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International Conference on Pattern Recognition

作者： Bineng Zhong Hongxun Yao Shiguang Shan Xilin Chen Wen Gao Department of Computer Science and Engineering Harbin Institute of Technology Digital Media Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Digital Media Institute Peking University Beijing China

ISBN: (纸本)9781424421749

We propose a robust hierarchical background subtraction technique which takes the spatial relations of neighboring pixels in a local region into account to detect objects in difficult conditions. Our algorithm combines a per-pixel with a per-region background model in a hierarchical manner, which accentuates the advantages of each. This is a natural combination because the two models have complementary strengths. The per-pixel background model is achieved by mixture of Gaussians models (GMM) with RGB feature. Although precisely describing background change in high resolution, it suffers from the sensitivity to quick variations in dynamic environment. To tolerate these quick variations, we further develop a novel GMM based per-region background model, which is updated by the cluster centers obtained from a k-means clustering of the pixelspsila RGB feature in the region. Numerical and qualitative experimental results on challenging videos demonstrate the robustness of the proposed method.

关键词： Autoregressive processes Robustness Object detection Videos Layout Smart pixels Content addressable storage Gaussian processes Lighting Cameras

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A hierarchical model for structure learning based on the physiological characteristics of neurons

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中国高等学校学术文摘·计算机科学 2007年第3期1卷 361-372页

作者： WEI Hui Department of Computer Science Laboratory of Algorithms for Cognitive ModelingLaboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory *** characteristics of typical ANN models are fixed connections,with evolved weights,globalized representations,and globalized optimizations,all based on a mathematical *** makes those models to be deficient in robustness,efficiency of learning,capacity,anti-jamming between training sets,and correlativity of samples,*** this paper,we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing.A hierarchical neural network was designed and realized to implement structure learning and representations based on connected *** basic characteristics of this model are localized and random connections,field limitations of neuron fan-in and fan-out,dynamic behavior of neurons,and samples represented through different sub-circuits of neurons specialized into different response *** the end of this paper,some important aspects of error correction,capacity,learning efficiency,and soundness of structural representation are analyzed *** paper has demonstrated the feasibility and advantages of structure learning and *** model can serve as a fundamental element of cognitive systems such as perception and associative ***-words structure learning,representation,associative memory,computational neuroscience

关键词： physiological characteristics based learning structure hierarchical neural network Artificial Neural Networks memory different error correction paper perception model connected cognitive problems typical dynamic aspects end ANN

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RESEARCH OF PATTERN FEATURE EXTRACTION AND SELECTION

RESEARCH OF PATTERN FEATURE EXTRACTION AND SELECTION

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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： SHI-FEI DING WEI-KUAN JIA CHUN-YANG SU ZHONG-ZHI SHI School of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 College of Plant Protection Shandong Agricultural University Taian 271018 China School of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Aca

As an important link of pattern recognition, pattern feature extraction and selection has been paid close attention by lots of scholars, and currently become one of the research hot spot in the field of pattern recognition. Its main purpose is "Low Loss Dimensionality Reduction";it is generally divided into two parts, that is, linear pattern feature extraction and selection and nonlinear pattern feature extraction and selection. This article gives a general discussion of pattern feature extraction and selection, and introduces the frontier methods of this field, at last discusses the development tendency of pattern feature extraction and selection.

关键词： Pattern Feature Eztraction information Theory Neural Network Independent Component Analysis Manifold Learning Optimization Algorithm

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Well limit behaviors of term rewriting systems

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中国高等学校学术文摘·计算机科学 2007年第3期1卷 283-296页

作者： MA Shilong SUI Yuefei XU Ke State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100083China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of SciencesBeijing 100080China

