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A novel method of fingerprint minutiae extraction based on Gabor phase

A novel method of fingerprint minutiae extraction based on G...

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IEEE International Conference on Image Processing

作者： Xin Gao Xiaoguang Chen Jia Cao Zirui Deng Chongjin Liu Jufu Feng Key Laboratory of Machine Perception MOE Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing China Institute of Image Processing and Pattern Recognition North-China University of Technology Beijing China

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.

关键词： Pixel Feature extraction Image quality Fingerprint recognition Detectors Frequency estimation Software algorithms

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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

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IEEE International Conference on Mechatronics and Automation

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan China College of Mechanical Engineering TaiYuan University of Technology TaiYuan China Institute for Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology WuHan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： Image edge detection Image restoration Degradation Atmospheric modeling Optical scattering Optical imaging

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Study on the Method of Image Restoration in the Environment of Dust

Study on the Method of Image Restoration in the Environment ...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education Taiyuan University Of Technology Taiyuan China College of Mechanical Engineering Taiyuan University Of Technology Taiyuan China Institute of Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology Wuhan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, GA optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with automatic threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： Image edge detection Image restoration Scattering Degradation Gallium Atmospheric modeling Genetic algorithms

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A swarm intelligence based searching strategy for articulated 3D human body tracking

A swarm intelligence based searching strategy for articulate...

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IEEE computer Society Conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Xiaoqin Zhang Weiming Hu Xiangyang Wang Yu Kong Nianhua Xie Hanzi Wang Haibin Ling Steve Maybank College of Mathematics & Information science Wenzhou University Zhejiang China Institute of Automation National Laboratory of Pattern Recognition Beijing China School of Communication and Information Engineering Shanghai University Shanghai China School of Computer Science Beijing Institute of Technology Beijing China School of Computer Science University of Adelaide Australia Department of Computer and Information Science Temple University Philadelphia USA School of Computer Science and Information Systems Birkbeck College London UK

This paper proposes an annealed particle swarm optimization based particle filter algorithm for articulated 3D human body tracking. In our algorithm, a sampling covariance and an annealing factor are incorporated into the velocity updating equation of particle swarm optimization (PSO). The sampling covariance and the annealing factor are initiated with appropriate values at the beginning of the PSO iteration, and `annealing' is carried out at reasonable steps. Experiments with multi-camera walking sequences from the Brown dataset show that: 1) the proposed tracker can effectively alleviate the problem of inconsistency between the image likelihood and the true model; 2) the tracker is also robust to noise and body self-occlusion.

关键词： Particle swarm optimization Humans Annealing Particle filters Particle tracking Sampling methods Equations Image sampling Legged locomotion Noise robustness

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Early diagnosis of dementia based on intersubject whole-brain dissimilarities

Early diagnosis of dementia based on intersubject whole-brai...

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IEEE International Symposium on Biomedical Imaging

作者： S. Klein M. Loog F. van der Lijn T. den Heijer A. Hammers M. de Bruijne A. van der Lugt R.P.W. Duin M.M.B. Breteler W.J. Niessen Biomedical Imaging Group Rotterdam Departments of Radiology & Medical Informatics Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Pattern Recognition Laboratory Faculty of Electrical Engineering Mathematics Computer Science Delft University of Technnology Netherlands Department of Neurology Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Neurodis Foundation CERMEP PET Centre Lyon France MRC Clinical Sciences Centre and Division of Neuroscience and Mental Health Imperial College Hammersmith Hospital London UK Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Radiology Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Department of Epidemiology & Biostatistics Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Imaging Science & Technology Department of Applied Sciences Delft University of Technnology Netherlands

ISBN: (纸本)9781424441259

This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58×58 dissimilarity matrix. A kNN classifier was trained on the dissimilarity matrix and the performance was tested in a leave-one-out experiment. A classification accuracy of 81% was attained (spec. 83%, sens. 79%). This demonstrates the potential of whole-brain intersubject dissimilarities to aid in early diagnosis of dementia.

关键词： Dementia Brain pattern recognition Shape Radiology computer science Biomedical imaging Senior citizens Diseases Biomarkers

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Tree segmentation from scanned scene data

Tree segmentation from scanned scene data

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3rd International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, PMA09

作者： Ning, Xiaojuan Zhang, Xiaopeng Wang, Yinghui Department of Computer Science and Engineering Xi'An University of Technology Xi'an China Ino-French Laboratory LIAMA CAS Institute of Automation Beijing China National Laboratory of Pattern Recognition CAS Institute of Automation Beijing China

ISBN: (纸本)9780769539881

Tree segmentation is an important step in tree reconstruction from scanned data. A new method is presented for automatic extraction of single objects from a complex scene. The proposed method can be used as a solution for tree segmentation from 3D point cloud data with few restrictions, where many complex objects are included in the scene, like trees, building, cars, and so on. The scene data is initially segmented into several small regions according to the distances between points. A weighted combination is constructed on distances and normal angles in each small region for further segmentation. The minimization of the function will be used to determine whether these regions will be merged or not. This method is tested on several data sets. Effective segmental results demonstrated that this approach could be applied to nondestructive measurements in forestry. © 2010 IEEE.

