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International Conference on Automatic Face and Gesture Recognition

作者： Dan Wang Xiujuan Chai Hongming Zhang Hong Chang Wei Zeng Shiguang Shan Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China NEC Laboratories Beijing China

Segmenting hair regions from human images facilitates many tasks like hair synthesis and hair style trends forecast. However, hair segmentation is quite challenging due to hair/background confusion and large hair pattern diversity. To address these problems to some extent, this paper proposes a novel coarse-to-fine hair segmentation method. In our approach, firstly, the recently proposed “Active Segmentation with Fixation” (ASF) is used to coarsely define an enclosed candidate region with high-recall (but possibly low-precision) of hair pixels and exclude considerable part of the backgrounds which are easily confused with hair. Then Graph Cuts (GC) method is applied to the candidate regions to remove additional false positives by incorporating hair-specific information. Specifically, Bayesian method is employed to select some reliable hair and background regions (seeds) among the ones over-segmented by Mean Shift. SVM classifier is then learnt online from these seeds and explored to predict hair/background likelihood probability, which is subsequently fed into GC algorithm. The novelty of the proposed approach lies in three folds: 1) an elaborate design of hair segmentation framework, which utilizes ASF to reduce the candidate hair regions and adopts GC to achieve more accurate hair region contours; 2) the region-based strategy for seed selection; 3) the exploration of the discriminative method, SVM, to predict the probability of each pixel belonging to hair and background regions. Extensive experimental results demonstrate the approach outperforms recently proposed methods.

关键词： Hair Pixel Image segmentation Support vector machines Bayesian methods Image color analysis Databases

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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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DSMS in Ubiquitous-Healthcare: A Borealis-based Heart Rate Variability Monitor

DSMS in Ubiquitous-Healthcare: A Borealis-based Heart Rate V...

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2011 4th International Conference on Biomedical Engineering and Informatics(第四届生物医学工程与信息学国际会议 BMEI 2011)

作者： Xiaorui Jiang Jinwook Choi Sooyoung Yoo Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of S Department of Biomedical Engineering Seoul National University Seoul Korea

With tremendous research progress in biomedical sensors and sensor networks, there is an increasingly need for employing new data processing technologies that are capable of online analysis of the streaming medical sensor data, which is a typical health monitoring scenario. This paper describes using the data stream management system (DSMS) in developing ubiquitous health monitoring application. We propose the system architecture suitable for simultaneously processing streaming data from heterogeneous sources and performing different monitoring tasks planned by clinicians. A pilot heart rate variability (HRV) monitor with a graphical user interface is developed based on Borealis and several tasks are implemented, including monitoring Rmssd, MeanHR (mean heart rate), and pNN50. Results of the test runs in the lab environment show the usefulness and potential of DSMS in performing intelligent real-time medical data processing.

关键词： Data Stream Management System Heart Rate Variability Ubiquitous Healthcare Real-Time Health Monitoring

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Enhanced line-based intra prediction with fixed interpolation filtering

Enhanced line-based intra prediction with fixed interpolatio...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Li Zhang Siwei Ma Wen Gao Xin Zhao Institute of Digital Media Peking University Beijing China Key Laboratory of Intelligence. Information Processing Institute for Computing Technology Beijing China

In this paper, a novel intra prediction algorithm, named enhanced line-based intra prediction (ELIP), is proposed to improve the traditional intra prediction methods for image/intra-frame coding. Different from the existing schemes where the interpolation filtering only depends on the intra prediction mode, in the proposed ELIP, the linear filtering coefficients are further refined by imposing both mode and position dependencies. For each intra prediction mode and each position within one block, a set of linear filtering coefficients are obtained from off-line training using the least square method. The proposed algorithm has been implemented in the latest ITU-T VCEG KTA software. Experimental results demonstrate that, compared with the intra coding scheme of KTA with new intra coding tool enabled, up to 0.44dB additional coding gain is achieved by the proposed method, while keeping applicable computational complexity for practical video codecs.

关键词： Training Maximum likelihood detection IP networks Pixel Encoding Image coding Filtering

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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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Attention driven face recognition: A combination of spatial variant fixations and glance

Attention driven face recognition: A combination of spatial ...

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International Conference on Automatic Face and Gesture Recognition

作者： Chongxiu Wang Laiyun Qing Jun Miao Fang Fang Xilin Chen University of the Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China

This paper proposes a novel face recognition algorithm inspired by the selective attention of Human Visual System (HVS). We record four observers' eye movements when they are viewing 100 FRGC [1] frontal view face images and find that the observers are highly consistent in the regions fixated. Inspired by the fact that fovea of HVS has a much higher spatial acuity than the periphery, a face recognition algorithm based on spatial variant sampling is proposed to simulate such foveated imaging phenomenon, where more information is reserved for the fixated regions. Moreover, information extracted from glance which adopts the low spatial frequency components of the image is integrated into the face recognition system to elicit a percept that occurs before any fixations. The experimental results on FERET database [2] demonstrate that the proposed method not only reduces the computational cost, but also achieves comparable performance, which shows that the characteristics of the HVS provide valuable insights into face recognition.

