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IEEE International Conference on Image processing

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

Today's camera sensors usually have a high gray-scale resolution, e.g. 256, however, due to the dramatic lighting variations, the gray-scales distributed to the face region might be far less than 256. Therefore, besides low spatial resolution, a practical face recognition system must also handle degraded face images of low gray-scale resolution (LGR). In the last decade, low spatial resolution problem has been studied prevalently, but LGR problem was rarely studied. Aiming at robust face recognition, this paper makes a first primary attempt to investigate explicitly the LGR problem and empirically reveals that LGR indeed degrades face recognition method significantly. Possible solutions to the problem are discussed and grouped into three categories: gray-scale resolution invariant features, gray-scale degradation modeling and Gray-scale Super-Resolution (GSR). Then, we propose a Coupled Subspace Analysis (CSA) based GSR method to recover the high gray-scale resolution image from a single input LGR image. Extensive experiments on FERET and CMU-PIE face databases show that the proposed method can not only dramatically increase the gray-scale resolution and visualization quality, but also impressively improve the accuracy of face recognition.

关键词： Image processing Conferences

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Topic level sampling towards optimized locality sensitive vocabulary coding

Topic level sampling towards optimized locality sensitive vo...

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International Conference on Information, Communications and Signal processing

作者： Jie Chen Ling-Yu Duan Bing Li Rongrong Ji Wen Gao Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781457700293

We propose a novel locality sensitive vocabulary coding scheme to extract compact descriptors for low bit rate visual search. We employ Latent Dirichlet Allocation (LDA) to learn the topic vocabularies of lower dimension to generate compact descriptors. To deal with diverse datasets, LDA model is introduced to subdivide a dataset into groups of images with a topic model, where the code word distributions in each group produce coherent statistics from generative learning. Moreover, our empirical study has shown that the original Bag-of-Word (BoW) is sparse, and the occurrences of non-zero words is coherent within a topic. Our proposed topic-wise vocabulary learning yields a more compact yet discriminative codebook to search images. Given a query image, multiple topics are determined, which is fed into the topic vocabularies to generate more compact topical descriptor. Comparison experiments show our topic-wise locality sensitive vocabulary coding produces more compact and discriminative descriptors than the state-of-the-arts.

关键词： Vocabulary Visualization Encoding Mobile communication Image coding Histograms Bit rate

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Coarse-to-fine video text detection

Coarse-to-fine video text detection

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Guangyi Miao Qingming Huang Shuqiang Jiang Wen Gao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

In this paper, we propose an effective coarse-to-fine algorithm to detect text in video. Firstly, in coarse-detection section, stroke filter is employed to detect all candidate stroke pixels, and then a fast region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by projection operation. Secondly, in fine-detection section, correct text regions are selected from candidate ones by support vector machine (SVM) model and stroke features, and text regions in multi-resolution are integrated. Finally, the result is optimized significantly according to temporal correlation information. Experimental results show that our algorithm achieves real-time performance and is robust for the variation of language, font, size, color and noise of text caused by low frame resolution in video.

关键词： Pixel Support vector machines Feature extraction Classification algorithms Filtering algorithms Streaming media Correlation

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Maximizing intra-individual correlations for illumination-insensitive face recognition

Maximizing intra-individual correlations for illumination-in...

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IEEE International Conference on Image processing

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

Illumination variation has been one of the most intractable problems in face recognition and many approaches have been proposed to handle illumination problem in the last decades of years. The key problem is how to get stable similarity measurements between two face images of the same individual but captured under dramatically different lighting conditions. We propose a framework to optimize the illumination normalization for a pair of gallery and probe face images by maximizing a correlation (MAC) between them. The illumination normalization in the proposed framework tends to maximize the intra-individual correlations instead of both the inter- and intra-individual correlations. Experiments on Extended YaleB and CMU-PIE face databases show the effectiveness of our proposed approach in face recognition across varying lighting conditions.

关键词： Image processing Conferences

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China's grain security warning based on multifactor information fusion

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2011年第5期27卷 183-189页

作者： Su, Xiaoyan Zhang, Huijie Li, Zhiqiang Deng, Yong School of Electronics and Information Technology Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Digital Agricultural Early-warning Technology Ministry of Agriculture of China Beijing 100081 China College of Computer and Information Sciences Southwest University Chongqing 400715 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China

China is the largest food consumption country in the world. With the social and economic development, China's food security has become a global attention. Grain security research involves many uncertain factors: as well as quantitative and qualitative information. In order to get the grain security status comprehensively, we proposed a method to evaluate risk in grain security based on multifactor information fusion. In the method, the quantitative and qualitative information were used to construct the basic probability assignment, and the attribute weights was got based on the Analytic Hierarchy Process method. After that, the multifactor fusion results were got based on the Dempster combination rule. The effectiveness of the method was verified with a numeric example that the data comes from the yearbook of China in 2007. The Results show that the method is effective and can correctly reflect the grain safety warning degrees.