关键词： Forestry

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Towards the universal semantic assessment of accessibility 09

Towards the universal semantic assessment of accessibility

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24th Annual ACM Symposium on Applied Computing, SAC 2009

作者： Lopes, Rui Votis, Konstantinos Carriço, Luís Tzovaras, Dimitrios Likothanassis, Spiridon LaSIGE University of Lisbon Campo Grande Edíficio C6 1749-016 Lisboa Portugal CERTH/ITI Thermi-Thessaloniki GR-57001 Greece Pattern Recognition Laboratory Computer Engineering and Informatics University of Patras Greece

ISBN: (纸本)9781605581668

The ever increasing adoption of software technologies has bring closer technology to users with disabilities and users that interact with devices other than a PC. This diversification poses a real challenge to developers when creating software that has to cope with a myriad of interaction situations, as well as specific directives for ensuring an accessible interaction. In this paper we present SAAF, the Semantic Accessibility Assessment Framework. SAAF provides a set of constructs to describe the semantics of accessibility assessment procedures that can cope with different users, devices, and software technologies in a coherent and formalised way. SAAF affords the description of users and devices, their constraints and requirements, in the light of different accessibility assessment procedures. We exemplify the usage of SAAF by applying it in the context of the Web and related accessibility best practices such as WCAG. Copyright 2009 ACM.

关键词： Semantics

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Facial expression classification using Eigen-components of principal expressions

Facial expression classification using Eigen-components of p...

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International Conference on Advanced Communication Technology (ICACT)

作者： Wilfred Onyango Odoyo Geum-Boon Lee Jung-Jin Park Beom-Joon Cho Artificial Intelligence and Pattern Recognition Laboratory Department of Computer Engineering Chosun University Gwangju South Korea

This paper proposes a simple but powerful method for automatic classification of facial expressions from static images. Still images do not bare as much information as in video sequences which have much information/activities during the expression actions. The main aim here is to be able to classify every facial image into the six universally researched and accepted prototypic facial expressions like happy, fear, disgust, anger, surprise and sadness. We propose a powerful matching algorithm to determine the correct grouping or class of the newly received image into the already learned data in the database. Principal Component Analysis (PCA), a powerful tool in the feature extraction and dimensionality reduction is utilized to generate eigen-components of the images. The six representatives (Principal) Eigen-components of the individual facial expressions classes are stored and used at the time of matching. We use Mahalanobis distance algorithm to determine the similarity between the newly unknown incoming data and the already trained and known data sets in our database. This similarity is calculated between one-to-many (six) classes. The individual matching rate is recorded and compared for the evaluation and proper classification. Falsely accepted data (FAR) and the ones rejected falsely (FRR) are studied for algorithms improvement. Homomorphic filtering, one of the image enhancing methods in the frequency domain is used for pre-processing the image. With proper initial image processing and noise removal techniques used, a high proper classification is envisioned. The algorithm has worked perfectly with our local data and part of JAFFE data base. More tests are yet to be carried out on other public databases.

关键词： Image databases Principal component analysis Video sequences Prototypes Spatial databases Feature extraction Power generation Filtering Frequency domain analysis Image processing

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Multi-level iris video image thresholding

Multi-level iris video image thresholding

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IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, CIB

作者： Yingzi Du N. Luke Thomas Emrah Arslanturk Biometrics and pattern recognition laboratory in the Electrical and Computer Engineering Department Indiana University-Purdue University Indianapolis IN USA

Iris recognition has been shown to be one of the most accurate biometrics. However, under non-ideal situations, its recognition accuracy can be reduced dramatically. Under such situations, video images can be used to improve the accuracy. The traditional single image based segmentation method could be inefficient. Video image based thresholding method can help improve the segmentation efficiency. However, traditional thresholding methods are not designed for iris images. In this paper, the multi-level iris video image thresholding method is proposed. It takes advantage of the correlations between consecutive images for video based thresholding. It is an orientation invariant thresholding scheme. K-mean clustering is used to find the clusters and PCA is used to quickly project the image to the clusters. The experimental results show the proposed method is effective. The thresholded images show clear pupil and iris areas, which can help further iris segmentation and processing. In addition, the proposed method can be applied to non-video images if they are obtained from the same sensor with similar illumination conditions.

关键词： Image segmentation Iris recognition Histograms Biometrics Lighting Entropy Focusing Design methodology Principal component analysis Image sensors

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Facial component features for facial expression identification using Active Appearance Models

Facial component features for facial expression identificati...

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AFRICON Conference

作者： Odoyo O. Wilfred Geum-Boon Lee Jung-Jin Park Beom-Joon Cho Artificial Intelligence and Pattern Recognition Laboratory Department of Computer Science and Engineering Chosun University Gwangju South Korea

Statistical methods of shape and appearance are powerful tools used in computer vision for near-correct interpretation of images. In this paper, we present a method for classifying facial expressions based on the extracted features of facial components. The face, the window to the inner self of an individual can be analyzed for outright expressions like sadness, happiness, anger, surprise, disgust and fear. The facial region is detected; pre-processing is done on the image by using Active Appearance Model (AAM) to extract the vital feature on the facial components. Six classes are formed based on the different expressions and the model from the AAM procedure used to compare with the query image/face. Mahalanobis distance algorithm is used to classify the image in question into the best-fit class. Japanese Female Facial Expression (JAFFE) public database is used to evaluate our method with over 200 images of still images used in our experiment. A higher classification rate observed.

关键词： Active appearance model

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