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2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

作者： Jiang, Yunfei Guo, Ping Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)1424409918

In face recognition, the dimensionality of raw data is very high, dimension reduction (Feature Extraction) should be applied before classification. There exist several feature extraction methods, commonly used are Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. In this paper, we present a comparative study of some feature extraction methods for face recognition in the same conditions. The methods evaluated here include Eigenfaces, Kernel Principal Component Analysis (KPCA), Fisherfaces, Direct Linear Discriminant Analysis (D-LDA), Regularized Linear Discriminant Analysis (R-LDA), and Kernel Direct Discriminant Analysis (KDDA). For the purpose of comparison on feature extraction methods, we adopt Nearest Neighbor (NN) algorithm from existed classifiers of face recognition, since this classifier is common and simpleness. Empirical studies are conducted to evaluate these feature extraction methods with images from ORL Face Database, and it is found that in most cases LDA-based methods are efficient than PCA-based ones. © 2007 IEEE.

关键词： Feature extraction

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Unified restoration method for different degraded images

Unified restoration method for different degraded images

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2010 International Conference on Optoelectronics and image processing, ICOIP 2010

作者： Hong, Hanyu Li, Liangcheng Yan, Luxin Zhang, Tianxu Laboratory for Image Processing and Intelligent Control Wuhan Institute of Technology Wuhan 430074 China Institute for Pattern Recognition and AI Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769542522

Blurred images are caused by many factors such as defocus, motion, and atmospheric turbulence. Due to the unknown various factors that cannot be distinguished in the blurred image, it is necessary to propose a unified method for image restoration. In order to solve this problem, a unified restoration method for different degraded images is presented, in which we use edge information of blurred image to predict the blur mode. It operates by locally predicting a sharp image using edge information of blurred image and establishes a nonlinear and spatial anisotropic regularization function from the prediction sharp image and the blurred image. Then we can estimate the point spread function (PSF) and recover the blurred image quickly. A series of restoration experiments for different blured images have been performed to verify the effectiveness of the proposed method. © 2010 IEEE.

关键词： Restoration

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Texture Segmentation Based on image Model and Neural Network

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Journal of Earth Science 1999年第4期18卷 333-335页

作者： Chen Zhenyu Sheng Wen Wang Guangjun Li Dehua(State Education Commission Laboratory for image processing and Intelligence Control, institute for pattern recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wu han 430074) State Education Commission Laboratory for Image Processing and Intelligence Control Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and TechnologyWuhan 430074

This paper presents a texture segmentation approach which is based on the Markov random field model (MRF) and feed forward neural *** texture is modeled by the second order Gauss MRF model, and the least square error estimation is employed for the solution of model parameters. To perform texture segmentation, we introduced an improved BP algorithm to get faster learning speed. Experiment shows that better segmentation results can be obtained than the traditional Euclidean distance method.

关键词： texture analysis Markov random field neural network

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High performance word level sequential and parallel coding methods and architectures for bit plane coding

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Science in China(Series F) 2008年第4期51卷 337-351页

作者： XIONG ChengYi TIAN JinWen LIU Jian College of Electronic Information Engineering South-Central University for Nationalities Wuhan 430074 China Institute of Pattern Recognition & Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China

This paper introduced a novel high performance algorithm and VLSI architectures for achieving bit plane coding （BPC） in word level sequential and parallel mode. The proposed BPC algorithm adopts the techniques of coding pass prediction and parallel ＆ pipeline to reduce the number of accessing memory and to increase the ability of concurrently processing of the system, where all the coefficient bits of a code block could be coded by only one scan. A new parallel bit plane architecture （PA） was proposed to achieve word-level sequential coding. Moreover, an efficient high-speed architecture （HA） was presented to achieve multi-word parallel coding. Compared to the state of the art, the proposed PA could reduce the hardware cost more efficiently, though the throughput retains one coefficient coded per clock. While the proposed HA could perform coding for 4 coefficients belonging to a stripe column at one intra-clock cycle, so that coding for an NxN code-block could be completed in approximate N2/4 intra-clock cycles. Theoretical analysis and experimental results demonstrate that the proposed designs have high throughput rate with good performance in terms of speedup to cost, which can be good alternatives for low power applications.

关键词： bit plane coding high performance word-level sequential multi-word parallel

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Active Appearance Model Based Hand Gesture recognition

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Journal of Donghua University(English Edition) 2005年第4期000卷 67-71页

作者： TENG Xiao-long YU Wei-wei LIU Chong-qing Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030

This paper addresses the application of hand gesture recognition in monocular image sequences using Active Appearance Model (AAM). For this work, the proposed algorithm is conposed of constructing AAMs and fitting the models to the interest region. In training stage, according to the manual labeled feature points, the relative AAM is constructed and the corresponding average feature is obtained. In recognition stage, the interesting hand gesture region is firstly segmented by skin and movement ***, the models are fitted to the image that includes the hand gesture, and the relative features are ***, the classification is done by comparing the extracted features and average features. 30 different gestures of Chinese sign language are applied for testing the effectiveness of the method. The Experimental results are given indicating good performance of the algorithm.

