophysics Data System (ADS)

Fechner, Thomas; Rockinger, Oliver; Vogler, Axel; Knappe, Peter

1997-04-01

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

3D automatic anatomy segmentation based on iterative graph-cut-ASM.

PubMed

Chen, Xinjian; Bagci, Ulas

2011-08-01

This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

Whole-face procedures for recovering facial images from memory.

PubMed

Frowd, Charlie D; Skelton, Faye; Hepton, Gemma; Holden, Laura; Minahil, Simra; Pitchford, Melanie; McIntyre, Alex; Brown, Charity; Hancock, Peter J B

2013-06-01

Research has indicated that traditional methods for accessing facial memories usually yield unidentifiable images. Recent research, however, has made important improvements in this area to the witness interview, method used for constructing the face and recognition of finished composites. Here, we investigated whether three of these improvements would produce even-more recognisable images when used in conjunction with each other. The techniques are holistic in nature: they involve processes which operate on an entire face. Forty participants first inspected an unfamiliar target face. Nominally 24h later, they were interviewed using a standard type of cognitive interview (CI) to recall the appearance of the target, or an enhanced 'holistic' interview where the CI was followed by procedures for focussing on the target's character. Participants then constructed a composite using EvoFIT, a recognition-type system that requires repeatedly selecting items from face arrays, with 'breeding', to 'evolve' a composite. They either saw faces in these arrays with blurred external features, or an enhanced method where these faces were presented with masked external features. Then, further participants attempted to name the composites, first by looking at the face front-on, the normal method, and then for a second time by looking at the face side-on, which research demonstrates facilitates recognition. All techniques improved correct naming on their own, but together promoted highly-recognisable composites with mean naming at 74% correct. The implication is that these techniques, if used together by practitioners, should substantially increase the detection of suspects using this forensic method of person identification. Copyright © 2013 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

PubMed

Mezgec, Simon; Koroušić Seljak, Barbara

2017-06-27

Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an accuracy of 94 . 47 % on a detection dataset containing 130 , 517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson's disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 % , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson's disease patients.

Recognition without Awareness: An Elusive Phenomenon

ERIC Educational Resources Information Center

Jeneson, Annette; Kirwan, C. Brock; Squire, Larry R.

2010-01-01

Two recent studies described conditions under which recognition memory performance appeared to be driven by nondeclarative memory. Specifically, participants successfully discriminated old images from highly similar new images even when no conscious memory for the images could be retrieved. Paradoxically, recognition performance was better when…

Robust wafer identification recognition based on asterisk-shape filter and high-low score comparison method.

PubMed

Hsu, Wei-Chih; Yu, Tsan-Ying; Chen, Kuan-Liang

2009-12-10

Wafer identifications (wafer ID) can be used to identify wafers from each other so that wafer processing can be traced easily. Wafer ID recognition is one of the problems of optical character recognition. The process to recognize wafer IDs is similar to that used in recognizing car license-plate characters. However, due to some unique characteristics, such as the irregular space between two characters and the unsuccessive strokes of wafer ID, it will not get a good result to recognize wafer ID by directly utilizing the approaches used in car license-plate character recognition. Wafer ID scratches are engraved by a laser scribe almost along the following four fixed directions: horizontal, vertical, plus 45 degrees , and minus 45 degrees orientations. The closer to the center line of a wafer ID scratch, the higher the gray level will be. These and other characteristics increase the difficulty to recognize the wafer ID. In this paper a wafer ID recognition scheme based on an asterisk-shape filter and a high-low score comparison method is proposed to cope with the serious influence of uneven luminance and make recognition more efficiently. Our proposed approach consists of some processing stages. Especially in the final recognition stage, a template-matching method combined with stroke analysis is used as a recognizing scheme. This is because wafer IDs are composed of Semiconductor Equipment and Materials International (SEMI) standard Arabic numbers and English alphabets, and thus the template ID images are easy to obtain. Furthermore, compared with the approach that requires prior training, such as a support vector machine, which often needs a large amount of training image samples, no prior training is required for our approach. The testing results show that our proposed scheme can efficiently and correctly segment out and recognize the wafer ID with high performance.

