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International Conference on Intelligent Computing, ICIC 2005

作者： Liu, Heng Zhang, David Yan, Jingqi Li, Zushu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 China Southwest University of Scientist and Technology Sichuan 621000 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Institute of Intelligent Automation Chongqing University Chongqing 400044 China

image segmentation is kernel part of image analysis and processing. Fast and robust portrait segmentation and fusion is still a challenging problem by far. In this paper, we present two fast and robust methods to segment portrait in blue background. One method is threshold obtaining based on histogram peak distribution, the other is Quantum Evolution Algorithm (QEA) threshold searching based on group variance. Detailed experiments and comparison analyses are presented to demonstrate the performance of our methods. © Springer-Verlag Berlin Heidelberg 2005.

关键词： image segmentation

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Background suppression based-on wavelet transformation to detect infrared target

Background suppression based-on wavelet transformation to de...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Sun, Y.U.-Qiu Tian, Jin-Wen Liu, Jian School of Information and Mathematics Yangtze University Jingzhou 434023 China 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 State Education Commission Key Laboratory for Image Processing and Intelligent Control Dept. of Electronic Information and Enginery Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)078039092X

Target detection and location in infrared clutter background is very important to infrared search and track system. Especially for small target detection in infrared image in background of sea and sky, there are no geometric and structure character to use. When targets such as ship and naval vessel sailing at long distance, they always appear around the sea-level line, and it is mixed with cloud and sea-wave clutter. It is difficult to segment and locate precisely. Background suppression based-on wavelet transformation is proposed in the paper. Wavelet decomposition makes it possible to analyze a signal both in time and frequency domains. In the paper infrared sea and key background images are processed. For their SNR is low and background is complex, using wavelet transformation decomposes an original image and extract approximate feature to reconstruct an image, which mainly includes background information. A new image will be obtained using background image subtracted from original image. There are mainly target and noise points left in the new image. By setting proper threshold, the target can be detected perfectly. At last, experiment results are given and show the method is practical. © 2005 IEEE.

关键词： pattern recognition

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A new approach of color quantization based on ant colony clustering algorithm

A new approach of color quantization based on ant colony clu...

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International Conference on Information Technology: Coding and Computing (ITCC)

作者： Xinrong Hu Tianzhen Wang Dehua Li Institute for Pattern Recognition and Artificial Intelligence State Education Commission Laboratory Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China Institute of Image Processing Wuhan University of Technology China

ISBN: (纸本)0769523153

Color quantization is wildly exploited for many applications especially in the fields of computer graphics and image processing. After studying the approaches of color clustering, a new approach based on ant colony clustering algorithm applied in color image quantization is proposed in the paper. According to the picking up-dropping theory, a promoted ant algorithm is applied to group colors into certain clusters in RGB space. It finishes color quantization after colors mapping of every pixel. Our experiment shows that the algorithm proposed in this paper has rather good performance with an excellent robustness, a less time consumption, and a simple realization.

关键词： Quantization Clustering algorithms Information technology

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Encoding Methods of Spectral Vector in Hyperspectral Romote Sensing image

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Journal of Shanghai University(English Edition) 2005年第1期9卷 52-57页

作者：杜培军方涛唐宏施鹏飞 Department of Remote Sensing and Geographical Information Science China University of Mining and Technology Xuzhou 221008 P.R. China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 P.R. China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 P.R. China

Taking into account the demands of hyperspectral remote sensing(RS) image retrieval and processing, some encoding methods of spectral vector including direct encoding, feature-based encoding and tree-based encoding methods are proposed and compared. In direct encoding, based on the analysis of binary encoding and quad-value encoding, decimal encoding is proposed. It is proved that quad-value encoding and decimal encoding are suitable to fast processing and retrieval. In absorption feature-based encoding method, five common metrics are compared. Because locations of reflection/absorption features are sensitive to noise, this method is not very effective in retrieval. In tree-based encoding methods, bitree, quadtree, octree and hextree are proposed and discussed. It is proved that 2-level octree and 2-level hextree are more effective than bitree and quadtree. Finally, quad-value encoding, decimal encoding, 2-level octree and 2-level hextree are proposed in spectral vectors encoding, similarity measure and hyperspectral RS image retrieval.

关键词： hyperspectral remote sensing spectral vector encoding similarity measure spectral angle.

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A robust approach to the detection and tracking of small targets with low contrast

A robust approach to the detection and tracking of small tar...

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IEEE International Workshop on VLSI Design and Video Technology, IWVDVT

作者： Xinyu Wang Guilin Zhang Ying Chu State Key Laboratory for Image Processing and lntelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780390059

An effective approach to the detection and tracking of small moving targets with low contrast is proposed. In our application, small moving targets are detected and tracked in a low quality video sequence captured from an airborne platform. The detection and tracking system includes three modules. First, the global motion parameters are estimated using fast least trimmed squares (FAST-ITS) regression and hierarchical processing. Once the global motion is robustly estimated, relatively stationary background can be eliminated almost completely through image warping. Then a motion-based fast target detector selects candidate pixels of the moving targets with motions statistically different from that of the background, and a particle filtering tracker examines the temporal consistency of targets to identify them and update their states. Information in the system flows in a closed loop form, in which the tracker instructs the detector where to look for a target, and the detector returns what it has found. Experimental results prove that the proposed method can reliably and effectively detect and track the real small moving target in real-time even if there is strong clutter influence.

