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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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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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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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A Smart Mathematic Morphology Method for recognition and Understanding of Airfield

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Wuhan University Journal of Natural Sciences 2005年第5期10卷 867-872页

作者： WANG Zhi-cheng TIAN Yan TIAN Jin-wen LIU Jian LIANG Chao-sheng TAO Wen-bin State Education Commission Key Laboratory for Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan430074 Hubei China Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 Hubei China

A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structure features of airfield, a segmentation method called recursive Otsu algorithm is employed on an airfield image. Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles. Finally, Radon transform is adopted to extract two typical and important components, primary and secondary runways of airfield exactly. At the same time, region growing algorithm is exploited to get the other components such as parking apron and garages. The experimental results demonstrate that the proposed method gives good performance.

关键词： mathematical morphology shrinking algorithm Radon transform Region growing

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Bidirectional Automated Branch and Bound Algorithm for Feature Selection

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Journal of Shanghai University(English Edition) 2005年第3期9卷 244-248页

作者：杨胜施鹏飞 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 P. R. China

Feature selection is a process where a minimal feature subset is selected from an original feature set according to a certain measure. In this paper, feature relevancy is defined by an inconsistency rate. A bidirectional automated branch and bound algorithm is presented. It is a new complete search algorithm for feature selection, which performs feature deletion and feature addition in parallel. Its bound is determined by inconsistency rate of the original feature set, hence termed as ‘automated’. Experimental study shows that it is fit for feature selection.

关键词： feature selection pattern classification data mining machine learning.

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Investigation on traditional and modern ceramic documentation

Investigation on traditional and modern ceramic documentatio...

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IEEE International Conference on image processing

作者： M. Kampel H. Mara R. Sablatnig Pattern Recognition and Image Processing Group Institute of Computer Aided Automation University of Technology Vienna Vienna Austria

Archaeology is at a point where it can benefit greatly from the application of computer vision methods, and in turn provides a large number of new, challenging and interesting conceptual problems and data for computer science. This is true in particular in the study of ceramics - the most abundant and widespread of all archaeological finds. The traditional way of documenting archaeological sherds is to draw the profile line, which is the intersection of a sherd along the axis of symmetry. A profilograph is a mechanical device, which can directly acquire and transfer a profile line by pin-pointing the profile on a sherd to a computer. We developed a fully automated vision system, which is able to compute the profile line out of the acquired 3D model of the fragment. In this paper we want to give a thorough comparison between the traditional manual approach, the profilograph and our system and present an improvement of the robustness of our approach by finding circular rills on the fragments. Practical experiments have been undertaken at the excavation Tel Dor in Israel.

关键词： Ceramics Documentation Computer vision Pins pattern recognition image processing Automation Application software Computer science Machine vision

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A two-stage level set evolution scheme for man-made objects detection in aerial images

A two-stage level set evolution scheme for man-made objects ...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Guo Cao Xin Yang Zhihong Mao Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai P.R. China

ISBN: (纸本)0769523722

A novel two-stage level set evolution method for detecting man-made objects in aerial images is described. The method is based on a modified Mumford-Shah model and it uses a two-stage curve evolution strategy to get a preferable detection. It applies fractal error metric, developed by Cooper, et al. (1994) at the first curve evolution stage and adds additional constraint texture edge descriptor that is defined by using DCT (discrete cosine transform) coefficients on the image at the next stage. Man-made objects and natural areas are optimally differentiated by evolving the partial differential equation. The method artfully avoids selecting a threshold to separate the fractal error image, while an improper threshold often results in great segmentation errors. Experiments of the segmentation show that the proposed method is efficient.

关键词： Level set Object detection Hidden Markov models image segmentation Fractals Discrete cosine transforms pattern recognition Roads Layout Parameter estimation

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Efficient VLSI architecture for multi-dimensional discrete wavelet transform

Efficient VLSI architecture for multi-dimensional discrete w...