关键词： human-machine interaction hand gesture recognition AAM sign language

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Construction of combinatorial pyramids

Construction of combinatorial pyramids

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4th IAPR International Workshop on Graph Based Representations in pattern recognition, GbRPR 2003

作者： Brun, Luc Kropatsch, Walter G. Université de Reims France Institute for Computer-aided Automation Pattern Recognition and Image Processing Group Vienna Univ. of Technology Austria

ISBN: (纸本)354040452X

Irregular pyramids are made of a stack of successively reduced graphs embedded in the plane. Each vertex of a reduced graph corresponds to a connected set of vertices in the level below. One connected set of vertices reduced into a single vertex at the above level is called the reduction window of this vertex. In the same way, a connected set of vertices in the base level graph reduced to a single vertex at a given level is called the receptive field of this vertex. The graphs used in the pyramid may be region adjacency graphs, dual graphs or combinatorial maps. This last type of pyramids are called Combinatorial Pyramids. Compared to usual graph data structures, combinatorial maps encode one graph and its dual within a same formalism and offer an explicit encoding of the orientation of edges around vertices. This paper describes the construction scheme of a Combinatorial Pyramid. We also provide a constructive definition of the notions of reduction windows and receptive fields within the Combinatorial Pyramid framework. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Graphic methods

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Classifier-based feature fusion for texture discrimination

Classifier-based feature fusion for texture discrimination

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2009 International Conference on Information Engineering and Computer Science, ICIECS 2009

作者： Ma, Jianglin Zhang, Zhouwei Wang, Chengyi Chen, Zhong Remote Sensing Image Processing Laboratory Institute of Remote Sensing Applications CAS Beijing China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424449941

A classifier-based method to select and fuse grey level co-occurrence matrix (GLCM), Gaussian Markov random field (GMRF) and discrete wavelet transform (DWT) features to improve texture discrimination is presented. Feature selection via wrapper approaches is applied to find the optimal combination of texture features. The fused features have obtained higher discrimination accuracy compared with individual features. The curse of dimensionality is shown to affect discrimination accuracy, and feature selection and reduction helps obtain higher accuracy. Overall our proposed classifier-based method obtains the highest discrimination accuracy compared to other feature reduction methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). Meanwhile GLCM features are found to produce higher discrimination accuracy than GMRF and DWT, and LDA is demonstrated to obtain higher discrimination accuracy than PCA. ©2009 IEEE.

关键词： Discrete wavelet transforms

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Color active contour models based tongue segmentation in traditional Chinese medicine

Color active contour models based tongue segmentation in tra...

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2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

作者： Yu, Shengyang Yang, Jie Wang, Yonggang Zhang, Yan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Dept. of Computer Science Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)1424411203

Automated tongue image segmentation in tongue diagnosis system of traditional Chinese medicine is difficult due to two factors: There are lots of pathological details on the surface of tongue, and the shapes of tongue bodies are quite different. By adequately considering color information of tongue images, knowledge-based initial tongue body boundary detection and a color gradient are introduced into the gradient vector flow snake. The roughly detected tongue body boundary is employed to lead the usage of color gradient and the convergence of snake. The experiments results show robustness and accuracy of the algorithm. This work establishes a solid foundation for feature analysis of tongue diagnosis. © 2007 Crown Copyright.

关键词： Medical imaging

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ChromTR:chromosome detection in raw metaphase cell images via deformable transformers

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Frontiers of Medicine 2024年第6期18卷 1100-1114页

作者： Chao Xia Jiyue Wang Xin You Yaling Fan Bing Chen Saijuan Chen Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai200240China Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational Medicine at ShanghaiRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200025China

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging *** this work,focusing on the joint optimization of chromosome localization and classification,we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell *** incorporates semantic feature learning and class distribution learning into a unified DETR-based detection ***,we first propose a Semantic Feature Learning Network(SFLN)for semantic feature extraction and chromosome foreground region segmentation with object-wise ***,we construct a Semantic-Aware Transformer(SAT)with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic *** provide a prediction with a precise chromosome number and category distribution,a Category Distribution Reasoning Module(CDRM)is built for foreground-background objects and chromosome class distribution *** evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset *** proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56%in R-band chromosome detection,surpassing the baseline method by 3.02%.In a clinical test,ChromTR is also confident in tackling normal and numerically abnormal *** extended to the chromosome enumeration task,ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image *** these superior performances to other methods,our proposed method has been applied to assist clinical karyotype diagnosis.

关键词： chromosome detection deformable transformer metaphase cell image distribution reasoning

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Face recognition under variable lighting using local qualitative representations

Face recognition under variable lighting using local qualita...

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MIPPR 2007: pattern recognition and Computer Vision

作者： Zhang, Yi Chu, Ying Mou, Xingang Zhang, Guilin State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819469526

In this paper, a face recognition method using local qualitative representations is proposed to solve the problem of face recognition in varying lighting. Based on the observation that the ordinal relationship between the average brightness of image regions pair is invariant under lighting changes, Local Binary Mapping is defined as an illumination invariant for face recognition based on Local Binary pattern descriptor, which extracts the local variance features of an image. For the 'symbol' feature vector, hamming distance is used as similarity measurement. It has been proved that the proposed method can provide the accuracy of 100 percent for subset 2, 3, 4 and 98.89 percent for subset 5 of the Yale facial database B when all images in subset 1 are used as gallery.

关键词： Face recognition

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