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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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An Improvement to Genetic Algorithms for Multimodal Optimization in Noisy Environments: Re-evaluation of All Individuals per Generation

An Improvement to Genetic Algorithms for Multimodal Optimiza...

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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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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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Salient object detection via color contrast and color distribution

Salient object detection via color contrast and color distri...

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11th Asian Conference on Computer Vision, ACCV 2012

作者： Fu, Keren Gong, Chen Yang, Jie Zhou, Yue Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai 200240 China

ISBN: (纸本)9783642373305

In this paper, we take the advantages of color contrast and color distribution to get high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contrast and color distribution computation, combination, final refinement and then object segmentation. During color contrast saliency computation, we combine two color systems and then introduce the using of distribution prior before saliency smoothing. It works to select correct color components. In addition, we propose a novel saliency smoothing procedure that is based on superpixel regions and is realized in color space. This processing step leads to total object being highlighted evenly, contributing to high quality color contrast saliency maps. Finally, a new refinement approach is utilized to eliminate artifacts and recover unconnected parts in the combined saliency maps. In visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutters. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improvement from the current highest), when evaluated via one of the most popular data sets [1]. Excellent content-aware image resizing also can be achieved with our saliency maps. © 2013 Springer-Verlag.

关键词： Superpixels

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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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Optimal classification of epileptic EEG signals using neural networks and harmony search methods

Journal of Software

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Journal of Software 2014年第1期9卷 230-239页

作者： Gao, Xiao-Zhi Wang, Jing Tanskanen, Jarno M. A. Bie, Rongfang Wang, Xiaolei Guo, Ping Zenger, Kai College of Information Engineering Shanghai Maritime University China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Laboratory of Image Processing and Pattern Recognition Beijing Normal University China Department of Biomedical Engineering Tampere University of Technology Finland School of Foundational Education Peking University Health Science Center China

In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training. © 2014 ACADEMY PUBLISHER.

关键词： Optimization

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3D model retrieval using density-based silhouette descriptor

3D model retrieval using density-based silhouette descriptor

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2nd International Conference on pattern recognition Applications and Methods, ICPRAM 2013

作者： Tang, Qi Yang, Xin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

ISBN: (纸本)9789898565419

In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new descriptor is computationally efficient and induces a permutation property that guarantees invariance at the matching stage. Also, it is insensitive to small shape perturbations and mesh resolution. The retrieval performance on several 3D databases shows that the DBS provides state-of-art discrimination over a broad and heterogeneous set of shape categories.

关键词： Probability density function

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Multiple kernel learning method using MRMR criterion and kernel alignment

Multiple kernel learning method using MRMR criterion and ker...

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20th International Conference on Neural Information processing, ICONIP 2013

作者： Wu, Peng Duan, Fuqing Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan 250022 China

ISBN: (纸本)9783642420535

Multiple kernel learning (MKL) is a widely used kernel learning method, but how to select kernel is lack of theoretical guidance. The performance of MKL is depend on the users' experience, which is difficult to choose the proper kernels in practical applications. In this paper, we propose a MKL method based on minimal redundant maximal relevance criterion and kernel alignment. The main feature of this method compared to others in the literature is that the selection of kernels is considered as a feature selection issue in the Hilbert space, and can obtain a set of base kernels with the highest relevance to the target task and the minimal redundancies among themselves. Experimental results on several benchmark classification data sets show that our proposed method can enhance the performance of MKL. © Springer-Verlag 2013.

关键词： Benchmarking

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