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International Conference on Automatic Face and Gesture Recognition

作者： Annan Li Shiguang Shan Xilin Chen Xiujuan Chai 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 System Research Center Nokia Research Center Beijing China Institute of Digital Media Peking University Beijing China

This paper presents a method for recovering 3D facial shape from single image via learning the relationship between the 2D intensity images and the 3D facial shapes. With a coupled training set, the intensity images and their corresponding facial shapes make up two vector spaces respectively. But only the correlated components in both spaces are useful for inference, so there must be embedded hidden subspaces in each space which preserve the inter-space correlation information. Thus by learning the projection onto hidden subspaces based on maximum correlation criteria and optimizing the linear transform between the hidden spaces, 3D facial shape is inferred from the intensity image. The effectiveness of the method is demonstrated on both synthesized and real world data.

关键词： Content addressable storage Humans Face Lighting Light sources Statistics Deformable models Image reconstruction Active shape model Space technology

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Face reconstruction using fixation positions and foveated imaging

Face reconstruction using fixation positions and foveated im...

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International Conference on Automatic Face and Gesture Recognition

作者： Fang Fang Zhiguo Ma Laiyun Qing Jun Miao Xilin Chen Wen Gao Harbin Institute of Technology School of Computer Science and Technology China Institute of Computing Technology CASKey Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Graduate School of Chinese Academy of Sciences Beijing China Institute for Digital Media Peking University China

Face representation is important for face recognition system. Though most popular face representations are based on uniform grid sampling, some recent face recognition systems adopt weighted sampling on the different regions of a face. Psychological analysis of visual attention or human eye fixations on human face images may suggest some cues for face representation. human visual system (HVS) gives different weight to different region of human face via space-variant sampling on fovea and non-uniform distribution of fixations. This paper focuses on the problem of simulation of the foveated imaging phenomenon in HVS, and introduction of foveated imaging method into reconstruction of face in region of interest (ROI) using different fixation sources. We compare the effectiveness of actual fixation on reconstruction of face in ROI with uniform, random distribution fixation as well as fixation generated by artificial model. The experimental results on 100 face images from FRGC [7] data set show that actual fixation positions and model-generated fixation positions reconstruct the face in ROI with considerably better quality. A further analysis on the statistics of fixation positions also shows that the distribution of the fixation points is consistent with the weights of different regions on face images used in some other face recognition systems.

关键词： Image reconstruction Humans Face recognition Layout Content addressable storage Image sampling Psychology Image analysis Statistical analysis Video compression

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Texture Classification via the Wavelet-Based Contourlet and Clonal Selection

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电子学报(英文版) 2007年第3期16卷 489-494页

作者： WANG Shuang HU Ying HOU Biao JIAO Licheng Institute of Intelligent Information Processing and National Key Laboratory for Radar Signal Processing Xidian University Xi＇an 710071 China

A texture classification method based on the Wavelet-based contourlet transform (WBCT) and Clonal selection algorithm (CSA) was proposed in this paper. First, an iterative filer bank is combined with wavelet and Directional filter bank (DFB), and then its energy measures are calculated as feature vectors which a K nearest neighbor (KNN) classifier is followed to classify. It's shown that the proposed method is superior to the widely used methods based on wavelet and on contourlet. Furthermore, it is emphasized that in order to reduce the high dimension of feature vectors, two methods are introduced in this paper. One is based on rearranging coefficients, and the other is a feature selection algorithm based on CSA. The experimental results show that CSA can efficiently reduce the dimension of features while getting higher accuracy.

关键词： WBCT 结构特征小波变换信息转换

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A Direct-Path Interference Cancellation Approach to Passive Radar Based on Adaptive Fractional Delay Estimation

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电子学报(英文版) 2007年第2期16卷 359-362页

作者： ZHU Jiabing WU Xianliang TAO Liang HONG Yi Education Ministry Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China Institute of Signal Processing East China Research Institute of Electronic Engineering Hefei 230031 China

Target detection by non-cooperative illuminator is a study hotspot in electronic warfare field, with'four countering' potential advantages for passive radar. One of the major problems in continuous wave bistatic radar is Direct path interference (DPI). Conventional solutions to this problem is the use of an adaptive antenna, steering null towards the interference. Unfortunately the null depth obtained by this technology is not enough for surveillance radar. First of all, DPI appeared in passive radar based on non-cooperative FM broadcast transmitter is analyzed in this paper. Secondly, a DPI cancellation based on Adaptive fractional delay estimation (AFDE) is in detail introduced how to deal with this problem. Finally, a passive radar experimental system based on non-cooperative FM broadcast transmitter is provided, simulation results by real collected data show applying the method proposed in this paper is effective in suppression DPI.

