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Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University 2007年第5期41卷 813-817页

作者： He, Hu-Yi Yao, Li-Xiu Shen, Hong-Bin Yang, Jie Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200240 China

A kind of subspace clustering algorithm based on density and grids was proposed. Boundaries between classes are located by partitioning grids in the data space and finding sparse regions. The algorithm uses the searching strategy of projected pursuit to find the classes in the subspace. A competitive pruning procedure is utilized to reduce the computational complexity. The experimental results on the benchmark datasets show that the proposed algorithm has advantages in accuracy and computational complexity.

关键词： Clustering algorithms

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Multi-label image segmentation via max-sum solver

Multi-label image segmentation via max-sum solver

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Banislav Micusik Tomas Pajdla Pattern Recognition and Image Processing Group Inst. of Computer Aided Automation University of Technology Vienna Austria Center for Machine Perception Dpt. of Cybernetics Czech Technical University Czech Republic

We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two labels, we go beyond widespread binary segmentation methods, e.g., MIN-CUT or normalized cut based approaches. We show that the MAX-SUM solver is a very powerful tool for obtaining the MAP estimate of a Markov random field (MRF). We build the MRF on superpixels to speed up the segmentation while preserving color and texture. We propose new quality functions for setting the MRF, exploiting priors from small representative image seeds, provided either manually or automatically. We show that the proposed automatic segmentation method outperforms previous techniques in terms of the global consistency error evaluated on the Berkeley segmentation database.

关键词： image segmentation image databases Belief propagation pattern recognition image processing Automation Cybernetics Color Linear programming Markov random fields

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Color-texture based unsupervised segmentation using JSEG with fuzzy connectedness

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Journal of Systems Engineering and Electronics 2006年第1期17卷 213-219页

作者： Zheng Yuanjie Yang Jie Zhou Yue Wang Yuzhong Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 P. R. China

Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven＇ t been heavily blurred near boundaries of underlying regions.

关键词： unsupervised segmentation color segmentation color texture segmentation fuzzy method.

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Color-texture segmentation using JSEG based on Gaussian mixture modeling

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Journal of Systems Engineering and Electronics 2006年第1期17卷 24-29页

作者： Wang Yuzhong Yang Jie Zhou Yue Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 P. R. China

An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.

关键词： color image segmentation JSEG adaptive mean shift based dustering Gaussian mixture modeling soft J-value.

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Finding minimal rough set redacts with particle swarm optimization

Finding minimal rough set redacts with particle swarm optimi...

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High-Power Diode Laser Technology and Applications IV

作者： Wang, Xiangyang Yang, Jie Peng, Ningsong Teng, Xiaolong Inst. of Image Processing and Pattern Recognition Jiaotong University Shanghai 200030 China

ISBN: (纸本)0819461466

We propose a new algorithm to find minimal rough set reducts by using Particle Swarm Optimization (PSO). Like Genetic Algorithm, PSO is also a type of evolutionary algorithm. But compared with GA, PSO does not need complex operators as crossover and mutation that G A does, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and times. The experiments on some UCI data compare our algorithm with GA-based, and other deterministic rough set reduction algorithms. The results show that PSO is efficient to minimal rough set reduction. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Rough set theory

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Research on a novel restoration algorithm of turbulence-degraded images with alternant iterations

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Journal of Systems Engineering and Electronics 2006年第3期17卷 477-482页

作者： Liu Chunsheng Hong Hanyu Zhang Tianxu Inst. for Pattern Recognition and AI State Key Laboratory of Image Processing and Intelligent Control Huazhong Univ. of Science and Technology Wuhan 430074 P. R. China

A new restoration algorithm based on double loops and alternant iterations is proposed to restore the object image effectively from a few frames of turbulence-degraded images, Based on the double loops, the iterative relations for estimating the turbulent point spread function PSF and object image alternately are derived. The restoration experiments have been made on computers, showing that the proposed algorithm can obtain the optimal estimations of the object and the point spread function, with the feasibility and practicality of the proposed algorithm being convincing.