The limit behaviors of computations have not been fully *** is necessary to consider such limit behaviors when we consider the properties of infinite objects in computer science,such as infinite logic programs,the symbolic solutions of infinite polynomial ***,we can use finite objects to approximate infinite objects,and we should know what kinds of infinite objects are approximable and how to approximate them effectively.A sequence {Rκ:κω}of term rewriting systems has the well limit behavior if under the condition that the sequence has the Set-theoretic limit or the distance-based limit,the sequence {Th(Rκ):κ∈ω} of corresponding theoretic closures of Rκ has the set-theoretic or distance-based limit,and limκ→∞ Th(Rκ) is equal to the theoretic closure of the limit of {Rκ:κ∈ω).Two kinds of limits of term rewriting systems are considered:one is based on the set-theoretic limit,the other is on the distance-based *** is proved thatgiven a sequence {Rκ:κ∈ω) of term rewriting systems Rκ,if there is a well-founded ordering (-＜) on terms such that every Rκ is (-＜)-well-founded,and the set-theoretic limit of {Rκ:κ∈ω).exists,then {Rκ:κ∈ω).has the well limit behavior;and if (1) there is a well-founded ordering(-＜)on terms such that every Rκ is(-＜-well-founded,(2) there is a distance d on terms which is closed under substitutions and contexts and (3) {Rκ:κ∈ω).is Cauchy under d then {Rκ:κ∈ω).has the well limit *** results are used to approximate the least Herbrand models of infinite Horn logic programs and real Horn logic programs,and the solutions and Cr(o)bner bases of (infinite) sets of real polynomials by sequences of (finite) sets of rational polynomials.

关键词： term rewriting systems limits Gr(o)bner bases well-foundedness

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Performance of a self-paced brain computer interface on data contaminated with eye-movement artifacts and on data recorded in a subsequent session

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Computational Intelligence and Neuroscience 2008年第unknown期2008卷 1-13页

作者： Fatourechi, Mehrdad Ward, Rabab K. Birch, Gary E. Image and Signal Processing Laboratory Department of Electrical and Computer Engineering University of British Columbia Vancouver BC V6T 1Z4 Canada Institute for Computing Information and Cognitive Systems University of British Columbia Vancouver BC V6T 1Z4 Canada Neil Squire Society Burnaby BC V5M 3Z3 Canada

The performance of a specific self-paced BCI (SBCI) is investigated using two different datasets to determine its suitability for using online: (1) data contaminated with large-amplitude eye movements, and (2) data recorded in a session subsequent to the original sessions used to design the system. No part of the data was rejected in the subsequent session. Therefore, this dataset can be regarded as a pseudo-online test set. The SBCI under investigation uses features extracted from three specific neurological phenomena. Each of these neurological phenomena belongs to a different frequency band. Since many prominent artifacts are either of mostly low-frequency (e.g., eye movements) or mostly high-frequency nature (e.g., muscle movements), it is expected that the system shows a fairly robust performance over artifact-contaminated data. Analysis of the data of four participants using epochs contaminated with large-amplitude eye-movement artifacts shows that the system's performance deteriorates only slightly. Furthermore, the system's performance during the session subsequent to the original sessions remained largely the same as in the original sessions for three out of the four participants. This moderate drop in performance can be considered tolerable, since allowing artifact-contaminated data to be used as inputs makes the system available for users at ALL times.

关键词： .Brain Computer Data Contaminated Eye-Movement Artifacts Subsequent Session Recorded in Subsequent SBCI contaminated with large-amplitude neurological phenomena

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Fast global motion estimation using threshold method

Fast global motion estimation using threshold method

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MIPPR 2007: Medical Imaging, Parallel processing of Images, and Optimization Techniques

作者： Zheng, Jiali Zhang, YongDong Ni, GuangNan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9780819469533

In this paper, a fast global motion estimation method is proposed for video coding. This method can accommodate not only a translational motion model but also a polynomial motion model. It speeds up the procedure of the global motion estimation (GME) by pre-analyzing the characteristics of the block. At the first stage, the smooth region blocks which contribute less to the GME are filtered by using a threshold method based on image intensity. Next, a threshold method based on the discrepancy of the motion vectors is used to exclude the foreground blocks from the GME. From the experimental results, we can conclude that the proposed fast global motion estimation method manages to speed up the processing of estimating the motion vector field while maintaining the coding performance.

关键词： Motion estimation

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A dependency treelet string correspondence model for statistical machine translation 2

A dependency treelet string correspondence model for statist...