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MIPPR 2005: SAR and Multispectral image processing

作者： Xiong, Cheng-Yi Tian, Jin-Wen Liu, Jian College of Electronic Information Engineering South-Center University for Nationalities Wuhan 430074 China Institute of Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

Efficient VLSI architectures for multi-dimensional (m-D) discrete wavelet transform (DWT), e.g. m=2, 3, are presented, in which the lifting scheme of DWT is used to reduce efficiently hardware complexity. The parallelism of 2 m subbands transforms in lifting-based m-D DWT is explored, which increases efficiently the throughput rate of separable m-D DWT. The proposed architecture is composed of m2m-1 1-D DWT modules working in parallel and pipelined, which is designed to process 2m input samples per clock cycle, and generate 2m subbands coefficients synchronously. The total time of computing one level of decomposition for a 2-D image (3-D image sequence) of size N2 (MN2) is approximately N2/4 (MN2/8) intra- clock cycles (ccs). An efficient line-based architecture framework for both 2D+t and t+2D 3-D DWT is first proposed. Compared with the similar works reported in previous literature, the proposed architecture has good performance in terms of production of computation time and hardware cost. The proposed architecture is simple, regular, scalable and well suited for VLSI implementation.

关键词： VLSI circuits

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Evaluation of motion segmentation quality for aircraft activity surveillance

Evaluation of motion segmentation quality for aircraft activ...

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2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS

作者： Aguilera, Josep Wildenauer, Horst Kampel, Martin Borg, Mark Thirde, David Ferryman, James Pattern Recognition and Image Processing Group Vienna University of Technology Favoritenstr.9 183-2 A-1040 Vienna Austria Computational Vision Group University of Reading Whiteknights Reading RG6 6AY United Kingdom

ISBN: (纸本)0780394240

Recent interest has been shown in performance evaluation of visual surveillance systems. The main purpose of performance evaluation on computer vision systems is the statistical testing and tuning in order to improve algorithm's reliability and robustness, In this paper we investigate the use of empirical discrepancy metrics for quantitative analysis of motion segmentation algorithms. We are concerned with the case of visual surveillance on an airport's apron, that is the area where aircraft s are parked and serviced by specialized ground support vehicles. Robust detection of individuals and vehicles is of major concern for the purpose of tracking objects and understanding the scene. In this paper, different discrepancy metrics for motion segmentation evaluation are illustrated and used to assess the performance of three motion segmentors on video sequences of an airport's apron. © 2005 IEEE.

关键词： image segmentation

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Error analysis and improvements of Spectral Angle Mapper (SAM) model

Error analysis and improvements of Spectral Angle Mapper (SA...

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MIPPR 2005: SAR and Multispectral image processing

作者： Du, Peijun Chen, Yunhao Fang, Tao Tang, Hong Department of RS and GISCi China University of Mining and Technology Xuzhou City Jiangsu Province 221008 China College of Resources Science Beijing Normal University Beijing 100875 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 China

Spectral Angle Mapper (SAM) model has got wide applications in hyperspectral Remote Sensing (RS) information processing. But Spectral Angle couldn't achieve satisfied performance in some cases because of its sensitivity to noises and uncertainty. Based on the analysis to traditional SAM algorithm, four types of errors and their impacts to spectral angle are investigated. In order to reduce the impacts of above errors, some improved algorithms are proposed and experimented. The first improved algorithm is grouping spectral angle algorithm. In this new algorithm all bands are divided into two sets by odd and even bands, that means two additional sub-vectors are created in addition to the original spectral vector. So three spectral angles will be computed and the minimum of three indexes is used as final index. The second improved algorithm is normalized spectral angle. In this way spectral angle is computed to the normalized vectors of two original vectors. Two approaches are used to normalize the spectral vector, and spectral angle is computed to the normalized vectors. This algorithm is able to decrease the impacts of random errors. The third algorithm is intersected spectral angle. Spectral angle is calculated by a spectral displacement strategy in this approach. That means a given displacement to change the corresponding bands of two spectral vectors is used and a spectral angle to the displaced vectors will be got. By this displacement strategy the impacts of band offset is reduced. Finally some experiments are used to test those improved algorithms. It proves that those new approaches can reduce and control the errors and improve the precision and reliability of similarity measure.

关键词： Remote sensing

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