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Conference on Computer Vision and pattern recognition (CVPR)

作者： Fanhuai Shi Jianhua Wang Jing Zhang Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0769523722

We present an algebraic approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody line constraint of translational motions. We show how to linearly estimate the multibody trifocal epipole from line-line-line correspondences. The individual trifocal epipoles are then obtained from the derivatives of the multibody line constraint (up to an unknown factor). Given normalized trifocal epipoles, we can use any special clustering technique to obtain the clustering of the motions and the correspondences. The limitations of the proposed algorithm are also discussed. Experimental results on synthetic and real dynamic scenes are presented.

关键词： Motion segmentation Computer vision Layout Motion estimation Cameras pattern recognition Polynomials Clustering algorithms image motion analysis Motion analysis

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Weighted MLS-SVM for approximation of directional derivatives

Weighted MLS-SVM for approximation of directional derivative...

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MIPPR 2005: image Analysis Techniques

作者： Zheng, Sheng Tian, JinWen Liu, Jian China Three Gorges University Yichang 443002 China Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

Based on statistical learning theory, support vector machine (SVM) is a novel type of learning machine, and it contains polynomial, neural network and radial basis function (RBF) as special cases. The mapped least squares support vector machine (MLS-SVM) is a special least square SVM (LS-SVM), which extends the application of the SVM to the image processing. Based on the MLS-SVM, a family of filters for the approximation of partial derivatives of the digital image surface is designed. Prior information (e.g., local dominant orientation) are incorporated in a two dimension weighted function. The weighted MLS-SVM with the radial basis function kernel is applied to design the proposed filters. Exemplary application of the proposed filters to fingerprint image segmentation is also presented.

关键词： Learning systems

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Segmentation of players and team discrimination in soccer videos

Segmentation of players and team discrimination in soccer vi...

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IEEE International Workshop on VLSI Design and Video Technology, IWVDVT

作者： Zhifei Xu Pengfei Shi Institute of image processing and pattern recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0780390059

In this paper, we introduce methods to extract low-level features for soccer video analysis. A new method is proposed to segment players by using a mean distributed color feature. In order to discriminate which team the player belongs to, we use mutual chromatic correlation degree of players to identify team without extracting templates of players in advance. Experimental results are included to show the effectiveness of the method.

关键词： Videos image segmentation Lighting Games Pixel Colored noise Feature extraction image analysis Event detection image processing

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Support value based fusing images with different focuses

Support value based fusing images with different focuses

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Zheng, Sheng Sun, Yu-Qiu Tian, Jin-Wen Liu, Jian China Three Gorges University Yichang 443002 China Key Laboratory for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)078039092X

Many vision-related processing tasks, including edge detection and image segmentation, can be performed more easily when all objects in the scene are in good focus. However, in practice, this may not be always feasible as optical lenses, especially those with long focal lengths, only have a limited depth of field. One classical approach to recover an everywhere-in-focus image is to use Laplacian pyramid image fusion. First, several source images with different focuses of the same scene are taken and decomposed into the low/high-frequency components image sequences. Within these decompositions, the high-frequency components image sequences with the largest magnitude are selected at each pixel location. Finally, the fused image can be recovered from the decomposed components image sequences. In the support vector machine (SVM), the pixels with larger support values have a physical meaning in the sense that they reveal relative more importance of the data points for contributing to the SVM model. In this paper, we use Laplacian pyramid for the multiresolution decomposition, and then replace the traditional salient features by support values of the mapped least squares (LS)-SVM for fusing image. Experimental results illustrate that the proposed method outperforms the traditional approach. ©2005 IEEE.

关键词： image segmentation

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Blind background subtraction in dental panoramic X-ray images: An application approach

Blind background subtraction in dental panoramic X-ray image...

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27th DAGM (German Association for pattern recognition) Symposium, DAGM 2005

作者： Goebel, Peter Michael Belbachir, Nabil Ahmed Truppe, Michael Vienna University of Technology Institute of Computer Aided Automation Pattern Recognition and Image Processing Group Austria Fh-campus Wien Technical Project- and Processmanagement Austria Karl Landsteiner Institute for Biotelematics Danube University Krems Austria

