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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Junhua Li Peng Liu Linxia Zhou Ming Li Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hang Kong University Nanchang China School of Information Engineering Nanchang Hang Kong University Nanchang China

ISBN: (纸本)9781479969302

Optimization in noisy environments is regard as a favorite application domains of genetic algorithms. Different methods for reducing the influence of noise are presented and discussed. A new fitness evaluation method is proposed that reevaluates all survival individuals each generation. Compared with re-sampling and population sizing, the new evaluation approach shows higher probability of searching to the global extremum area and precision of convergence. These results demonstrate that the proposed method is effective for reducing noise effects.

关键词： Noise Noise measurement Genetic algorithms Optimization Sociology Statistics Convergence

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An efficient and scalable learning algorithm for Near-Earth objects detection in astronomy big image data

An efficient and scalable learning algorithm for Near-Earth ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ke Wang Ping Guo School of computer Science and Technology Beijing Institute of Technology Beijing P.R. China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing P.R. China

ISBN: (纸本)9781479938414

In this paper, we investigate the efficiency and scalability of Gaussian mixture model based learning algorithm for the detection of Near-Earth objects in large scale astronomy image data. We propose an effective scheme to reduce the computational complexity of current learning algorithm, this is achieved by adopting the perceptual image hashing method. Our proposed scheme is validated on raw astronomy image data. The experiment results illustrate that both efficiency and scalability are improved significantly in astronomical scenario and other scenario.

关键词： Astronomy Scalability Big data Data mining Gaussian distribution Hamming distance Gaussian mixture model

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A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

A Probabilistic Framework for Multitarget Tracking with Mutu...

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IEEE Conference on Computer Vision and pattern recognition

作者： Menglong Yang Yiguang Liu Longyin Wen Zhisheng You Stan Z. Li Key Laboratory of Fundamental Synthetic Vision Graphics and Image for National Defense School of Aeronautics and Astronautics & Computer Science Sichuan University Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479951192

Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target. This paper presents a novel probability framework for multitarget tracking with mutual occlusions. The primary contribution of this work is the introduction of a vectorial occlusion variable as part of the solution. The occlusion variable describes occlusion states of the targets. This forms the basis of the proposed probability framework, with the following further contributions: 1) Likelihood: A new observation likelihood model is presented, in which the likelihood of an occluded target is computed by referring to both of the occluded and occluding targets. 2) Priori: Markov random field (MRF) is used to model the occlusion priori such that less likely "circular" or "cascading" types of occlusions have lower priori probabilities. Both the occlusion priori and the motion priori take into consideration the state of occlusion. 3) Optimization: A realtime RJMCMC-based algorithm with a new move type called "occlusion state update" ispresented. Experimental results show that the proposed framework can handle occlusions well, even including long-duration full occlusions, which may cause tracking failures in the traditional methods.

关键词： Target tracking Probabilistic logic Approximation algorithms Cameras Proposals Computational modeling

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Shadow detection using regression tree fields with paired regions

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Journal of Computational Information Systems 2013年第11期9卷 4309-4317页

作者： Tao, Wei Zhou, Yue Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai 200240 China

In this paper, we try to deal with the problem of shadow detection from static images and video sequences. In instead to considering individual regions separately, we use relative illumination conditions between segmented regions and perform pair-wise classification based on such information. We use Regression Tree Fields to solve the labeling of shadow and non-shadow regions. And we use continuous value to describe the possibility of the shadow of the region. We evaluate our method on static images and a series of video sequences. © 2013 by Binary Information Press.

关键词： Classification (of information)

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A robust CPD approach based on shape context

A robust CPD approach based on shape context

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Chinese Control Conference (CCC)

作者： Yawen Yang Zhang Peng Peng Yu Qiao Jie Yang Sheng Zheng Wang The Key Laboratory of Ministry of Education for System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai CN Merchant Marine College Shanghai Maritime University Shanghai CN

Point matching is an important component of image registration. Recent years, Coherent Point Drift (CPD) method becomes a very popular point matching approach. CPD treats point matching as a probability estimation problem and speeds up the process of matching a lot. In this method, one set of points are thought to be sampled from a Gaussian Mixture Model (GMM), which is centered by the other set of points. However, CPD is sensitive to outliers and noises, especially when the noise ratio increased or the number of outliers gets much high. To deal with this problem, we introduce shape context into the step of searching for matching points and then improve the form of prior probabilities of GMM in this paper. The main idea of our method is that if the most points in a data set are likely to be matched to a particular centroid, this Gaussian component should be have a more influence to GMM. Therefore, we set prior probability of GMM with the similarity between GMM components and the data set. And the computation of similarity is based on shape context. The experiments on 2D and 3D images show that when noise ratio is low, our method performs as well as CPD does, but as the ratio increased, our method is more robust and satisfactory than CPD.