关键词： Robustness Target tracking Detectors Motion estimation Video sequences Parameter estimation Motion detection Filtering Particle tracking Tracking loops

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Line-based landmark recognition for self-localization of soccer robots

Line-based landmark recognition for self-localization of soc...

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International Conference on Emerging Technologies, ICET

作者： A. Baist R. Sablatnig G. Novak Pattern Recognition and Image Processing Group Vienna University of Technology Vienna Austria Institute of Computer Technology Vienna University of Technology Vienna Austria

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A framework for the uncertain spatial data mining

A framework for the uncertain spatial data mining

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International Conference on Space Information Technology

作者： He, Binbin Fang, Tao Guo, Dazhi Institue of Geo-Spatial Information Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200030 China School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou 221008 China

ISBN: (纸本)0819460079

On the basis of analyzing the uncertainties of spatial data mining (SDM), and in view of the limits of traditional spatial data mining, the framework for the uncertain spatial data mining has been founded. For which, four key problems have been probed and analyzed, including uncertainty simulation of spatial data with Monte Carlo method, measurement of spatial autocorrelation based on uncertain spatial positional data, discretization of continuous data based on neighborhood EM algorithm and quality assessment of results. Meanwhile, the experiments concerned have been performed using the geo-spatial datum gotten from 37 typified cites in China.

关键词： Data mining

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Performing clustering analysis on collaborative models

Performing clustering analysis on collaborative models

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作者： Shen, Hongbin Yang, Jie Chen, Ningjiang Dong, Yifei Wang, Shitong Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 China Philips Research East Asia Shanghai 200070 China School of Computer Science and Engineering University of New South Wales Australia Department of Information Science Southern Yangtze University Wuxi Jiangsu 214036 China

Conventional clustering algorithms are designed for a single independent dataset, i.e. Fuzzy C-Means (FCM) clustering algorithm. In the real world, a dataset is independent of other datasets but sometimes can be cooperative with others by exchanging information, such as the relationship between subsidiary companies. We should therefore consider the influence from other relative collaborative datasets while performing clustering learning under such collaborative circumstances. In this paper, three different collaborative models are discussed and new correct methods are proposed to quantitatively measure such collaboration between datasets, i.e. information gain. The corresponding collaborative clustering algorithms are presented accordingly and the theoretical analysis shows that the new cooperative clustering algorithms can finally converge to a local minimum. Experimental results demonstrate that the clustering structures obtained by new cooperative algorithms are different from those of conventional algorithms for the consideration of collaboration and the performances of these collaborative clustering algorithms can be much better than those conventional single clustering algorithms under the cooperating circumstances. ©?2005-IOS Press and the authors. All rights reserved.

关键词： Clustering algorithms

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Noise estimation in panoramic x-ray images: an application analysis approach

Noise estimation in panoramic x-ray images: an application a...

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IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： P.M. Goebel A.N. Belbachir M. Truppe Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Karl Landsteiner Institute for Biotelematics Danube-University of Krems Krems Austria

This paper presents an appropriate approach for the robust estimation of the noise statistics in dental panoramic X-ray images. To achieve maximum image quality after denoising, a semi-empirical scatter model is presented, leading to a local adaptive Gaussian scale mixture (GSM) model. State of the art methods use multiscale filtering of images to reduce the irrelevant part of information, based on generic estimation of noise. The usual assumption of a distribution of Gaussian and Poisson statistics only leads to overestimation of the noise variance in regions of low intensity (small photon counts), but to underestimation in regions of high intensity and therefore to non-optimal results. The analysis approach is tested on a database of 50 panoramic X-ray images and the results are cross-validated by medical experts. It is shown that the local standard deviation (SDEV) in images, stemming from homogeneous phantoms (AI, PMMA), follows a generalized Nakagami distribution (GND). The heavily tailed distribution is not covered entirely by the GND. The error density function, is hypothesized to stem from scatter-glare, degrading the image. A beam stop method, for estimation of the scatter-glare amount, verifies that hypothesis. Finally, the application of the method for a phantom image, is shown with denoising results for comparison purpose, followed by the conclusion

关键词： X-ray imaging image analysis Noise reduction Electromagnetic scattering Particle scattering X-ray scattering Imaging phantoms Noise robustness Statistics Dentistry

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A robust matching algorithm for active appearance models 1

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2005 - Workshop on image processing for Medicine: Algorithms - Systems - Applications, BVM 2005

作者： Beichel, Reinhard Bischof, Horst Langs, Georg Sonka, Milan Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/2 A-8010 Graz Austria Pattern Recognition and Image Processing Group Vienna University of Technology Favoritenstr. 9 A-1040 Vienna Austria Department of Electrical and Computer Engineering University of Iowa Iowa City IA 52242 United States

ISBN: (数字)9783540264316

ISBN: (纸本)3540250522

This paper proposes a method for robustly matching active appearance models (AAMs) on images with gross disturbances (outliers). The method consists of two steps. First, an initial residual is calculated by comparing model and image appearance, and modes of the residual are analyzed. Second, all possible mode combinations are tested by evaluating an objective function. The objective function allows the selection of an outlier-free mode combination. Experiments demonstrate the ability of the robust matching method to successfully cope with outliers - compared to standard AAM matching, no degeneration of the model during matching occurs.

关键词： Statistics

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