关键词： turbulence-degraded image image restoration double loops alternant iterations.

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A Model of Car-Following with Adaptive Headway

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Journal of Shanghai Jiaotong university(Science) 2006年第3期11卷 394-398页

作者：李志鹏刘允才 Inst.of Image Processing and Pattern Recognition Shanghai Jiaotong Univ.Shanghai 200240China

The performances of a well-known GHR car-following model was investigated by using numerical simulations in describing the acceleration and deceleration process induced by the motion of a leading car. It is shown that in GHR model vehicle is allowed to run arbitrarily close together if their speed are identical,and it waves aside even though the separation is larger than its desired distance. Based on these investigations, a modified GHR model which features a new nonlinear term which attempts to adjust the inter-vehicle spacing to a certain desired value was proposed accordingly to overcome these deficiencies. In addition, the analysis of the additive nonlinear term and steady-state flow of the new model were studied to prove its rationality.

关键词： car-following models inter-vehicle spacing traffic flow steady-state

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Template patch driven image segmentation

Template patch driven image segmentation

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2006 17th British Machine Vision Conference, BMVC 2006

作者： Mičušík, Branislav Hanbury, Allan Pattern Recognition and Image Processing Group Inst. of Computer Aided Automation Vienna University of Technology Favoritenstr. 9/1832 A-1040 Vienna Austria

ISBN: (纸本)1904410146

We present a method that partitions a single image into two layers, requiring that one layer has similar properties in terms of pixel colour variation to a provided template patch. First the paper provides a new view on defining a similarity function for a pixel with its small neighbourhood to be part of the texture described by the template patch. This results in better description of pixels near the texture boundary. Second, it is shown how the Maximally Stable Extremal Regions (MSERs), originally designed for wide baseline stereo matching, can be used to locally merge pixels having the same intensity and thus reduce the dimension of the graph representing the image. The MSERs help in texture description and yield significant reduction of memory and computation time. Finally the graph is fed into the ***/*** algorithm to cut the graph into two parts. Performance of the method is presented on some images from the Berkeley database. Finally, restrictions in using the method are discussed.

关键词： Pixels

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A Model of Car-Following with Adaptive Headway

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Journal of Shanghai Jiaotong University(Science) 2006年第3期000卷 394-398页

作者： LI Zhi-peng LIU Yun-cai Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200240 China

The performances of a well-known GHR car-following model was investigated byusing numerical simulations in describing the acceleration and deceleration process induced by themotion of a leading car. It is shown that in GHR model vehicle is allowed to run arbitrarily closetogether if their speed are identical, and it waves aside even though the separation is larger thanits desired distance. Based on these investigations, a modified GHR model which features a newnonlinear term which attempts to adjust the inter-vehicle spacing to a certain desired value wasproposed accordingly to overcome these deficiencies. In addition, the analysis of the additivenonlinear term and steady-state flow of the new model were studied to prove its rationality.

关键词： car-following models inter-vehicle spacing traffic flow steady-state

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Model of car-following with adaptive headway

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Journal of Shanghai Jiaotong University (Science) 2006年第3期11 E卷 394-398页

作者： Li, Zhi-Peng Liu, Yun-Cai Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200240 China

The performances of a well-known GHR car-following model was investigated by using numerical simulations in describing the acceleration and deceleration process induced by the motion of a leading car. It is shown that in GHR model vehicle is allowed to run arbitrarily close together if their speed are identical , and it waves aside even though the separation is larger than its desired distance. Based on these investigations, a modified GHR model which features a new nonlinear term which attempts to adjust the inter-vehicle spacing to a certain desired value was proposed accordingly to overcome these deficiencies. In addition, the analysis of the additive nonlinear term and steady-state flow of the new model were studied to prove its rationality.

关键词： Street traffic control

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