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

ISBN: (纸本)9781424421756

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 pixels' RGB feature in the region. Numerical and qualitative experimental results on challenging videos demonstrate the robustness of the proposed method. © 2008 IEEE.

关键词： Pixels

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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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Image segmentation using dynamic mechanism based PCNN model

Image segmentation using dynamic mechanism based PCNN model

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuanhua Qiao Jun Miao Lijuan Duan Yunfeng Lu College of Applied Science Beijing University of Technology Beijing China Institute of Computing Technology and the Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China College of Computer Science and Technology Beijing University of Technology Beijing China

Pulse-coupled neuron networks (PCNN) can be efficiently applied to image segmentation. However, the performance of segmentation depends on the suitable PCNN parameters, which are obtained by manual experiment, and the effect of the segmentation needs to be improved for images with noise. In this paper, dynamic mechanism based PCNN(DMPCNN) is brought forward to simulate the integrate-and-fire mechanism, and it is applied to segment images with noise effectively. Parameter selection is based on dynamic mechanism. Experimental results for image segmentation show its validity and robustness.

关键词： Neurons Image segmentation Couplings Mathematical model Equations Fires Artificial neural networks

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Design sparse features for age estimation using hierarchical face model

Design sparse features for age estimation using hierarchical...

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International Conference on Automatic Face and Gesture Recognition

作者： Jinli Suo Tianfu Wu Songchun Zhu Shiguang Shan Xilin Chen Wen Gao Chinese Academy and Sciences China Lotus Hill Institute of Computer Vision and Information Science China Key Laboratory of Intelligent Information Processing of CAS Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Digital Media Peking University China

A key point in automatic age estimation is to design feature set essential to age perception. To achieve this goal, this paper builds up a hierarchical graphical face model for faces appearing at low, middle and high resolution respectively. Along the hierarchy, a face image is decomposed into detailed parts from coarse to fine. Then four types of features are extracted from this graph representation guided by the priors of aging process embedded in the graphical model: topology, geometry, photometry and configuration. On age estimation, this paper follows the popular regression formulation for mapping feature vectors to its age label. The effectiveness of the presented feature set is justified by testing results on two datasets using different kinds of regression methods. The experimental results in this paper show that designing feature set for age estimation under the guidance of hierarchical face model is a promising method and a flexible framework as well.

关键词： Aging Feature extraction Active appearance model Face detection Spatial databases Skin Hair Content addressable storage Computer vision Humans

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A Novel Fuzzy Likelihood Measure Algorithm

A Novel Fuzzy Likelihood Measure Algorithm

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International Conference on Computer science and Software Engineering

作者： Shifei Ding Fengxiang Jin School of Computer Science and Technology China China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China College of Geoinformation Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781424439027

Similarity measure (PSM) is a kind of measurement that measure the size of similarity between two patterns, it plays a key role in the analysis and research of pattern recognition, machine learning, clustering analysis. This article will firstly study the current PSM theory, point out its application range; secondly, discuss the axiomatic theory of fuzzy likelihood measure, give the frequently-used algorithms of fuzzy likelihood measure that based on axiomatic theory; finally, advance a new kind of fuzzy likelihood function, then establish the fuzzy likelihood measure(FLM) between two fuzzy sets, so as to describe the similar degree between two fuzzy sets. FLM theory not only enriches and improves the PLS theory, and also provides new research methods for the theory research such as pattern recognition, machine learning, clustering analysis and so on.

关键词： Fuzzy sets Pattern recognition Fuzzy set theory Pattern analysis Size measurement Software measurement Fuzzy systems Current measurement Computer science information processing

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Mahalanobis distance based Polynomial Segment Model for Chinese Sign Language Recogniton

Mahalanobis distance based Polynomial Segment Model for Chin...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yu Zhou Xilin Chen Debin Zhao Hongxun Yao Wen Gao School of Computer Science and Technology Harbin Institute of Technology Harbin 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

Sign Language Recognition (SLR) systems are mostly based on Hidden Markov Model (HMM) and have achieved excellent results. However, the assumption of frame independence in HMM makes it inconsistent with the characteristic of strong temporal correlation in sign language signals. Polynomial Segment Model (PSM) explicitly represents the temporal evolution of sign language features as a Gaussian process with time-varying parameters. In this paper PSM is first introduced to SLR framework to solve the temporal correlation problem. Considering the correlation among the coefficients of polynomial trajectorypsilas different orders, Mahalanobis distance is used as the classification criterion to evaluate the likelihood of test data. Experimental results show that our method outperform the conventional HMM methods by 6.81% in recognition accuracy.

关键词： Hidden Markov models Handicapped aids Distance measurement Trajectory Vocabulary Euclidean distance Polynomials

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Enhancing Training Set for Face Detection Based on SVM

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Ruan Jian Xue Bao/Journal of Software 2008年第11期19卷 2921-2931页

作者： Wang, Rui-Ping Chen, Jie Shan, Shi-Guang Chen, Xi-Lin Gao, Wen Key Laboratory of Intelligent Information Processing The Chinese Academy of Sciences Beijing 100190 China Institute of Computing Technology The Chinese Academy of Sciences Beijing 100190 China Graduate University The Chinese Academy of Sciences Beijing 100049 China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

According to support vector machines (SVMs), for those geometric approach based classification methods, examples close to the class boundary usually are more informative than others. Taking face detection as an example, this paper addresses the problem of enhancing given training set and presents a nonlinear method to tackle the problem effectively. Based on SVM and improved reduced set algorithm (IRS), the method generates new examples lying close to the face/non-face class boundary to enlarge the original dataset and hence improve its sample distribution. The new IRS algorithm has greatly improved the approximation performance of the original reduced set (RS) method by embedding a new distance metric called image Euclidean distance (IMED) into the kernel function. To verify the generalization capability of the proposed method, the enhanced dataset is used to train an AdaBoost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the original collected database can be enhanced effectively by the proposed method to learn a face detector with improved generalization performance.

关键词： Support vector machines

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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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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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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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