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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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A method for improving the accuracy of predicting protein localization

A method for improving the accuracy of predicting protein lo...

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The 5th International Conference on Computer Science & Education(第五届国际计算机新技术与教育学术研讨会 ICCSE'10)

作者： Tong Wang Qinghua Huang Lixiu Yao Institute of Computer and Information Shanghai Second Polytechnic UniversityShanghai201209China School of Communication and Information Engineering Shanghai University Shanghai200444China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong UniversityShanghai200240C

In this paper, a system based on the novel Maximum Variance Projection (MVP) is proposed to improve the performance of protein subcellular localization prediction. Firstly, the protein sequences are quantized into a high dimension space using a new representation approach Position-Specific Score Matrix (PSSM). However, the problems caused by such representation are computation complexity and complicated classifier design. To sort out this problem, a new dimension reduction algorithm, MVP, is introduced. It extracts the essential features from the high dimension feature space. Then, K-Nearest Neighbor (K-NN) classifier is employed to recognize the subcellular localization of proteins according to the new features after dimension reduction. A good experimental result is obtained based on the jackknife dataset.

关键词： Subcellular localization:Dimensionality reduction:PSSM

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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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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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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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Pose estimation based on human detection and segmentation

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Science in China(Series F) 2009年第2期52卷 244-251页

作者： CHEN Qiang ZHENG EnLiang LIU YunCai Institute of image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

We address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We do not make such assumption, In this paper, a two-step approach is proposed, first, instead of applying background subtraction to get the segmentation of human, we combine the segmentation with human detection using an ISM-based detector. Then, silhouette feature can be extracted and 3D pose estimation is solved as a regression problem. RVMs and ridge regression method are applied to solve this problem. The results show the robustness and accuracy of our method.

关键词： human detection and segmentation 3D pose estimation regression machine learning

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Model-based Gait Representation via Spatial Point Reconstruction

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Journal of Shanghai Jiaotong university(Science) 2009年第3期14卷 293-298页

作者：张元元吴晓娟阮秋琦 Institute of Image Processing and Pattern Recognition Shandong University Institute of Information Science Beijing Jiaotong University

This paper proposed a novel model-based feature representation method to characterize human walking properties for individual recognition by gait. First, a new spatial point reconstruction approach is proposed to recover the coordinates of 3D points from 2D images by the related coordinate conversion factor (CCF). The images are captured by a monocular camera. Second, the human body is represented by a connected three-stick model. Then the parameters of the body model are recovered by the method of projective geometry using the related CCF. Finally, the gait feature composed of those parameters is defined, and it is proved by experiments that those features can partially avoid the influence of viewing angles between the optical axis of the camera and walking direction of the subject.

关键词： coordinate conversion factor （CCF） gait feature monocular camera parallel restriction

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Target tracking in infrared imagery using a novel particle filter

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Chinese Optics Letters 2009年第7期7卷 576-579页

作者：王芳林刘尔琦杨杰郁生阳周越 Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Institute of the Second Academy China Aerospace Science and Industry Corporation

To address two challenging problems in infrared target tracking, target appearance changes and unpre- dictable abrupt motions, a novel particle filtering based tracking algorithm is introduced. In this method, a novel saliency model is proposed to distinguish the salient target from background, and the eigenspace model is invoked to adapt target appearance changes. To account for the abrupt motions efficiently, a two- step sampling method is proposed to combine the two observation models. The proposed tracking method is demonstrated through two real infrared image sequences, which include the changes of luminance and size, and the drastic abrupt motions of the target.

关键词： Infrared imaging Nonlinear filtering Targets

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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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Adaptive beam hardening correction based on projection data consistency condition

Adaptive beam hardening correction based on projection data ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Shaojie Tang Xuanqin Mou Qiong Xu Yanbo Zhang Hengyong Yu Institute of Image Processing & Pattern Recognition Xi''an Jiaotong University Xi'an Shaanxi China Department of Radiology Emory University School of Medicine Atlanta GA USA Department of Radiology Division of Radiologic Sciences Wake Forest University Health Sciences Winston-Salem NC 27157 USA

In the medical diagnostic computed tomography (CT) systems, the x-ray tube usually emits photons with a polychromatic spectrum, resulting in beam hardening artifacts in the reconstructed images. Although the bone correction method is extensively used to compensate for the beam hardening artifacts, its performance crucially depends on the empirical choice of a scaling factor. To overcome this shortcoming, here we propose two adaptive correction methods, which utilize the Helgasson-Ludwig (H-L) consistency condition to determine the optimal scaling factor and the corresponding coefficient vector. Our numerical simulation results demonstrate the effectiveness of the proposed methods.

关键词： Bones X-ray imaging image reconstruction Computed tomography Biomedical imaging Detectors

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