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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Qiao Huang Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Tracking the same person across multiple cameras is an important task in multi-camera systems. It is also desirable to re-identify the individuals who have been previously seen with a single-camera. This paper addresses this problem by the re-identification of the same individual in two different datasets, which are both challenging situations from video surveillance system. In this paper, local descriptors are introduced for image description, and support vector machines are employed for high classification performance and so an efficient Bag of Features approach for image presentation. In this way, robustness against low resolution, occlusion and pose, viewpoint and illumination changes is achieved in a very fast way. We get promising results from the evaluation with situations where a number of individuals vary continuously from a multi-camera system.

关键词： Vocabulary Cameras Histograms Feature extraction image color analysis Support vector machine classification Kernel

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Multiobjective evolutionary algorithm-based soft subspace clustering

Multiobjective evolutionary algorithm-based soft subspace cl...

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Congress on Evolutionary Computation

作者： Lin Zhu Longbing Cao Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Advanced Analytics Institute Faculty of Engineering and IT University of Technology Sydney Sydney Australia

ISBN: (纸本)9781467315104

In this paper, a multiobjective evolutionary algorithm based soft subspace clustering, MOSSC, is proposed to simultaneously optimize the weighting within-cluster compactness and weighting between-cluster separation incorporated within two different clustering validity criteria. The main advantage of MOSSC lies in the fact that it effectively integrates the merits of soft subspace clustering and the good properties of the multiobjective optimization-based approach for fuzzy clustering. This makes it possible to avoid trapping in local minima and thus obtain more stable clustering results. Substantial experimental results on both synthetic and real data sets demonstrate that MOSSC is generally effective in subspace clustering and can achieve superior performance over existing state-of-the-art soft subspace clustering algorithms.

关键词： Clustering algorithms Optimization Indexes Biological cells Evolutionary computation Entropy Partitioning algorithms

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Detection of Billet Character in Complex Production Line Scene

Detection of Billet Character in Complex Production Line Sce...

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International Conference on Digital Manufacturing and Automation (ICDMA)

作者： Hong Hanyu Yu Zhejun Zhang Xiuhua Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Laboratory for Image Processing and Intelligent Control Wuhan Institute of Technology Wuhan China

Character information is hard to detect in billet scene images by CCD camera. In this paper, we present a method for detection of billet characters from measurements of recursive segmented image. This recursive segmented method can be used in a wide variety of billet scenes. According to high temperature and complex scene in the rolling line, we use an effective clustering and projection characteristics to determine the terminal condition of recursive segmentation. Then we can label character candidate regions in turn by this effective characteristics, and select the regions we want to achieve. The experiments show that this method makes full use of the characteristics of region and clustering. It can improve the quality of detection, and the detection result meets the need of practical application.

关键词： Billets image segmentation Steel Arrays Filtering Licenses

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Integrating Segmentation and Classification Accuracy for Accuracy Assessment in Object-based image Analysis

Integrating Segmentation and Classification Accuracy for Acc...

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International Conference on Audio, Language and image processing

作者： Nan Li Hong Huo Tao Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education Institute of Image Processing and PatternRecognition Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781467301732

An accuracy assessment method that integrates segmentation and classification accuracy is proposed to meet the requirements of object-based image analysis. Segmentation errors are measured by establishing the relationship between pixels and their corresponding segments according to the overlaps of segments and reference polygons. Then, two improved confusion matrices that take the segmentation errors into consideration are used: one for pixel-level classification results, and the other for object-level classification results. A final accuracy assessment combines the statistics of these two confusion matrices. The proposed method can be applied to segmentation scale selection in the hierarchical interpretation system. An experiment on a SPOT5 image demonstrates the effectiveness of this method for segmentation scale selection, which can guide the fusion of objects of different scales to obtain a higher accuracy.

关键词： segmentation image analysis Segmentation object orientation confusion matrix pixel

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The Remote Sensing image Enhancement Algorithm Based on NSCT and Grayscale Morphology

The Remote Sensing Image Enhancement Algorithm Based on NSCT...

