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International Conference on Natural Computation (ICNC)

作者： Baixian Zou Jun Miao Xiaoling Yang Lijuan Duan Yuanhua Qiao Department of Information Science and Technology College of Arts and Science of Beijing Union University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Tianjin Academy of Agricultural Science Tianjin China College of Computer Science and Technology Beijing Beijing China College of Applied Science Beijing University of Technology Beijing China

Sparse coding has high-performance encoding and ability to express images, sparse encoding basis vector plays a crucial role. The computational complexity of the most existing sparse coding basis vectors of is relatively large. In order to reduce the computational complexity and save the time to train basis vectors. A new Hebbian rules based method for computation of sparse coding basis vectors is proposed in this paper. A two-layer neural network is constructed to implement the task. The main idea of our work is to learn basis vectors by removing the redundancy of all initial vectors using Hebbian rules. The experiments on natural images prove that the proposed method is effective for sparse coding basis learning. It has the smaller computational complexity compared with the previous work.

关键词： Computer science Stability Space technology Collaboration Mathematical model Euclidean distance Extraterrestrial measurements Particle measurements Fitting Game theory

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Fea-Accu cascade for face detection

Fea-Accu cascade for face detection

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IEEE International Conference on Image Processing

作者： Shengye Yan Shiguang Shan Xilin Chen Wen Gao Chinese Academy and Sciences Beijing China Institute of Computing Technology Digital Media Research Center Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China School of EE & CS Peking University Beijing China

Aiming at unloading the high training time burden of the popular cascaded classifier, in this paper, a novel cascade structure called Fea-Accu cascade is proposed. In Fea-Accu cascade training, the times of feature selection are largely reduced by enhancing the correlation among different stage classifiers of the cascaded classifier. In detail, for each stage classifier, before selecting new features out, the features selected out by previous stage classifiers are reused through creating new corresponding weak classifiers. To verify the efficiency and effectiveness of the proposed method, experiment is designed on frontal face detection problem. The experimental results show that it can largely reduce the training time. A frontal face detector with state-of-the-art classification performance can be learned in less than 10 hours.

关键词： Face detection Detectors Content addressable storage Design methodology information processing Computer vision Cameras Robustness Boosting Computer architecture

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The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex

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Science China(Life Sciences) 2008年第10期51卷 902-912页

作者： HU Hong1, LI Su2, WANG YunJiu2, QI XiangLin2 & SHI ZhongZhi1 1 key laboratory of intelligent information Processing, Institute of computing technology, Chinese Academy of Sciences, Beijing 100080, China 2 laboratory of Visual information Processing, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Laboratory of Visual Information Processing Institute of Biophysics Chinese Academy of Sciences Beijing China

Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

关键词： nonlinear dynamical system fuzzy logic visual cortex

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Data-deficiency-tolerated method for viewpoint independent sign language recognition

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Jisuanji Xuebao/Chinese Journal of Computers 2009年第5期32卷 953-961页

作者： Wang, Qi Chen, Xi-Lin Wang, Chun-Li Gao, Wen School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Information Science and Technology Dalian Maritime University Dalian 116026 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

This paper proposes a Sample-Consensus method for viewpoint independent sign language recognition under data deficiency (matched features are possibly deficient with regard to some frame pairs). The proposed method is based on the epipolar geometry and inspired by RANSAC. The basic idea is that all corresponded frames between two sequences of the same sign can be roughly considered as captured synchronously by a virtual stereo vision system and thus they will satisfy the same fundamental matrix. In addition, the fundamental matrix can be estimated from point correspondences contained by some part of corresponding frames. Experimental results demonstrate the efficiency of the proposed method. Moreover, this Sample-Consensus method can be easily extended to some similar problems, such as viewpoint independent activity analysis and rigid-motion analysis.

关键词： Stereo image processing

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A computational model for feature binding

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Science China(Life Sciences) 2008年第5期51卷 470-478页

作者： SHI ZhiWei1,2, SHI ZhongZhi1, LIU Xi1,2 & SHI ZhiPing1 1 key laboratory of intelligent information Processing, Institute of computing technology, Chinese Academy of Sciences, Beijing 100190, China 2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

The "Binding Problem" is an important problem across many disciplines, including psychology, neuroscience, computational modeling, and even philosophy. In this work, we proposed a novel computational model, Bayesian Linking Field Model, for feature binding in visual perception, by combining the idea of noisy neuron model, Bayesian method, Linking Field Network and competitive mechanism. Simulation Experiments demonstrated that our model perfectly fulfilled the task of feature binding in visual perception and provided us some enlightening idea for future research.

