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IEEE Conference on Computer Vision and pattern recognition

作者： Wei Li Lei Zhang David Zhang Guangming Lu Jingqi Yan Biometrics Research Center Dept. of Computing The Hong Kong Polytechnic University Biocomputing Research Center Shenzhen Graduate School Harbin Institute of Technology Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781424469840;9781424469857

Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past decade, and recently three dimensional (3D) palmprint recognition techniques were also proposed. The 2D and 3D palmprint data can be captured simultaneously and they provide different and complementary information. 3D palmprint contains the depth information of the palm surface, while 2D palmprint contains plenty of textures. How to efficiently extract and fuse the 2D and 3D palmprint features to improve the recognition performance is a critical issue for practical palmprint systems. In this paper, an efficient joint 2D and 3D palmprint matching scheme is proposed. The principal line features and palm shape features are extracted and used to accurately align the palmprint, and a couple of matching rules are defined to efficiently use the 2D and 3D features for recognition. The experiments on a 2D+3D palmprint database which contains 8000 samples show that the proposed scheme can greatly improve the performance of palmprint verification.

关键词： Dermatoglyphics Three Dimensions

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Ethnicity classification based on gait using multi-view fusion

Ethnicity classification based on gait using multi-view fusi...

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

作者： De Zhang Yunhong Wang Bir Bhanu Center for Research in Intelligent Systems University of California Riverside CA USA Intelligent Recognition and Image Processing Laboratory Beihang University Beijing China

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x; y; view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs the best.

关键词： Cameras Tensile stress Biometrics Surveillance image analysis image recognition Rendering (computer graphics) Principal component analysis Feature extraction Voting

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Prediction of protein folding rates from structural topology and complex network properties

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IPSJ Transactions on Bioinformatics 2010年 3卷 40-53页

作者： Song, Jiangning Takemoto, Kazuhiro Shen, Hongbin Tan, Hao Gromiha, M. Michael Akutsu, Tatsuya Bioinformatics Center Institute for Chemical Research Kyoto University Japan Department of Biochemistry and Molecular Biology Monash University Australia PRESTO Japan Science and Technology Agency Japan Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China Computational Biology Research Center National Institute of Advanced Industrial Science and Technology Japan

As a fundamental biological problem, revealing the protein folding mechanism remains to be one of the most challenging problems in structural bioinformatics. Prediction of protein folding rate is an important step towards our further understanding of the protein folding mechanism and the complex sequencestructure-function relationship. In this article, we develop a novel approach to predict protein folding rates for two-state and multi-state protein folding kinetics, which combines a variety of structural topology and complex network properties that are calculated from protein three-dimensional structures. To take into account the specific correlations between network properties and protein folding rates, we define two different protein residue contact networks, based on two different scales Protein Contact Network (PCN) and Long-range Interaction Network (LIN) to characterize the corresponding network features. The leave-one-out cross-validation (LOOCV) tests indicate that this integrative strategy is more powerful in predicting the folding rates from 3D structures, with the Pearson's Correlation Coefficient (CC) of 0.88, 0.90 and 0.90 for two-state, multi-state and combined protein folding kinetics, which provides an improved performance compared with other prediction work. This study provides useful insights which shed light on the network organization of interacting residues underlying protein folding process for both two-state and multi-state folding kinetics. Moreover, our method also provides a complementary approach to the current folding rate prediction algorithms and can be used as a powerful tool for the characterization of the foldomics protein data. The implemented webserver (termed PRORATE) is freely accessible at http://***/~sjn/folding/. © 2010 Information processing Society of Japan.

关键词： Forecasting

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Registration-based auto-detection of the optimal cross sections in 3D echocardiographic images

Registration-based auto-detection of the optimal cross secti...