Dental Panoramic X-ray images are images having complex content, because several layers of tissue, bone, fat, etc. are superimposed. Non-uniform illumination, stemming from the X-ray source, gives extra modulation to the image, which causes spatially varying X-ray photon density. The interaction of the X-ray photons with the density of matter causes spatially coherent varying noise contribution. Many algorithms exist to compensate background effects, by pixel based or global methods. However, if the image is contaminated by a non-negligible amount of noise, that is usually non-Gaussian, the methods cannot approximate the background efficiently. In this paper, a dedicated approach for background subtraction is presented, which operates blind, that means the separation of a set of independent signals from a set of mixed signals, with at least, only little a priori information about the nature of the signals, using the A-Trous multiresolution transform to alleviate this problem. The new method estimates the background bias from a reference scan, which is taken without a patient. The background values are rescaled by a polynomial compensation factor, given by mean square error criteria, thus subtracting the background will not produce additional artifacts in the image. The energy of the background estimate is subtracted from the energy of the mixture. The method is capable to remove spatially varying noise also, allocating an appropriate spatially noise estimate. This approach has been tested on 50 images from a database of panoramic X-ray images, where the results are cross validated by medical experts. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Medical imaging

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Improved MBNN for image segmentation

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Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications 2005年第SUPPL. 2期28卷 55-57+61页

作者： Cai, Nian Yang, Jie Hu, Kuang-Hu Xiong, Hai-Tao Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

An improved MBNN (model-based neural network) was proposed to segment images. An image model obtained by the Markov random filed (MRF) was introduced into the MBNN. The MRF's parameters were estimated by modified expectation-maximization (EM) algorithm. The technique of pre-assigning a class number was employed to decrease the computation burden. Therefore the task of image segmentation was implemented by the network. The experiment results show that it is feasible to apply the improved MBNN to image segmentation since a priori knowledge is excellently combined with local statistical correlation.

关键词： image segmentation

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A framework for spatial-variant thresholding of digital subtraction angiography images

A framework for spatial-variant thresholding of digital subt...

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MIPPR 2005: Geospatial Information, Data Mining, and Applications

作者： Sang, Nong Peng, Weixue Li, Heng Zhang, Tianxu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

Vessel segmentation is the base of 3d reconstruction of Digital Subtraction Angiograph (DSA) images. This paper proposes a framework of adaptive local thresholding based on a verification-based approach for vessel segmentation of DSA images. The original DSA image is firstly divided into overlapping subimages according to a priori knowledge of the diameter of vessels. We implement a hypothesis test to determine whether each subimage contains vessels and then choose an optimal threshold respectively for every subimage previously determined to contain vessels, with a secondary verification process to exclude the condition that the subregion only containing the background but misclassified as one containing vessels by the hypothesis test. Finally an overall binarization of the original image is achieved by combining the thresholded subimages. Experiments demonstrate superior performance over global thresholding and some adaptive local thresholding methods.

关键词： Angiography

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Adaptive thresholding of digital subtraction angiography images

Adaptive thresholding of digital subtraction angiography ima...

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MIPPR 2005: image Analysis Techniques

作者： Sang, Nong Li, Heng Peng, Weixue Zhang, Tianxu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in the human body. Blood vessel segmentation is a main problem for 3D vascular reconstruction. In this paper, we propose a new adaptive thresholding method for the segmentation of DSA images. Each pixel of the DSA images is declared to be a vessel/background point with regard to a threshold and a few local characteristic limits depending on some information contained in the pixel neighborhood window. The size of the neighborhood window is set according to a priori knowledge of the diameter of vessels to make sure that each window contains the background definitely. Some experiments on cerebral DSA images are given, which show that our proposed method yields better results than global thresholding methods and some other local thresholding methods do.

关键词： image analysis

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Human Activity recognition with User-Free Accelerometers in the Sensor Networks

Human Activity Recognition with User-Free Accelerometers in ...

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2005 International Conference on Neural Networks and Brain(ICNN&B’05)

作者： Shuangquan Wang Jie Yang Ningjiang Chen Xin Chen Qinfeng Zhang Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University Philips Research East Asia

Many applications using wireless sensor networks (WSNs) aim at providing friendly and intelligent services based on the recognition of human's *** the research result on wearable computing has been fruitful,our experience indicates that a user-free sensor deployment is more natural and acceptable to *** our system,activities were recognized through matching the movement patterns of the objects,which tri-axial accelerometers had been attached to. Several representative features,including accelerations and their fusion,were calculated and three classifiers were tested on these *** with Decision Tree(DT) C4.5 and Multiple-Layer Perception(MLP),Support Vector Machine (SVM) performs relatively well across different tests. Additionally,feature selection will be discussed for better system performance for WSNs.

关键词： Human Activity recognition with User-Free Accelerometers in the Sensor Networks Sensor Activity

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Diversity Sampling Based Kernel Density Estimation for Background Modeling

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Journal of Shanghai University(English Edition) 2005年第6期9卷 506-509页

作者：毛燕芬施鹏飞 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 P.R. China

A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.

关键词： background subtraction diversity sampling kernel density estimation multi-modal background model

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