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2nd Workshop on Statistical Machine Translation, WMT 2007 at the 45th Annual Meeting of the Association of Computational Linguistics, ACL 2007

作者： Xiong, Deyi Liu, Qun Lin, Shouxun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

This paper describes a novel model using dependency structures on the source side for syntax-based statistical machine translation: Dependency Treelet String Correspondence Model (DTSC). The DTSC model maps source dependency structures to target strings. In this model translation pairs of source treelets and target strings with their word alignments are learned automatically from the parsed and aligned corpus. The DTSC model allows source treelets and target strings with variables so that the model can generalize to handle dependency structures with the same head word but with different modifiers and arguments. Additionally, target strings can be also discontinuous by using gaps which are corresponding to the uncovered nodes which are not included in the source treelets. A chart-style decoding algorithm with two basic operations-substituting and attaching-is designed for the DTSC model. We argue that the DTSC model proposed here is capable of lexicalization, generalization, and handling discontinuous phrases which are very desirable for machine translation. We finally evaluate our current implementation of a simplified version of DTSC for statistical machine translation. © 2007 Association for Computational Linguistics

关键词： Machine translation

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Granular computing in the information transformation of patten recognition

Granular computing in the information transformation of patt...

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2007 IEEE International Conference on Granular computing, GrC 2007

作者： Hu, Hong Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China

ISBN: (纸本)076953032X

In the past decade, many papers about granular computing(GrC) have been published, but the keypoints about granular computing(GrC) are still unclear. In this paper, we try to find the key points of GrC in the information transformation of the pattern recognition. The information similarity is the main point in the original insight of Granular computing (GrC) proposed by Zadeh(1997[1]). Many GrC researches are based on equivalence relation or more generally tolerance relation, equivalence relation or tolerance relation can be described by some distance functions and GrC can be geometrically defined in a framework of multiscale covering, at other hand, the information transformation in the pattern recognition can be abstracted as a topological transformation in a feature information space, so topological theory can be used to study GrC The key points of GrC are (1) there are two granular computing approaches to change a high dimensional complex distribution domain to a low dimensional and simple domain, (2)these two kind approaches can be used in turn if feature vector itself can be arranged in a granular way. © 2007 IEEE.

关键词： Granular computing

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A Bayesian computational cognitive model

A Bayesian computational cognitive model

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3rd International Workshop on Neural-Symbolic Learning and Reasoning, NeSy 2007, Held at IJCAI 2007

作者： Shi, Zhiwei Hu, Hong Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China Kexueyuan Nanlu #6 Beijing 100080 China

Computational cognitive modeling has recently emerged as one of the hottest issues in the AI area. Both symbolic approaches and connectionist approaches present their merits and demerits. Although Bayesian method is suggested to incorporate advantages of the two kinds of approaches above, there is no feasible Bayesian computational model concerning the entire cognitive process by now. In this paper, we propose a variation of traditional Bayesian network, namely Globally Connected and Locally Autonomic Bayesian Network (GCLABN), to formally describe a plausible cognitive model. The model adopts a unique knowledge representation strategy, which enables it to encode both symbolic concepts and their relationships within a graphical structure, and to generate cognition via a dynamic oscillating process rather than a straightforward reasoning process like traditional approaches. Then a simple simulation is employed to illustrate the properties and dynamic behaviors of the model. All these traits of the model are coincident with the recently discovered properties of the human cognitive process.

关键词： Bayesian networks

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3-D projective moment invariants

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Journal of information and Computational Science 2007年第2期4卷 821-828页

作者： Xu, Dong Li, Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Acad. of Sci. Beijing 100080 China Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Acad. of Sci. Beijing 100080 China National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Acad. of Sci. Beijing 100080 China Graduate University Chinese Acad. of Sci. Beijing 100080 China

2-D projective moment invariants were firstly proposed by Suk and Flusser in [12]. We point out here that there is a useless projective moment invariant which is equivalent to zero in their paper. 3-D projective moment invariants are generated theoretically by investigating the property of signed volume of a tetrahedron. The main part is the selection of permutation invariant cores for multiple integrals to generate independent and nonzero 3-D projective moment invariants. We give the conclusion that projective moment invariants don't exist strictly speaking because of their convergence problem.

关键词： Computer vision

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