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International Conference on Bioinformatics and Biomedical Technology (ICBBT)

作者： Xiaoping Liu Xin Yang Lanping Wu Xiao Tan Computer Center East China Jiao Tong University Shanghai China Institute of Pattern recognition and Image Processing Shanghai Jiaotong University Shanghai China Children's Medical Center Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424467754;9781424467761

In most cases, echocardiographers examine three-dimensional (3D) or four-dimensional (4D) echocardiographic images by studying several important cross sections which are defined as the optimal cross sections. It is a time-consuming and tedious task for echocardiographers to manually search the optimal cross sections by existing real-time 3D echocardiography (RT3DE) systems or off-line 3D softwares. To assist doctors' diagnosis, we design a registration-based method to automatically detect the optimal cross sections in three-dimensional echocardiographic (3DE) images. Firstly, a normal end-diastolic (ED) volumetric data is chosen, in which its optimal cross section is predetermined. Secondly, instead of the whole ED volume, only part of it is selected as the template. Finally, all the optimal cross sections of a 3DE image are detected by aligning the 3DE image with the template image. As a preliminary study, we performed this method on 28 normal apical datasets and the error rate is 15%. The results show good prospects for implementing automatic detection, which might offer better repeatability and save time. Furthermore, it can be a prior procedure for measurement and the computer-aided diagnosis.

关键词： Ultrasonic imaging image registration Heart Biomedical imaging Echocardiography Medical diagnostic imaging image reconstruction Shape pattern recognition image processing

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Age Classification Base on Gait Using HMM

Age Classification Base on Gait Using HMM

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International Conference on pattern recognition

作者： De Zhang Yunhong Wang Bir Bhanu Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China Center of Research in Intelligent Systems University of California Riverside CA USA

In this paper we propose a new framework for age classification based on human gait using Hidden Markov Model (HMM). A gait database including young people and elderly people is built. To extract appropriate gait features, we consider a contour related method in terms of shape variations during human walking. Then the image feature is transformed to a lower-dimensional space by using the Frame to Exemplar (FED) distance. A HMM is trained on the FED vector sequences. Thus, the framework provides flexibility in the selection of gait feature representation. In addition, the framework is robust for classification due to the statistical nature of HMM. The experimental results show that video-based automatic age classification from human gait is feasible and reliable.

关键词： Hidden Markov models Senior citizens Databases Legged locomotion Feature extraction Humans Pixel

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People detection in complex scene using a cascade of Boosted classifiers based on Haar-like-features

People detection in complex scene using a cascade of Boosted...

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IEEE Intelligent Vehicles Symposium

作者： Siala, A. Khlifa, N. Bremond, F. Hamrouni, K. Research Unit in Signal Processing Image Processing and Pattern Recognition ENIT Tunisia PULSAR Team INRIA Sophia Antipolis Nice France

ISBN: (纸本)9781424435036

Pedestrian detection in a real scene is an interesting application for video surveillance systems. This paper presents our contribution to improve the work of Viola and Jones, originally designed to detect faces. This work uses a cascade of classifiers based on Adaboost using Haar features. It improves the learning step by including a decision tree presenting the different poses and possible occlusions. The method has been tested on real and complex sequences and has given a good detection despite occlusions and poses variation.

关键词： Adaptive boosting

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People detection in complex scene using a cascade of boosted classifiers based on Haar-like-features

People detection in complex scene using a cascade of boosted...

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IEEE Symposium on Intelligent Vehicle

作者： M. Siala N. Khlifa F. Bremond K. Hamrouni Research Unit in Signal Processing Image Processing and Pattern Recognition(ENIT Tunisia) PULSAR Team (INRIA Sophia Antipolis Nice) Research Unit in Signal Processing Image Processing and Pattern Recognition(ENIT Tunisia) Tunisia

ISBN: (纸本)9781424435036

关键词： Layout Face detection Object detection Support vector machines Motion detection Support vector machine classification Humans Testing Robustness Detectors

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A framework for nuclear image enhancement based on the Anscomb transform and the bayesian thresholding

A framework for nuclear image enhancement based on the Ansco...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Imen Mbazaa Najla Gribaa Nawres Khlifa Kamel Hamrouni Research Unit of Signal Processing Image Processing and Pattern Recognition National Engineering School of Sfax (ENIS) Tunisia

