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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3338卷 506-515页

作者： Zhang, Yongliang Yang, Jie Wu, Hongtao Xue, Yunfeng Inst. of Image Processing and Pattern Recognition Shanghai JiaoTong Univ. 200030 Shanghai China School of Computer Science and Software Hebei University of Technology 300130 Tianjin China

Thermal swipe sensor is one of the technologies behind many of today's fingerprint sensors. This paper presents a new direct Fourier-based algorithm for performing swipe fingerprints registration to subpixel accuracy, where the fingerprint image differences are restricted to translations and uniform changes of illumination. The method is based on the extension of the well-known phase correlation technique and is able to estimate large translations of swipe fingerprints with pixel and subpixel accuracy. Excellent experimental results have been obtained for pixel and subpixel translation estimation of swipe fingerprints. And experimental data show that the algorithm has less time consumption and more robustness against noise and superior registration precision. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Pixels

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image SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES

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Journal of Shanghai Jiaotong university(Science) 2002年第1期7卷 36-41页

作者： NASSIR H.SALMAN(纳瑟) LIU Chong-qing (刘重庆) Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China

This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.

关键词： Markov random field(MRF) watershed algorithm K-means edge detection image segmentation image analysis

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image ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

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Journal of Shanghai Jiaotong university(Science) 2002年第2期7卷 198-203页

作者： NASSIR H.SALMAN(纳瑟) LIU Chong-qing(刘重庆) Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China

A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model, gray level l , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.

关键词： Difference In Strength (DIS) Markov Random Field (MRF) watershed algorithm K-means edge detection image segmentation image analysis

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TOWARDS AN ONTOLOGY-BASED MULTI-AGENT MEDICAL INFORMATION SYSTEM BASED ON THE WEB

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Journal of Shanghai Jiaotong university(Science) 2002年第2期7卷 194-197页

作者： ZHANG Quan-hai(张全海) SHI Peng-fei(施鹏飞) Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China

This paper described an ontology based multi agent knowledge process made (MAKM) which is one of multi agents systems (MAS) and uses semantic network to describe agents to help to locate relative agents distributed in the workgroup. In MAKM, an agent is the entity to implement the distributed task processing and to access the information or knowledge. Knowledge query manipulation language (KQML) is adapted to realize the communication among agents. So using the MAKM mode, different knowledge and information on the medical domain could be organized and utilized efficiently when a collaborative task is implemented on the web.

关键词： multi-agents system (MAS) medical information semantic network ontology

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image Analysis Based on Edge Detection Techniques

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Journal of Shanghai Jiaotong University(Science) 2002年第2期E-7卷 198-203页

作者：纳瑟刘重庆 Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China

A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K-means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model,gray level l, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.

关键词： Difference In Strength (DIS) Markov Random Field (MRF) watershed algorithm K-means edge detection image segmentation image analysis

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Towards an Ontology-Based Multi-Agent Medical Information System Based on the Web

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Journal of Shanghai Jiaotong University(Science) 2002年第2期E-7卷 194-197页

作者：张全海施鹏飞 Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China

This paper described an ontology-based multi-agent knowledge process made (MAKM) which is one of multi-agents systems (MAS) and uses semantic network to describe agents to help to locate relative agents distributed in the workgroup. In MAKM, an agent is the entity to implement the distributed task processing and to access the information or knowledge. Knowledge query manipulation language (KQML) is adapted to realize the communication among agents. So using the MAKM mode, different knowledge and information on the medical domain could be organized and utilized efficiently when a collaborative task is implemented on the web.

关键词： multi-agents system (MAS) medical information semantic network ontology

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GLOBAL MEASURE ON image CONTENT

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Journal of Shanghai Jiaotong university(Science) 2000年第2期5卷 108-111页

作者：李介谷 Inst.of Image Processing & Pattern Recog. Shanghai Jiaotong Univ.Shanghai 200030China

This paper investigated approaches to supporting effective and efficient retrieval of image based on principle component analysis. First, it extracted the image content, texture and color. Gabor wavelet transforms were used to extract texture feature of the image and the average color was used to extract the color features. The principle component of the feature vector of image can be constructed. Content based image retrieval was performed by comparing the feature vector of the query image with the projection feature vector of the image database on the principle component space of the query image. By this technique, it can reduce the dimensionality of feature vector, which in turn reduce the searching time.