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2012 4th International Conference on Environmental Science and Information Application Technology(ESIAT 2012)

作者： Xiaoting Pu Zhenhong Jia Liejun Wang Jie Yang Raphael Hu College of Information Science and Engineering Xinjiang University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Knowledge Engineering and Discovery Research Institute Auckland University of Technology

In order to restrain pseudo-Gibbs phenomenon, low contrast and blurred phenomenon in the process of image enhancement, a new method based on the Nonsubsampled Contourlet Transform (NSCT) and Grayscale Morphology is proposed in this paper. The proposed method utilizes the shift-invariance of NSCT to restrain the pseudo-Gibbs phenomenon. The results obtained with the proposed method are superior to histogram equalization and contourlet method in detail and vision of the image.

关键词： Remote Sensing image image Enhancement Nonsubsampled Contourlet Transform Unsharp Masking

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image Denoising Based on NSCT Domain with an Optimal Threshold and Neighbouring Window

Image Denoising Based on NSCT Domain with an Optimal Thresho...

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2012 4th International Conference on Environmental Science and Information Application Technology(ESIAT 2012)

作者： Juan Yang Zhenhong Jia Xizhong Qin Jie Yang Yingjie Hu College of Information Science and Engineering Xinjiang University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Knowledge Engineering and Discovery Research Institute Auckland University of Technology

By considering the advantage of Nonsubsampled Contourlet Transform(NSCT),while Commonly used NeighShrink algorithm uses a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper,A novel image denoising algorithm based on an improved method, which can determine an optimal threshold and neighbouring window size for every NSCT subband by the Stein's unbiased risk estimate (SURE).The proposed method can effectively reduce Gaussian noise in remote sensing image and improve the image of the peak signal-to-noise ratio,This method utilizes the redundant and translation invariant of NSCT transform to inhibit the effect of pseudo Gibbs, and preserves the image texture and edge detail informations, thus obviously ameliorate the visual effect of the image.

关键词： image denoising, redundant, Nonsubsampled Contourlet Transform, adaptive, Neighbourhood

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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 IEEE.

关键词： Iterative methods

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An analysis of relationship of watermarking decoding error bit rate and payload capacity

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Journal of Computational Information Systems 2012年第20期8卷 8431-8438页

作者： Zhang, Xinhong Zhang, Fan Li, Duoyi Li, Congcong Computing Center Henan University Kaifeng 475001 China Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475001 China Department of Electronic Science and Engineering Nanjing University Nanjing 210008 China

Capacity and bit error rate (EBR) are two important properties of digital image watermarking. An analysis about the relationship of watermarking decoding error bit rate and payload capacity is proposed in this paper. Two watermarking communication models are established for the error bit rate analysis of digital image watermarking. In the case of channel coding is used, the EBR of watermarking depends on the channel decoding EBR and the channel transmission EBR. The EBR of watermarking will drop with the decrease of watermark payload capacity. When the payload capacity is less than the channel capacity, the EBR will maintain a lower lever. © 2012 Binary Information Press.

关键词： Bit error rate

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One-Class SVM assisted accurate tracking

One-Class SVM assisted accurate tracking

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Keren Fu Chen Gong Yu Qiao Jie Yang Irene Guy Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Chalmers tekniska hogskola Goteborg SE

Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. In this paper, we argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by One-Class SVM is bounded by a closed sphere, we propose a novel tracking method utilizing One-Class SVMs that adopt HOG and 2bit-BP as features, called One-Class SVM Tracker (OCST). Simultaneously an efficient initialization and online updating scheme is also proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods on providing accurate tracking and alleviating serious drifting.

关键词： Target tracking Support vector machines Training Feature extraction Face Boosting Semisupervised learning

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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International Conference on Systems, Signals and image processing, IWSSIP

作者： Gayane Shalunts Yll Haxhimusa Robert Sablatnig Institute of Computer Aided Automation Computer Vision Laboratory University of Technology Vienna Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab University of Technology Vienna Austria

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate.

关键词： Shape Three dimensional displays Visualization Computer vision Histograms Windows

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