Hand-gesture-based sterile interface for the operating room using contextual cues for the navigation of radiological images

PubMed Central

Jacob, Mithun George; Wachs, Juan Pablo; Packer, Rebecca A

2013-01-01

This paper presents a method to improve the navigation and manipulation of radiological images through a sterile hand gesture recognition interface based on attentional contextual cues. Computer vision algorithms were developed to extract intention and attention cues from the surgeon's behavior and combine them with sensory data from a commodity depth camera. The developed interface was tested in a usability experiment to assess the effectiveness of the new interface. An image navigation and manipulation task was performed, and the gesture recognition accuracy, false positives and task completion times were computed to evaluate system performance. Experimental results show that gesture interaction and surgeon behavior analysis can be used to accurately navigate, manipulate and access MRI images, and therefore this modality could replace the use of keyboard and mice-based interfaces. PMID:23250787

Hand-gesture-based sterile interface for the operating room using contextual cues for the navigation of radiological images.

PubMed

Jacob, Mithun George; Wachs, Juan Pablo; Packer, Rebecca A

2013-06-01

This paper presents a method to improve the navigation and manipulation of radiological images through a sterile hand gesture recognition interface based on attentional contextual cues. Computer vision algorithms were developed to extract intention and attention cues from the surgeon's behavior and combine them with sensory data from a commodity depth camera. The developed interface was tested in a usability experiment to assess the effectiveness of the new interface. An image navigation and manipulation task was performed, and the gesture recognition accuracy, false positives and task completion times were computed to evaluate system performance. Experimental results show that gesture interaction and surgeon behavior analysis can be used to accurately navigate, manipulate and access MRI images, and therefore this modality could replace the use of keyboard and mice-based interfaces.

Performance evaluation methodology for historical document image binarization.

PubMed

Ntirogiannis, Konstantinos; Gatos, Basilis; Pratikakis, Ioannis

2013-02-01

Document image binarization is of great importance in the document image analysis and recognition pipeline since it affects further stages of the recognition process. The evaluation of a binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images. In the proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a weighting scheme that diminishes any potential evaluation bias. Additional performance metrics of the proposed evaluation scheme consist of the percentage rates of broken and missed text, false alarms, background noise, character enlargement, and merging. Several experiments conducted in comparison with other pixel-based evaluation measures demonstrate the validity of the proposed evaluation scheme.

Monitoring of facial stress during space flight: Optical computer recognition combining discriminative and generative methods

NASA Astrophysics Data System (ADS)

Dinges, David F.; Venkataraman, Sundara; McGlinchey, Eleanor L.; Metaxas, Dimitris N.

2007-02-01

Astronauts are required to perform mission-critical tasks at a high level of functional capability throughout spaceflight. Stressors can compromise their ability to do so, making early objective detection of neurobehavioral problems in spaceflight a priority. Computer optical approaches offer a completely unobtrusive way to detect distress during critical operations in space flight. A methodology was developed and a study completed to determine whether optical computer recognition algorithms could be used to discriminate facial expressions during stress induced by performance demands. Stress recognition from a facial image sequence is a subject that has not received much attention although it is an important problem for many applications beyond space flight (security, human-computer interaction, etc.). This paper proposes a comprehensive method to detect stress from facial image sequences by using a model-based tracker. The image sequences were captured as subjects underwent a battery of psychological tests under high- and low-stress conditions. A cue integration-based tracking system accurately captured the rigid and non-rigid parameters of different parts of the face (eyebrows, lips). The labeled sequences were used to train the recognition system, which consisted of generative (hidden Markov model) and discriminative (support vector machine) parts that yield results superior to using either approach individually. The current optical algorithm methods performed at a 68% accuracy rate in an experimental study of 60 healthy adults undergoing periods of high-stress versus low-stress performance demands. Accuracy and practical feasibility of the technique is being improved further with automatic multi-resolution selection for the discretization of the mask, and automated face detection and mask initialization algorithms.

