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International Conference on Space Information technology

作者： Lu, Li Shi, Baochang Tian, Jinwen Liu, Jian State Education Commission Key Lab for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei 430074 China Inst. for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China Department of Mathematics Huazhong University of Science and Technology Wuhan Hubei 430074 China

Walsh-Haar function system that was first intruoduced by us is a new kind of function systems, and has a good global/local property. This function system is called Walsh ordering function system since its generation kernel functions belong to Walsh ordering Walsh function system. We worked out a recursive property of the matrix WHKRm+1 corresponding to the first KR m+1 Walsh-Haar functions in Walsh-Haar function system, and we also proved that Walsh-Haar function system is perfect and orthogonal similar to Walsh function system and Haar function system. Thus, discrete Walsh-Haar transformation (DW-HT) is an orthogonal transformation that can be widely used in signal processing. In this paper, using the recursive property of the matrix WHKRm+1 and the fast algorithm of discrete Walsh transformation (DWT) in Walsh ordering, we have designed a fast algorithm of Walsh ordering DW-HT based on the bisection technique. The idea and method used in this paper can be used for designing fast algorithms of other ordering DW-HTs and other discrete orthogonal transformations.

关键词： Mathematical transformations

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Fast and robust portrait segmentation using QEA and histogram peak distribution methods

Fast and robust portrait segmentation using QEA and histogra...

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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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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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Multifeature distance map based detection of small infrared targets with low contrast in image sequences

Multifeature distance map based detection of small infrared ...

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International Conference on Space Information technology

作者： Wang, Zhicheng Gao, Chenqiang Tian, Jinwen Liu, Jian Chen, Xiangping State Education Commission Key Lab for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei 430074 China Inst. for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China Electronic and Engineering Department Huazhong University of Science and Technology Wuhan Hubei 430074 China

Target detection techniques play an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. In this paper, a novel method for fusion detection of infrared weak targets based on multifeature distance map (MFDM) in image sequences is proposed. As for small weak targets, there are many features, such as local entropy, average gradient strength. These features depict the characteristics of small infrared targets and can be extracted. Multifeature-based fusion techniques are applied to detect such weak targets. The problem of detecting small targets is converted to search peak values in specified feature space where multifeature vectors space (MFVS) is considered. Distance map (DM) can be derived according to feature vectors and target detection is performed in DM. In order to accumulate energy of targets deeply and suppress background and clutters to a great extent, five distance maps obtained by corresponding five consecutive frames are utilized to fuse with average weight, which results in the fact that the contrast between targets and background including clutters are enlarged and that the feature peaks of targets are obvious different from background and clutters. After these steps, a contrast segmentation method is used to extract targets from complicated background on the fused DM. Actual infrared image sequences in background of sea and sky are applied to validate the proposed approach. Experimental results demonstrate the robustness of the proposed method with high performance.

关键词： Automatic target recognition

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Background removal in dental panoramic X-ray images by the A-Trous multiresolution transform

Background removal in dental panoramic X-ray images by the A...

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European Conference on Circuit Theory and Design, ECCTD

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

Dental panoramic X-ray images are images having complex content, because several layers of tissue, bone, fat, etc. are superimposed. Nonuniform 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 nonnegligible amount of noise, that is usually nonGaussian, the methods cannot approximate the background efficiently. In this paper, a dedicated approach for the removal of a multiplying background is presented using polynomial scaling and the A-Trous multiresolution transform. The new method uses a background image and a diagnostic image together to estimate the density of the diagnostic content. It assumes a locally Gaussian statistic scaled by a hidden factor, where the hidden factor represents the variance of the nonGaussian process of image generation. Because, the method also removes noise from the compound signal, a comparison to a standard denoising method is given. 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.

关键词： Dentistry X-ray imaging image resolution Spatial resolution Signal resolution Bones Lighting Optical modulation Spatial coherence Background noise

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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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Visual Surveillance for Aircraft Activity Monitoring

Visual Surveillance for Aircraft Activity Monitoring

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Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance

作者： D. Thirde M. Borg J. Ferryman V. Valentin F. Fusier F. Bremond M. Thonnat J. Aguilera M. Kampel Computational Vision Group University of Reading UK ORION Team I.N.R.I.A. Sophia Antipolis France Pattern Recognition and Image Processing Group University of Technology Vienna Austria

This paper presents a visual surveillance system for the automatic scene interpretation of airport aprons. The system comprises two modules - scene tracking and scene understanding. The scene tracking module, comprising a bottom-up methodology, and the scene understanding module, comprising a video event representation and recognition scheme, have been demonstrated to be a valid approach for apron monitoring

关键词： Surveillance Aircraft Monitoring Layout Tracking Motion detection Airports Vehicles image motion analysis Computer vision

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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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Real-time signal processing system for target tracking based on ADSP21060 and FPGA

Real-time signal processing system for target tracking based...

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International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Guangzhou Zhao Tianxu Zhang State Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780390156

This paper presents a novel real-time signal processing system using for target tracking, it uses both ADSP21060 and FPGA to attain high performance and high-speed image processing. It analyses the flexible hardware architecture based on the idea of reconfigurability and modularization, and discusses three main modules and extensibility of the hardware system. Then the implementation of the image pre-processing and target intelligent tracking algorithm is discussed in detail. The experimental result shows that the system processing rate reaches 30 frames per second. The system can automatically detect targets and output the tracking information in real-time.

关键词： Real time systems Signal processing Target tracking Field programmable gate arrays Digital signal processing Hardware Logic arrays Signal processing algorithms image processing image analysis

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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)

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

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 multi resolution 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.

关键词： Focusing image sequences Support vector machines Layout Laplace equations image edge detection image segmentation Optical sensors Lenses image fusion

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