关键词： Face Face recognition Observers Humans Pixel Databases Feature extraction

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Shape-based web image clustering for unsupervised object detection?

Shape-based web image clustering for unsupervised object det...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wei Zheng Changhu Wang Xilin Chen Graduate School Chinese Academy and Sciences Beijing China Microsoft Research Asia Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781612843483

Automatic object detection for an arbitrary class is an important but very challenging problem, due to the countless kinds of objects in the world and the large amount of labeling work for each object. In this work, we target at solving the problem of automatic object detection for an arbitrary class without the laborious human effort. Motivated by the explosive growth of Web images and the phenomenal success of search techniques, we develop an unsupervised object detection framework by automatically training the object detector on the top returns of certain image search engine queried by the name of the object class. In order to automatically isolate the objects from the Web images for training, only clipart images with simple background are used, which preserve most of the shape information of the objects. A two-stage shape-based clustering algorithm is proposed to mine typical shapes of the object, in which the inner-class variance of object shapes is considered and undesired images are filtered out. In order to reduce the gap between clipart images and real-world images, we introduce an efficient algorithm to synthesize the real-world images from clipart images, and only shape feature is used in the detector training part. Finally, the synthetic images could be used to train object detectors by an off-the-shelf discriminative algorithm, e.g., boosting or SVM. Extensive experiments show the effectiveness of the proposed framework on objects with simple and representative shapes, and the proposed framework could be considered as a good beginning of solving this challenging problem.

关键词： Shape Clustering algorithms Training Detectors Image edge detection Object detection Image color analysis

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Context constrained facial landmark localization based on discontinuous Haar-like feature

Context constrained facial landmark localization based on di...

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International Conference on Automatic Face and Gesture Recognition

作者： Xiaowei Zhao Xiujuan Chai Zhiheng Niu Cherkeng Heng Shiguang Shan Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Panasonic Singapore Laboratories Private Limited (PSL) Singapore

Facial landmark localization is well known as one of the bottlenecks in face recognition. This paper proposes a novel facial landmark localization method, which introduces facial context constrains into cascaded AdaBoost framework. The motivation of our method lies in the basic human physiology observation that not only the local texture information but also the global context information is used together for human to realize the landmark location task. Therefore, in our solution, a novel type of Haar-like feature, called discontinuous Haar-like feature, is proposed to characterize the facial context, i.e. the cooccurrence relationship between target facial landmark and other local texture patterns within face region (including other landmarks, facial organs and also smoothing regions). For the locating task, traditional Haar-like features (characterizing local texture information) and discontinuous Haar-like features (characterizing context constrains in global sense) are combined together to form more powerful representations. Through Real AdaBoost learning, distinctive features are selected automatically and used for facial landmark detection. Our experiments on BioID and Cohn-Kanade databases have validated the proposed method by comparing with other state-of-the-art results.

关键词： Context Face Feature extraction Mouth Databases Pixel Accuracy

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Contracting of E-Business Services in a Constraint- Based Process Modeling Framework

Contracting of E-Business Services in a Constraint- Based Pr...

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The Seventh International Conference on Semantics,Knowledge,and Grids(第七届语义、知识与网格国际会议 SKG 20110)

作者： Ruoyu Chen Lejian Liao Zhi Fang Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing 100081 Chi

Analyzing and contracting of E-Business service processes is a crucial step between discovery and execution of Semantic Web Services. This paper proposes a service modelling framework consists of constraint-based service contract, agent style communication messages and service process specification. Service contract is used as shared knowledge between participants and instrument for advertising and requesting of services. The Constraint Process Specification proposed is capable of describing complex data and control-flow restrictions as well as data content restrictions. A state transition based operational semantic for this framework is given. A virtual machine is built for analyzing and contracting of services. We illustrate the expressiveness of this framework within an online shopping scenario.

关键词： Semantic Web Service Service Contracting Constraint Process Specification

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Visual saliency detection by spatially weighted dissimilarity

Visual saliency detection by spatially weighted dissimilarit...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lijuan Duan Chunpeng Wu Jun Miao Laiyun Qing Yu Fu College of Computer Science and Technology Beijing University of Technology Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China School of Information Science and Engineering Chinese Academy and Sciences Beijing China Department of Computing University of Surrey Guildford Surrey UK

In this paper, a new visual saliency detection method is proposed based on the spatially weighted dissimilarity. We measured the saliency by integrating three elements as follows: the dissimilarities between image patches, which were evaluated in the reduced dimensional space, the spatial distance between image patches and the central bias. The dissimilarities were inversely weighted based on the corresponding spatial distance. A weighting mechanism, indicating a bias for human fixations to the center of the image, was employed. The principal component analysis (PCA) was the dimension reducing method used in our system. We extracted the principal components (PCs) by sampling the patches from the current image. Our method was compared with four saliency detection approaches using three image datasets. Experimental results show that our method outperforms current state-of-the-art methods on predicting human fixations.

关键词： Humans Color Visualization Image color analysis Principal component analysis Correlation Computational modeling

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