Image-based automatic recognition of larvae

NASA Astrophysics Data System (ADS)

Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

2010-08-01

As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

Automatic anatomy recognition on CT images with pathology

NASA Astrophysics Data System (ADS)

Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

2016-03-01

Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

Context-Aware Local Binary Feature Learning for Face Recognition.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2018-05-01

In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.

Palm vein recognition based on directional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Volumetric segmentation of range images for printed circuit board inspection

NASA Astrophysics Data System (ADS)

Van Dop, Erik R.; Regtien, Paul P. L.

1996-10-01

Conventional computer vision approaches towards object recognition and pose estimation employ 2D grey-value or color imaging. As a consequence these images contain information about projections of a 3D scene only. The subsequent image processing will then be difficult, because the object coordinates are represented with just image coordinates. Only complicated low-level vision modules like depth from stereo or depth from shading can recover some of the surface geometry of the scene. Recent advances in fast range imaging have however paved the way towards 3D computer vision, since range data of the scene can now be obtained with sufficient accuracy and speed for object recognition and pose estimation purposes. This article proposes the coded-light range-imaging method together with superquadric segmentation to approach this task. Superquadric segments are volumetric primitives that describe global object properties with 5 parameters, which provide the main features for object recognition. Besides, the principle axes of a superquadric segment determine the phase of an object in the scene. The volumetric segmentation of a range image can be used to detect missing, false or badly placed components on assembled printed circuit boards. Furthermore, this approach will be useful to recognize and extract valuable or toxic electronic components on printed circuit boards scrap that currently burden the environment during electronic waste processing. Results on synthetic range images with errors constructed according to a verified noise model illustrate the capabilities of this approach.

Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using the visible/near-infrared hyperspectral imaging technique.

PubMed

Cao, Yang; Zhang, Chaojie; Chen, Quansheng; Li, Yanyu; Qi, Shuai; Tian, Lin; Ren, YongLin

2015-08-01

Identifying stored-product insects is essential for granary management. Automated, computer-based classification methods are rapidly developing in many areas. A hyperspectral imaging technique could potentially be developed to identify stored-product insect species and geographical strains. This study tested and adapted the technique using four geographical strains of each of two insect species, the rice weevil and maize weevil, to collect and analyse the resultant hyperspectral data. Three characteristic images that corresponded to the dominant wavelengths, 505, 659 and 955 nm, were selected by multivariate image analysis. Each image was processed, and 22 morphological and textural features from regions of interest were extracted as the inputs for an identification model. We found the backpropagation neural network model to be the superior method for distinguishing between the insect species and geographical strains. The overall recognition rates of the classification model for insect species were 100 and 98.13% for the calibration and prediction sets respectively, while the rates of the model for geographical strains were 94.17 and 86.88% respectively. This study has demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a potential instrument for identifying insects and could become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects. © 2014 Society of Chemical Industry.

Quantifying the effect of colorization enhancement on mammogram images

NASA Astrophysics Data System (ADS)

Wojnicki, Paul J.; Uyeda, Elizabeth; Micheli-Tzanakou, Evangelia

2002-04-01

Current methods of radiological displays provide only grayscale images of mammograms. The limitation of the image space to grayscale provides only luminance differences and textures as cues for object recognition within the image. However, color can be an important and significant cue in the detection of shapes and objects. Increasing detection ability allows the radiologist to interpret the images in more detail, improving object recognition and diagnostic accuracy. Color detection experiments using our stimulus system, have demonstrated that an observer can only detect an average of 140 levels of grayscale. An optimally colorized image can allow a user to distinguish 250 - 1000 different levels, hence increasing potential image feature detection by 2-7 times. By implementing a colorization map, which follows the luminance map of the original grayscale images, the luminance profile is preserved and color is isolated as the enhancement mechanism. The effect of this enhancement mechanism on the shape, frequency composition and statistical characteristics of the Visual Evoked Potential (VEP) are analyzed and presented. Thus, the effectiveness of the image colorization is measured quantitatively using the Visual Evoked Potential (VEP).