关键词： Analytic hierarchy process

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Maximizing intra-individual correlations for face recognition across pose differences

Maximizing intra-individual correlations for face recognitio...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Annan Li Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing of CAS Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

The variations of pose lead to significant performance decline in face recognition systems, which is a bottleneck in face recognition. A key problem is how to measure the similarity between two image vectors of unequal length that viewed from different pose. In this paper, we propose a novel approach for pose robust face recognition, in which the similarity is measured by correlations in a media subspace between different poses on patch level. The media subspace is constructed by canonical correlation analysis, such that the intra-individual correlations are maximized. Based on the media subspace two recognition approaches are developed. In the first, we transform non-frontal face into frontal for recognition. And in the second, we perform recognition in the media subspace with probabilistic modeling. The experimental results on FERET database demonstrate the efficiency of our approach.

关键词： Face recognition Shape Content addressable storage Robustness Ellipsoids Geometry Information processing Computers Length measurement Databases

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Boosted Sigma Set for Pedestrian Detection

Boosted Sigma Set for Pedestrian Detection

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International Conference on Pattern Recognition

作者： Xiaopeng Hong Hong Chang Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Institute of Digital Media Peking University China

ISBN: (纸本)9781424475421

This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discriminative power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the proposed method.

关键词： Classification algorithms Manifolds Detectors Feature extraction Training Covariance matrix Support vector machine classification

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Illumination transfer using homomorphic wavelet filtering and its application to light-insensitive face recognition

Illumination transfer using homomorphic wavelet filtering an...

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International Conference on Automatic Face and Gesture Recognition

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Key Laboratory of Intelligent Information Processing of CAS Institute of Computing Technology Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Beijing China Institute of Digital Media Peking University Beijing China

In this paper, we propose a novel homomorphic wavelet filtering based illumination transfer technique to change the dominant lighting of one face image (source face image) to another face image (reference face image ). Specifically, in the proposed method, based on the ldquoreflectance-illuminationrdquo imaging model, we first obtain an approximate estimate of the illumination component of the face image through a wavelet-based Multiresolution Analysis (MRA) in the logarithm domain of the input image. Then, a homomorphic filtering procedure is applied to improve the accuracy of the illumination component estimation. Finally, the source face image is re-lighted by substituting the estimated illumination component of the reference image for that of the source image. The proposed method is entirely an image processing based method without any 3D geometry modeling steps, so it is simple but effective. The method is also applied easily to illumination invariant face recognition by transferring a standard illumination to all the face images. Experimental results show that our method is quite effective for both illumination transfer and illumination-insensitive face recognition.

关键词： Lighting Filtering Face recognition Solid modeling Geometry Face detection Facial animation Multiresolution analysis Image processing digital filters

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Risk analysis of system security based on evidence theory

Risk analysis of system security based on evidence theory

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2010 International Conference on Computer Design and Applications, ICCDA 2010

作者： Xu, Peida Xu, Jianling Deng, Yong Su, Xiaoyan Key Laboratory of Digital Agricultural Early-warning Technology Ministry of Agriculture of China Beijing China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China School of Electronics and Information Technology Shanghai Jiao Tong University Shanghai China Grain Economics Institute Nanjing University of Finance and Economics Nanjing China Hangzhou Key Lab. of E-Business and Information Security Hangzhou Normal University Hanzhou Zhejiang China Hangzhou Key Lab. of E-Business and Information Security Hangzhou Normal University Shanghai China

ISBN: (纸本)9781424471638

Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In the proposed method, each individual component of the protection system in the simulated plane is modeled. Then, the threat report of each component according to the specific tactics is determined based on its real environment. Finally, the comprehensive threat distribution is obtained based on through the D-S evidence theory to combine multi-sources information. The proposed method can easily applied to the evaluation of the effectiveness of the protection system. We make the total threat of the protection system lowest through changes of the protection resources allocation. A numerical example is used to illustrate the efficiency of the proposed method. © 2010 IEEE.

关键词： Information fusion

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Adaptive generic learning for face recognition from a single sample per person

Adaptive generic learning for face recognition from a single...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yu Su Shiguang Shan Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Digital Media Research Center Institute of Computing Technology CAS China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China Institute for Digital Media Peking University China

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.

关键词： Face recognition Principal component analysis Linear discriminant analysis Computer science Content addressable storage Laboratories Deductive databases Information processing Scattering Image databases

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