关键词： Binding Problem computational model Bayesian Linking Field Model

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Personalized multimedia web summarizer for tourist 08

Personalized multimedia web summarizer for tourist

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17th International Conference on World Wide Web 2008, WWW'08

作者： Wu, Xiao Li, Jintao Zhang, Yongdong Tang, Sheng Neo, Shi-Yong Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Department of Computer Science National University of Singapore Singapore

ISBN: (纸本)9781605580852

In this paper, we highlight the use of multimedia technology in generating intrinsic summaries of tourism related information. The system utilizes an automated process to gather, filter and classify information on various tourist spots on the Web. The end result present to the user is a personalized multimedia summary generated with respect to users queries filled with text, image, video and real-time news made retrievable for mobile devices. Preliminary experiments demonstrate the superiority of our presentation scheme to traditional methods.

关键词： Automation

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Robust appearance-based method for head pose estimation

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Ruan Jian Xue Bao/Journal of Software 2009年第6期20卷 1651-1663页

作者： Ma, Bing-Peng Shan, Shi-Guang Chen, Xi-Lin Gao, Wen Key Laboratory of Intelligent Information Processing Chinese Acad. of Sci. Beijing 100190 China Institute of Computing Technology Chinese Acad. of Sci. Beijing 100190 China Graduate University Chinese Acad. of Sci. Beijing 100049 China School of Electronic Engineering and Computer Science Peking University Beijing 100871 China

This paper proposes a new pose estimation method based on the appearance of 2D head image. First, the 1D Gabor filters are used to extract the features on the raw images. Compared with the traditional 2D Gabor represents, the 1D Gabor represents are more closely related to the head pose, while the advantages of computation and storage are obvious. Second, for the extracted features, a new method, named kernel local fisher discriminant analysis, is applied to eliminate the multimodal problem, while at the same time enhance the discrimination ability. Experimental results show that the proposed method is effective for pose estimation. It must be pointed out that the generalizability of the proposed method is illustrated by the impressive performance when the training dataset and the testing dataset are heterogeneous. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Gabor filters

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An Automatic Visual Detecting Method for Semantic Object in Video

An Automatic Visual Detecting Method for Semantic Object in ...

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3rd International Conference on Pervasive computing and Applications

作者： Li Zongmin Li Deshan Li Hua Lin Zongkai School ofComputer and Communication Engineering China University of Petroleum Shandong China Key Laboratory Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424420209

In this paper, we propose an approach to automatic detection of semantic object. The method provides an effective content expression pattern for semantic analysis and retrieval of video. In the moving semantic object detection model, motion contrast is computed based on the planar motion (homography) between frames, which is estimated by applying RANSAC algorithm on point correspondences in the scene. In the semantic object detection model of static frame, the three features used are intensity, color and texture. Then a dynamic fusion technique is applied to combine these models. The automatic detection method can greatly decrease computation and be used in pervasive computing environment conveniently. Experimental results verify efficiency of proposed approach.

关键词： automatic detection semantic object saliency map pervasive computing

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The ICT System Description for IWSLT 2008 5

The ICT System Description for IWSLT 2008

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5th International Workshop on Spoken Language Translation, IWSLT 2008

作者： Liu, Yang He, Zhongjun Mi, Haitao Huang, Yun Feng, Yang Jiang, Wenbin Lu, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

This paper presents a description for the ICT systems involved in the IWSLT 2008 evaluation campaign. This year, we participated in Chinese-English and English-Chinese translation directions. Four statistical machine translation systems were used: one linguistically syntax-based, two formally syntax-based, and one phrase-based. The outputs of the four SMT systems were fed to a sentence-level system combiner, which was expected to produce better translations than single systems. We will report the results of the four single systems and the combiner on both the development and test sets. © 2008 International Workshop on Spoken Language Translation, IWSLT 2008. All rights reserved.

关键词： Syntactics

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FEATURE TRANSFORMATION FOR EFFICIENTLY IMPROVING PERFORMANCE OF HSC

FEATURE TRANSFORMATION FOR EFFICIENTLY IMPROVING PERFORMANCE...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： FU-ZHEN ZHUANG QING HE ZHONG-ZHI SHI The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese

Hyper Surface Classification (HSC) is a novel classification method based on hyper surface which is put forward by Qing He, etc. Experiments show that HSC can efficiently and accurately classify large-size data in two dimensional space and three-dimensional space. Actually, it is difficult to deal with high dimensional data for HSC. So the dimension reduction (data rearrangement) and ensemble methods (feature subspace) are proposed for HSC. But the method based on ensemble will produce many inconsistent and repetitious data in some density dataset, which influence the classification ability of HSC. To solve the problem, a simple and effective kind of data feature transformation method for enhancing performance of HSC is proposed in this paper. The experimental results show that this method can efficiently reduce the inconsistent and repetitious data, efficiently utilize the data information, and remarkably improve the classification performance of HSC.

关键词： Hyper Surface Classification Ensemble Classification Performance Feature Transformation.

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