Method of particle trajectory recognition in particle flows of high particle concentration using a candidate trajectory tree process with variable search areas

DOEpatents

Shaffer, Franklin D.

2013-03-12

The application relates to particle trajectory recognition from a Centroid Population comprised of Centroids having an (x, y, t) or (x, y, f) coordinate. The method is applicable to visualization and measurement of particle flow fields of high particle. In one embodiment, the centroids are generated from particle images recorded on camera frames. The application encompasses digital computer systems and distribution mediums implementing the method disclosed and is particularly applicable to recognizing trajectories of particles in particle flows of high particle concentration. The method accomplishes trajectory recognition by forming Candidate Trajectory Trees and repeated searches at varying Search Velocities, such that initial search areas are set to a minimum size in order to recognize only the slowest, least accelerating particles which produce higher local concentrations. When a trajectory is recognized, the centroids in that trajectory are removed from consideration in future searches.

Comparison Analysis of Recognition Algorithms of Forest-Cover Objects on Hyperspectral Air-Borne and Space-Borne Images

NASA Astrophysics Data System (ADS)

Kozoderov, V. V.; Kondranin, T. V.; Dmitriev, E. V.

2017-12-01

The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of hyperspectral air-borne and space-borne imagery processing. The model is based on improvements of the Bayesian classifier that is a computational procedure of statistical decision making in machine-learning methods of pattern recognition. The principal component method is implemented to decompose the hyperspectral measurements on the basis of empirical orthogonal functions. Application examples are shown of various modifications of the Bayesian classifier and Support Vector Machine method. Examples are provided of comparing these classifiers and a metrical classifier that operates on finding the minimal Euclidean distance between different points and sets in the multidimensional feature space. A comparison is also carried out with the " K-weighted neighbors" method that is close to the nonparametric Bayesian classifier.

Leukocyte Recognition Using EM-Algorithm

NASA Astrophysics Data System (ADS)

Colunga, Mario Chirinos; Siordia, Oscar Sánchez; Maybank, Stephen J.

This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm. A new cell image is classified using the maximum a posteriori decision rule.

Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.

PubMed

Udupa, Jayaram K; Odhner, Dewey; Zhao, Liming; Tong, Yubing; Matsumoto, Monica M S; Ciesielski, Krzysztof C; Falcao, Alexandre X; Vaideeswaran, Pavithra; Ciesielski, Victoria; Saboury, Babak; Mohammadianrasanani, Syedmehrdad; Sin, Sanghun; Arens, Raanan; Torigian, Drew A

2014-07-01

To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide Automatic Anatomy Recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions - thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) - involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and testing data sets are divided into equal size in all cases except for the neck. Overall the AAR method achieves a mean accuracy of about 2 voxels in localizing non-sparse blob-like objects and most sparse tubular objects. The delineation accuracy in terms of mean false positive and negative volume fractions is 2% and 8%, respectively, for non-sparse objects, and 5% and 15%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 0.9 and 1.5 voxels, respectively. Some sparse objects - venous system (in the thorax on CT), inferior vena cava (in the abdomen on CT), and mandible and naso-pharynx (in neck on MRI, but not on CT) - pose challenges at all levels, leading to poor recognition and/or delineation results. The AAR method fares quite favorably when compared with methods from the recent literature for liver, kidneys, and spleen on CT images. We conclude that separation of modality-independent from dependent aspects, organization of objects in a hierarchy, encoding of object relationship information explicitly into the hierarchy, optimal threshold-based recognition learning, and fuzzy model-based IRFC are effective concepts which allowed us to demonstrate the feasibility of a general AAR system that works in different body regions on a variety of organs and on different modalities. Copyright © 2014 Elsevier B.V. All rights reserved.