关键词：无源雷达直达波干扰对消自适应分数延迟估计 FM广播

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Visual context representation using a combination of feature-driven and object-driven mechanisms

Visual context representation using a combination of feature...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jun Miao Lijuan Duan Laiyun Qing Xilin Chen Wen Gao Institute of Computing Technology and the Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China College of Compter Science and Technology Beijing University of Technology Beijing China School of Information Science and Engineering University of the Chinese Academy of Science Beijing China Institute of Degital Media Peking University and the Institute of Computing Technology Chinese Academy and Sciences Beijing China

Visual context between objects is an important cue for object position perception. How to effectively represent the visual context is a key issue to study. Some past work introduced task-driven methods for object perception, which led a large coding quantity. This paper proposes an approach that incorporates feature-driven mechanism into object-driven context representation for object locating. As an example, the paper discusses how a neuronal network encodes the visual context between feature salient regions and human eye centers with as little coding quantity as possible. A group of experiments on efficiency of visual context coding and object searching are analyzed and discussed, which show that the proposed method decreases the coding quantity and improve the object searching accuracy effectively.

关键词： Artificial neural networks Joints

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Hierarchical background subtraction using local pixel clustering

Hierarchical background subtraction using local pixel cluste...

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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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New approach to heterogeneous semantic search on the web

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第8期45卷 1338-1345页

作者： Huang, Rui Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

Relevance ranking is a key to Web search in determining how results are retrieved and ordered. As keyword-based search does not guarantee relevance in meanings, semantic search has been put forward as an attractive and promising approach. Recently several kinds of semantic information have been adopted in search respectively, such as thesauruses, ontologies and semantic markups, as well as folksonomies and social annotations. However, although to integrate more semantics would logically generate better search results, search mechanism to fully adopt different kinds of semantic information is still in absence and to be researched. To these ends, an integrated semantic search mechanism is proposed to incorporate textual information and keyword search with heterogeneous semantic information and semantic search. A statistical based measurement of semantic relevance, defined as semantic probabilities, is introduced to integrate both keywords and four kinds of semantic information including thesauruses, categories, ontologies and folksonomies. It is calculated with all textual information and semantic information, and stored in a newly proposed index structure called semantic-keyword dual index. Based on this uniform measurement, the search mechanism is developed that fully utilizes existing keyword and semantic search mechanisms to enhance heterogeneous semantic search. Experiments show that the proposed approach can effectively integrate both keyword-based information and heterogeneous semantic information in search.

关键词： Semantic Web

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Survey of semantic mapping in image retrieval

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 2008年第8期20卷 1085-1096页

作者： Li, Zhixin Shi, Zhiping Li, Zhiqing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

Semantic gap has become a bottleneck of content-based image retrieval. In order to bridge the gap and improve retrieval accuracy, a map from lower-level visual features to high-level semantics should be formulated. This paper provides a comprehensive survey on semantic mapping. Firstly, an image retrieval framework integrated with high-level semantics is presented. Secondly, image semantic description is introduced in two aspects: image content level-models and semantic representations. Thirdly, as the emphasis of this paper, semantic mapping approaches and techniques are investigated by classifying them into four main categories in terms of their characteristics. Various ideas and models proposed in these approaches are analyzed. In addition, advantages and limitations of each category are discussed. Finally, based on the state-of-the-art technology and the demand from real-world applications, several important issues related to semantic image retrieval are identified and some promising research directions are suggested.

关键词： Image annotation

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An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street

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Acta Automatica Sinica 2008年第11期34卷 1404-1409页

作者： Zhi-Peng LI Hong YU Yun-Cai LIU Fu-Qiang LIU Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 P. R. China Intelligent Transportation System Department Shanghai Electrical Apparatus Research Institute Shanghai 200063 P. R. China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 P. R. China

Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.

关键词： Travel time short-term forecasting license plate matching (LPM) exponential smoothing

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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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