Scintigraphic imagery is an important data source since it provides morphological and functional clinical informations. However, scintigraphic images present very bad quality because of several degradation factors. In fact, the recorded data are embedded in noise modelled as the realisation of Poisson process. The aim of this paper is to provide a framework able to enhance nuclear images quality by Poisson intensity estimation. This framework consists, in a first step, of performing variance-stabilizing step for the Poisson process thanks to the Anscombe transformation. So the obtained data can be considered as contaminated by a white Gaussian noise. In a second step, it uses a Bayesian technique inspired of Pizurica approach, known in the literature for exhibiting good results as for white Gaussian noise. In fact, the complex wavelet packets were exploited regarding to Pizurica algorithm.

关键词： image enhancement Bayesian methods Gaussian noise Noise reduction Wavelet packets Wavelet transforms image processing image quality Pixel Cameras

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recognition Driven Page Orientation Detection

Recognition Driven Page Orientation Detection

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IEEE International Conference on image processing

作者： Yves Rangoni Faisal Shafait Joost van Beusekom Thomas M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group German Research Center for Artificial Intelligence Kaiserslautern Germany Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Kaiserslautern Germany

In document image recognition, orientation detection of the scanned page is necessary for the following procedures to work correctly as they assume that the text is well oriented. Several methods have been proposed, but most of them rely on heuristics of the script such as the graphical asymmetry between ascenders and descenders for Roman script. The literature shows that as soon as this assumption is not fulfilled, e.g. plain capital text, noisy or degraded characters, etc. they fail. For a large-scale digitalization process, a low error and rejection rate are expected in order to reduce the amount of human intervention. We propose a recognition Driven Page Orientation Detection (RD-POD) which does not depend on external criteria or assumption on the shape of the script. It uses the OCR engine for estimating the right orientation with a few lines of the document image. The RD-POD is highly robust and accurate, and is able to detect multiple orientations. Experimental evaluation shows that our method outperforms the current state-of-the-art on UW-1 dataset with an accuracy of 99.7%. Further tests on other three large and public datasets (MARG, ICDAR07, Google 1000 books) show accuracies of above 99% on each of them.

关键词： image recognition Noise shaping Degradation Large-scale systems Humans Shape Optical character recognition software Search engines Robustness Testing

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Server-Side Prediction of Source IP Addresses Using Density Estimation

Server-Side Prediction of Source IP Addresses Using Density ...

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International Conference on Availability, Reliability and Security, ARES

作者： Markus Goldstein Matthias Reif Armin Stahl Thomas Breuel Research Group Image Understanding and Pattern Recognition (IUPR) German Research Center for Artificial Intelligence Kaiserslautern Germany Research Group Image Understanding and Pattern Recognition (IUPR) Technical University of Kaiserslautern Kaiserslautern Germany

Source IP addresses are often used as a major feature for user modeling in computer networks. Particularly in the field of distributed denial of service (DDoS) attack detection and mitigation traffic models make extensive use of source IP addresses for detecting anomalies. Typically the real IP address distribution is strongly undersampled due to a small amount of observations. Density estimation overcomes this shortage by taking advantage of IP neighborhood relations. In many cases simple models are implicitly used or chosen intuitively as a network based heuristic. In this paper we review and formalize existing models including a hierarchical clustering approach first. In addition, we present a modified k-means clustering algorithm for source IP density estimation as well as a statistical motivated smoothing approach using the Nadaraya-Watson kernel-weighted average. For performance evaluation we apply all methods on a 90 days real world dataset consisting of 1.3 million different source IP addresses and try to predict the users of the following next 10 days. ROC curves and an example DDoS mitigation scenario show that there is no uniformly better approach: k-means performs best when a high detection rate is needed whereas statistical smoothing works better for low false alarm rate requirements like the DDoS mitigation scenario.

关键词： Computer crime Traffic control Telecommunication traffic pattern recognition Predictive models Smoothing methods Machine learning Quality of service Routing Density functional theory

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