Cascaded K-means convolutional feature learner and its application to face recognition

NASA Astrophysics Data System (ADS)

Zhou, Daoxiang; Yang, Dan; Zhang, Xiaohong; Huang, Sheng; Feng, Shu

2017-09-01

Currently, considerable efforts have been devoted to devise image representation. However, handcrafted methods need strong domain knowledge and show low generalization ability, and conventional feature learning methods require enormous training data and rich parameters tuning experience. A lightened feature learner is presented to solve these problems with application to face recognition, which shares similar topology architecture as a convolutional neural network. Our model is divided into three components: cascaded convolution filters bank learning layer, nonlinear processing layer, and feature pooling layer. Specifically, in the filters learning layer, we use K-means to learn convolution filters. Features are extracted via convoluting images with the learned filters. Afterward, in the nonlinear processing layer, hyperbolic tangent is employed to capture the nonlinear feature. In the feature pooling layer, to remove the redundancy information and incorporate the spatial layout, we exploit multilevel spatial pyramid second-order pooling technique to pool the features in subregions and concatenate them together as the final representation. Extensive experiments on four representative datasets demonstrate the effectiveness and robustness of our model to various variations, yielding competitive recognition results on extended Yale B and FERET. In addition, our method achieves the best identification performance on AR and labeled faces in the wild datasets among the comparative methods.

Grayscale inhomogeneity correction method for multiple mosaicked electron microscope images

NASA Astrophysics Data System (ADS)

Zhou, Fangxu; Chen, Xi; Sun, Rong; Han, Hua

2018-04-01

Electron microscope image stitching is highly desired to acquire microscopic resolution images of large target scenes in neuroscience. However, the result of multiple Mosaicked electron microscope images may exist severe gray scale inhomogeneity due to the instability of the electron microscope system and registration errors, which degrade the visual effect of the mosaicked EM images and aggravate the difficulty of follow-up treatment, such as automatic object recognition. Consequently, the grayscale correction method for multiple mosaicked electron microscope images is indispensable in these areas. Different from most previous grayscale correction methods, this paper designs a grayscale correction process for multiple EM images which tackles the difficulty of the multiple images monochrome correction and achieves the consistency of grayscale in the overlap regions. We adjust overall grayscale of the mosaicked images with the location and grayscale information of manual selected seed images, and then fuse local overlap regions between adjacent images using Poisson image editing. Experimental result demonstrates the effectiveness of our proposed method.

Recognition of finger flexion motion from ultrasound image: a feasibility study.

PubMed

Shi, Jun; Guo, Jing-Yi; Hu, Shu-Xian; Zheng, Yong-Ping

2012-10-01

Muscle contraction results in structural and morphologic changes of the related muscle. Therefore, finger flexion can be monitored from measurements of these morphologic changes. We used ultrasound imaging to record muscle activities during finger flexion and extracted features to discriminate different fingers' flexions using a support vector machine (SVM). Registration of ultrasound images before and after finger flexion was performed to generate a deformation field, from which angle features and wavelet-based features were extracted. The SVM was then used to classify the motions of different fingers. The experimental results showed that the overall mean recognition accuracy was 94.05% ± 4.10%, with the highest for the thumb (97%) and the lowest for the ring finger (92%) and the mean F value was 0.94 ± 0.02, indicating high accuracy and reliability of this method. The results suggest that the proposed method has the potential to be used as an alternative method of surface electromyography in differentiating the motions of different fingers. Copyright © 2012 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Investigation into diagnostic agreement using automated computer-assisted histopathology pattern recognition image analysis.

PubMed

Webster, Joshua D; Michalowski, Aleksandra M; Dwyer, Jennifer E; Corps, Kara N; Wei, Bih-Rong; Juopperi, Tarja; Hoover, Shelley B; Simpson, R Mark