关键词： Noise Shape Testing Context Robustness Three-dimensional displays Marine animals

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Rendering scenes with participating media based on RBFs for photon mapping using graphics hardware

Rendering scenes with participating media based on RBFs for ...

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12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry, VRCAI 2013

作者： Wang, Yuanlong Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781450325905

The photon mapping is one of the more widely used algorithms for rendering scenes with participating media. Currently, it suffers from two main problems: one is how to improve the rendering efficiency, and another is how to reduce large memory requirement to store the photons. In this paper, we propose a hardware-accelerated algorithm that is based on radial basis function model for rendering participating media with multiple scattering. The radial basis function model is used to describe the photon distribution in participating media, and the main purpose is to provide high reconstruction quality using a relatively low number of basis function terms. Consequently, it can result in faster rendering. The radiance estimation is designed according to the radial basis function model. The implementation demonstrates that proposed method is the attractive low memory representation and it is capable of fully simulating the scattering of participating media with interactive feedback to the user. © 2013 ACM.

关键词： Radial basis function networks

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Global matching to enhance the strength of local intensity order pattern feature descriptor

Global matching to enhance the strength of local intensity o...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Dawood, Hassan Dawood, Hussain Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9783642390647

Local intensity order pattern feature descriptor is proposed to extract the feature of image recently. However, it did not provide the global information of an image. In this paper, a simple, efficient and robust feature descriptor is presented, which is realized by adding the global information to local intensity features. A descriptor, which utilizes local intensity order pattern and/or global matching, is proposed to gather the global information with local intensity order. Experimental results shows that the proposed hybrid approach outperform over the state-of-the art feature extraction method like scale-invariant feature transform, local intensity order pattern and DAISY for standard oxford dataset. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Feature extraction

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Classification of COPD with Multiple Instance Learning

Classification of COPD with Multiple Instance Learning

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International Conference on pattern recognition

作者： Veronika Cheplygina Lauge Sørensen David M.J. Tax Jesper Holst Pedersen Marco Loog Marleen de Bruijne Pattern Recognition Laboratory Delft University of Technology Delft The Netherlands The Image Group University of Copenhagen Copenhagen Denmark Department of Thoracic Surgery University of Copenhagen Copenhagen Denmark Technische Universiteit Delft Delft Zuid-Holland NL

Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate. COPD can be quantified by classifying patches of computed tomography images, and combining patch labels into an overall diagnosis for the image. As labeled patches are often not available, image labels are propagated to the patches, incorrectly labeling healthy patches in COPD patients as being affected by the disease. We approach quantification of COPD from lung images as a multiple instance learning (MIL) problem, which is more suitable for such weakly labeled data. We investigate various MIL assumptions in the context of COPD and show that although a concept region with COPD-related disease patterns is present, considering the whole distribution of lung tissue patches improves the performance. The best method is based on averaging instances and obtains an AUC of 0.742, which is higher than the previously reported best of 0.713 on the same dataset. Using the full training set further increases performance to 0.776, which is significantly higher (DeLong test) than previous results.

关键词： Lungs Support vector machines Diseases Training Kernel Noise measurement Prototypes

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An image compression-encryption algorithm based on 2-D compressive sensing

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Journal of Computational Information Systems 2013年第24期9卷 10057-10064页

作者： Mo, Yan Zhang, Aidi Zheng, Fen Zhou, Nanrun Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China Department of Electronic Information Engineering Nanchang University Nanchang 330031 China

An image compression-encryption algorithm based on 2-D compressive sensing is proposed, which can accomplish encryption and compression simultaneously. The measurements are performed in two directi-ons and the measurement matrices are constructed as partial Hadamard matrices, which are controlled by chaos map. The proposed algorithm is sensitive to the keys and it can resist various attacks. Simulation results verify the validity and reliability of the proposed algorithm. Copyright © 2013 Binary Information Press.

关键词： Compressed sensing

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Kernel based weighted group sparse representation classifier

Kernel based weighted group sparse representation classifier

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15th International Conference on Human-Computer Interaction, HCI International 2013

作者： Xu, Bingxin Guo, Ping Chen, C. L. Philip Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Faculty of Science and Technology University of Macau China

ISBN: (纸本)9783642393419

Sparse representation classification (SRC) is a new framework for classification and has been successfully applied to face recognition. However, SRC can not well classify the data when they are in the overlap feature space. In addition, SRC treats different samples equally and ignores the cooperation among samples belong to the same class. In this paper, a kernel based weighted group sparse classifier (KWGSC) is proposed. Kernel trick is not only used for mapping the original feature space into a high dimensional feature space, but also as a measure to select members of each group. The weight reflects the importance degree of training samples in different group. Substantial experiments on benchmark databases have been conducted to investigate the performance of proposed method in image classification. The experimental results demonstrate that the proposed KWGSC approach has a higher classification accuracy than that of SRC and other modified sparse representation classification. © 2013 Springer-Verlag.

关键词： Classification (of information)

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