关键词： content based image retrieval principle component analysis average color texture Gabor wavelet transform Document code:A

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Tracking object using improved Hausdorff measure

Tracking object using improved Hausdorff measure

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World Congress on Intelligent Control and Automation (WCICA)

作者： Ke Liu Xianmin Zhang Institute of Image Processing & Pattern Recog. Shanghai Jiaotong University Shanghai China Inst.of Image Processing & Pattern Recog Shanghai Jiaotong University Shanghai China

This paper describes the techniques for tracking nonrigid object moving in a complex scene. The improvement incorporates that both location and orientation information are used in model-matching and model-updating process. In this paper, we also propose a highly efficient searching strategy of locating object in an image. Results indicate that these techniques can perform well in complex conditions such as moving background and moving camera.

关键词： image processing Layout Cameras

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A Region-Based Representation of images in MARS

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Journal of VLSI Signal processing Systems for Signal, image, and Video Technology 1998年第1-2期20卷 137-150页

作者： Servetto, Sergio D. Rui, Yong Ramchandran, Kannan Huang, Thomas S. Beckman Inst. Adv. Sci. and Technol. Univ. Illinois at Urbana-Champaign Urbana IL 61801 United States Universidad Nacional de La Plata Argentina Univ. Illinois at Urbana-Champaign United States Comp. Res. Adv. Applications Group IBM Argentina Argentina Image Formation and Processing Group Beckman Institute UIUC United States Department of Computer Science UNLP Argentina Dept. of Elec. and Comp. Engineering UIUC United States Multimedia Commun. Res. Department Bell Laboratories Murray Hill NJ United States Info. Sciences Research Department AT and T Labs. Florham Park NJ United States Department of Computer Science UIUC United States Southeast University China Tsinghua University China University of Illinois Urbana-Champaign IL United States Image Formation and Processing Group Beckman Inst. Advance Sci. Technol. UIUC United States Vis. Technol. Grp. of Microsoft Res. Redmond WA United States City College of New York United States Columbia University United States AT and T Bell Labs. United States Ctr. for Telecommunications Research Columbia University United States Elec. and Comp. Eng. Department United States Beckman Institute Coordinated Science Laboratory IL United States IEEE Signal Processing Society United States IEEE IMDSP Technical Committee United States IEEE Transactions on Image Proc. United States National Taiwan University Taipei Taiwan Massachusetts Inst. of Technology Cambridge MA United States Department of Electrical Engineering MIT United States School of Electrical Engineering United States Lab. for Info. and Signal Processing Purdue University United States Dept. of Elec. and Comp. Engineering United States Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Inst. Adv. Sci. and Technol. United States MIT Lincoln Laboratory IBM Thomas J. Watson Research Center Rheinishes Landes Museum Bonn Germany Swiss Institutes of Technology Zurich Switzerland Swiss Institutes of Technology Lausanne S

We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propose new image coding techniques which combine a wavelet representation, embedded coding of the wavelet coefficients, and segmentation of image-domain regions in the wavelet domain. A bitstream is generated in which each image region is encoded independently of other regions, without having to explicitly store information describing the regions. Simulation results show that our proposed algorithms achieve coding performance which compares favorably, both perceptually and objectively, to that achieved using state-of-the-art image/video coding techniques while additionally providing region-based support.

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An efficient moving target identification method

An efficient moving target identification method

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IEEE International Conference on Intelligent processing Systems, ICIPS

作者： Nong Shang Tianxu Zhang Jiaxiong Peng Inst. for Pattern Recognition & Artifical Intelligence Huazhong Univ. of Sci. & Technol. Hubei China Institute for pattern Recognition and Artificial intelligence state Education commission Laboratory for Image processing and Intelligent control Huazhong University of Science슠and슠Technology Wuhan Hubei China

ISBN: (纸本)0780342534

Presents an efficient method which uses two neighboring frames in image sequences for target identification. Using statistical information about the background noise and candidate regions' noise after background registration, we can determine those candidate regions that have the same or similar noise distributions to the background's which should be background regions, and those candidate regions that have different noise distributions from the background's which should be the target region. In particular, when there is only one target in the image, we can simply determine that the candidate region whose noise distribution is most different from the background's is the true target.

关键词： Background noise image sequences

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