2012-01-01

The extent to which histopathology pattern recognition image analysis (PRIA) agrees with microscopic assessment has not been established. Thus, a commercial PRIA platform was evaluated in two applications using whole-slide images. Substantial agreement, lacking significant constant or proportional errors, between PRIA and manual morphometric image segmentation was obtained for pulmonary metastatic cancer areas (Passing/Bablok regression). Bland-Altman analysis indicated heteroscedastic measurements and tendency toward increasing variance with increasing tumor burden, but no significant trend in mean bias. The average between-methods percent tumor content difference was -0.64. Analysis of between-methods measurement differences relative to the percent tumor magnitude revealed that method disagreement had an impact primarily in the smallest measurements (tumor burden <3%). Regression-based 95% limits of agreement indicated substantial agreement for method interchangeability. Repeated measures revealed concordance correlation of >0.988, indicating high reproducibility for both methods, yet PRIA reproducibility was superior (C.V.: PRIA = 7.4, manual = 17.1). Evaluation of PRIA on morphologically complex teratomas led to diagnostic agreement with pathologist assessments of pluripotency on subsets of teratomas. Accommodation of the diversity of teratoma histologic features frequently resulted in detrimental trade-offs, increasing PRIA error elsewhere in images. PRIA error was nonrandom and influenced by variations in histomorphology. File-size limitations encountered while training algorithms and consequences of spectral image processing dominance contributed to diagnostic inaccuracies experienced for some teratomas. PRIA appeared better suited for tissues with limited phenotypic diversity. Technical improvements may enhance diagnostic agreement, and consistent pathologist input will benefit further development and application of PRIA.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Improved iris localization by using wide and narrow field of view cameras for iris recognition

NASA Astrophysics Data System (ADS)

Kim, Yeong Gon; Shin, Kwang Yong; Park, Kang Ryoung

2013-10-01

Biometrics is a method of identifying individuals by their physiological or behavioral characteristics. Among other biometric identifiers, iris recognition has been widely used for various applications that require a high level of security. When a conventional iris recognition camera is used, the size and position of the iris region in a captured image vary according to the X, Y positions of a user's eye and the Z distance between a user and the camera. Therefore, the searching area of the iris detection algorithm is increased, which can inevitably decrease both the detection speed and accuracy. To solve these problems, we propose a new method of iris localization that uses wide field of view (WFOV) and narrow field of view (NFOV) cameras. Our study is new as compared to previous studies in the following four ways. First, the device used in our research acquires three images, one each of the face and both irises, using one WFOV and two NFOV cameras simultaneously. The relation between the WFOV and NFOV cameras is determined by simple geometric transformation without complex calibration. Second, the Z distance (between a user's eye and the iris camera) is estimated based on the iris size in the WFOV image and anthropometric data of the size of the human iris. Third, the accuracy of the geometric transformation between the WFOV and NFOV cameras is enhanced by using multiple matrices of the transformation according to the Z distance. Fourth, the searching region for iris localization in the NFOV image is significantly reduced based on the detected iris region in the WFOV image and the matrix of geometric transformation corresponding to the estimated Z distance. Experimental results showed that the performance of the proposed iris localization method is better than that of conventional methods in terms of accuracy and processing time.

Slant rectification in Russian passport OCR system using fast Hough transform

NASA Astrophysics Data System (ADS)

Limonova, Elena; Bezmaternykh, Pavel; Nikolaev, Dmitry; Arlazarov, Vladimir

2017-03-01

In this paper, we introduce slant detection method based on Fast Hough Transform calculation and demonstrate its application in industrial system for Russian passports recognition. About 1.5% of this kind of documents appear to be slant or italic. This fact reduces recognition rate, because Optical Recognition Systems are normally designed to process normal fonts. Our method uses Fast Hough Transform to analyse vertical strokes of characters extracted with the help of x-derivative of a text line image. To improve the quality of detector we also introduce field grouping rules. The resulting algorithm allowed to reach high detection quality. Almost all errors of considered approach happen on passports of nonstandard fonts, while slant detector works in appropriate way.

Identification and location of catenary insulator in complex background based on machine vision

NASA Astrophysics Data System (ADS)

Yao, Xiaotong; Pan, Yingli; Liu, Li; Cheng, Xiao

2018-04-01

It is an important premise to locate insulator precisely for fault detection. Current location algorithms for insulator under catenary checking images are not accurate, a target recognition and localization method based on binocular vision combined with SURF features is proposed. First of all, because of the location of the insulator in complex environment, using SURF features to achieve the coarse positioning of target recognition; then Using binocular vision principle to calculate the 3D coordinates of the object which has been coarsely located, realization of target object recognition and fine location; Finally, Finally, the key is to preserve the 3D coordinate of the object's center of mass, transfer to the inspection robot to control the detection position of the robot. Experimental results demonstrate that the proposed method has better recognition efficiency and accuracy, can successfully identify the target and has a define application value.

Visual recognition system of cherry picking robot based on Lab color model

NASA Astrophysics Data System (ADS)

Zhang, Qirong; Zuo, Jianjun; Yu, Tingzhong; Wang, Yan

2017-12-01

This paper designs a visual recognition system suitable for cherry picking. First, the system deals with the image using the vector median filter. And then it extracts a channel of Lab color model to divide the cherries and the background. The cherry contour was successfully fitted by the least square method, and the centroid and radius of the cherry were extracted. Finally, the cherry was successfully extracted.

Increasing the information acquisition volume in iris recognition systems.

PubMed

Barwick, D Shane

2008-09-10

A significant hurdle for the widespread adoption of iris recognition in security applications is that the typically small imaging volume for eye placement results in systems that are not user friendly. Separable cubic phase plates at the lens pupil have been shown to ameliorate this disadvantage by increasing the depth of field. However, these phase masks have limitations on how efficiently they can capture the information-bearing spatial frequencies in iris images. The performance gains in information acquisition that can be achieved by more general, nonseparable phase masks is demonstrated. A detailed design method is presented, and simulations using representative designs allow for performance comparisons.

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks.

PubMed

Yu, Lequan; Chen, Hao; Dou, Qi; Qin, Jing; Heng, Pheng-Ann

2017-04-01

Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams, respectively. This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.

3D automatic anatomy segmentation based on iterative graph-cut-ASM

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

Chen, Xinjian; Bagci, Ulas; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room 1C515, Bethesda, Maryland 20892-1182

2011-08-15

Purpose: This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. Methods: The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability ofmore » the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al.[Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. Results: The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 deg. and 0.03, and over all foot bones are about 3.5709 mm, 0.35 deg. and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. Conclusions: The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.« less

3D automatic anatomy segmentation based on iterative graph-cut-ASM

PubMed Central

Chen, Xinjian; Bagci, Ulas

2011-01-01

Purpose: This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images.Methods: The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1–72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine.Results: The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10° and 0.03, and over all foot bones are about 3.5709 mm, 0.35° and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s.Conclusions: The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min. PMID:21928634

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment

PubMed Central

Koroušić Seljak, Barbara

2017-01-01

Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86.72%, along with an accuracy of 94.47% on a detection dataset containing 130,517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson’s disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55%, which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson’s disease patients. PMID:28653995

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles.

PubMed

Suhr, Jae Kyu; Jung, Ho Gi

2018-04-16

An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM) system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision.

A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles

PubMed Central

2018-01-01

An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM) system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision. PMID:29659512

Physiology-based face recognition in the thermal infrared spectrum.

PubMed

Buddharaju, Pradeep; Pavlidis, Ioannis T; Tsiamyrtzis, Panagiotis; Bazakos, Mike

2007-04-01

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.

Deep learning and face recognition: the state of the art

NASA Astrophysics Data System (ADS)

Balaban, Stephen

2015-05-01

Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition.1-3 Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset.3-6 In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. Despite the ability of DNNs and autoencoders to perform unsupervised feature learning, modern facial recognition pipelines still require domain specific engineering in the form of re-alignment. For example, in Facebook's recent DeepFace paper, a 3D "frontalization" step lies at the beginning of the pipeline. This step creates a 3D face model for the incoming image and then uses a series of affine transformations of the fiducial points to "frontalize" the image. This step enables the DeepFace system to use a neural network architecture with locally connected layers without weight sharing as opposed to standard convolutional layers.6 Deep learning techniques combined with large datasets have allowed research groups to surpass human level performance on the LFW dataset.3, 5 The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques.3, 5 There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike.