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An alignment based similarity measure for hand detection in cluttered sign language video

An alignment based similarity measure for hand detection in ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ashwin Thangali Stan Sclaroff Department of Computer Science Boston University Boston MA USA

Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a Web cam in a user's home. Moreover, the signers' clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The histogram of oriented gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a support vector machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the vocabulary guided pyramid match kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.

关键词： Handicapped aids Clothing Testing Lighting Cameras Layout Image matching Histograms Shape Support vector machines

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3D SEGMENTATION OF RODENT BRAINS USING DEFORMABLE MODELS AND VARIATIONAL METHODS

3D SEGMENTATION OF RODENT BRAINS USING DEFORMABLE MODELS AND...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Shaoting Zhang Jinghao Zhou Xiaoxu Wang Sukmoon Chang Dimitris N. Metaxas George Pappas Foteini Delis Nora D. Volkow Gene-Jack Wang Panayotis K. Thanos Chandra Kambhamettu CBIM Rutgers University USA Behavior Neuropharmacology and Neuroimaging Lab Medical Department Brookhaven National Laboratory USA Behavior Neuropharmacologyand Neuroimaging Lab Medical Department Brookhaven National Laboratory USA Computer and Information Sciences University of Delaware USA

ISBN: (纸本)9781424439942

3D functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains. Volumetric analysis of various brain structures such as the cerebellum plays a critical role in studying the structural changes in brain regions as a function of development, trauma, or neurodegeneration. Although various segmentation methods in clinical studies have been proposed, many of them require a priori knowledge about the locations of the structures of interest, which prevents the fully automatic segmentation. Besides, the topological changes of structures are difficult to detect. In this paper, we present a novel method for detecting and locating the brain structures of interest that can be used for the fully automatic 3D functional segmentation of rodent brain MR images. The presented method is based on active shape model (ASM), Metamorph models and variational techniques. It focuses on detecting the topological changes of brain structures based on a novel volume ratio criteria. The mean successful rate of the topological change detection shows 86.6% accuracy compared to the expert identified ground truth.

关键词： Rodents Deformable models Image segmentation Brain Biomedical imaging Magnetic resonance imaging Anatomy Active shape model Alzheimer's disease Image analysis

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A framework for automated measurement of the intensity of non-posed Facial Action Units

A framework for automated measurement of the intensity of no...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mohammad H. Mahoor Steven Cadavid Daniel S. Messinger Jeffrey F. Cohn Department of Electrical and Computer Engineering University of Denver Denver CO USA Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Department of Psychology University of Miami Coral Gables FL USA Department of Psychology University of Pittsburgh Pittsburgh PA USA

This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (lip corner puller) and AU6 (cheek raising) in videos captured from infant-mother live face-to-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train support vector machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.

关键词： Humans Face detection Image analysis Psychology Face recognition Support vector machines Support vector machine classification Shape Behavioral science Image texture analysis

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A general framework for Approximate Nearest Subspace search

A general framework for Approximate Nearest Subspace search

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International conference on computer vision workshops (ICCV workshops)

作者： Ronen Basri Tal Hassner Lihi Zelnik-Manor Weizmann Institute of Science Rehovot Israel Open University of Israel Raanana Israel Israel Institute of Technology Technion-Israel Institute of Technology Haifa Israel

Subspaces offer convenient means of representing information in many pattern recognition, Machine vision, and Statistical Learning applications. Contrary to the growing popularity of subspace representations, the problem of efficiently searching through large subspace databases has received little attention in the past. In this paper we present a general solution to the Approximate Nearest Subspace search problem. Our solution uniformly handles cases where both query and database elements may differ in dimensionality, where the database contains subspaces of different dimensions, and where the queries themselves may be subspaces. To this end we present a simple mapping from subspaces to points, thus reducing the problem to the well studied Approximate Nearest Neighbor problem on points. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its performance on synthetic and real data. Our tests indicate that an approximate nearest subspace can be located significantly faster than the nearest subspace, with little loss of accuracy.

关键词： Databases Search problems pattern recognition Nearest neighbor searches Error correction computer vision conferences Face recognition Machine vision Statistical learning

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Supervised Neighborhood Topology Learning for Human Action recognition

Supervised Neighborhood Topology Learning for Human Action R...

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International conference on computer vision workshops (ICCV workshops)

作者： Jinhua Ma Pong C Yuen Weiwen Zou Jian-Huang Lai Sun Yat-Sen University Guangzhou China Hong Kong Baptist University Hong Kong China

Supervised manifold learning has been successfully applied to human action recognition. With the class label information, the recognition performance can be improved. However, the learned manifold may not be able to well preserve the local structure which reflects temporal information of an action. To overcome this limitation, this paper proposes a new supervised manifold learning algorithm namely supervised neighborhood topology learning (SNTL) for human action recognition. SNTL is based on the framework of locality preserving projection (LPP). Different from LPP, SNTL constructs the adjacency graph with a topology defined in a supervised manner, which not only separates data points from different actions but also preserves the local structure of data points from the same action. With the advantage of locality preserving property in the framework of LPP, SNTL provides good discriminant ability and preserves temporal information of each action contained in local structure. Weizmann human action database is used for evaluation. Experimental results show that the method achieves 95.56% recognition accuracy.

关键词： Topology Humans computer vision Data mining Optical computing Linear discriminant analysis pattern recognition conferences Sun Shape

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Boosted Exemplar Learning for human action recognition

Boosted Exemplar Learning for human action recognition

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International conference on computer vision workshops (ICCV workshops)

作者： Tianzhu Zhang Jing Liu Si Liu Yi Ouyang Hanqing Lu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Human action recognition has been an active research topic in computer vision. How to model all kinds of actions, varying with time resolution, visual appearance, etc., is quite a challenging task for recognition. In this paper, we propose a Boosted Exemplar Learning (BEL) approach to recognize various actions in a weakly supervised manner, i.e., only video-based labels are provided but frame-based ones are not. First, for a given action, each video is described as a set of similarities between its frames and some candidate ones (called as exemplars), which are selected from training videos belonging to the action. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the Multiple Instance Learning (MIL), in which a positive (or negative) video is deemed as a positive (or negative) bag and those similar frames to the given exemplar in Euclidean Space as instances. Second, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain a video-based action detector in the boosted learning process. Experimental results on two publicly available challenging datasets: the KTH dataset and Weizmann dataset demonstrate the validity and effectiveness of the proposed approach.

关键词： Humans

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Identity and Variation Spaces: Revisiting the Fisher Linear Discriminant

Identity and Variation Spaces: Revisiting the Fisher Linear ...

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International conference on computer vision workshops (ICCV workshops)

作者： Sheng Zhang Terence Sim Mei-Chen Yeh Department of Psychology University of California Santa Barbara USA School of Computing National University of Singapore Singapore Department of Computer Science and Information Engineering National Taiwan Normal University Taipei Taiwan

The Fisher Linear Discriminant (FLD) is commonly used in classification to find a subspace that maximally separates class patterns according to the Fisher Criterion. It was previously proven that a pre-whitening step can be used to truly optimize the Fisher Criterion. In this paper, we study the theoretical properties of the subspaces induced by this whitened FLD. Of the four subspaces induced, two are most important for classification and representation of patterns. We call these Identity Space and Variation Space. We show that only the between-class variation remains in Identity Space, and only the within-class variation remains in Variation Space. Both spaces can be used for decomposition and representation of class data. Moreover, we give sufficient conditions for these spaces to exist. Finally, we also run experiments to show how Identity and Variation Spaces may be used for classification and image synthesis.

关键词： Linear discriminant analysis pattern classification conferences Psychology computer science Sufficient conditions Image generation Face recognition Heart Scattering

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Stepwise Correlation metric based Discriminant Analysis and multi-probe images fusion for face recognition

Stepwise Correlation metric based Discriminant Analysis and ...

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International conference on computer vision workshops (ICCV workshops)

作者： Zhen Lei Shengcai Liao Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Face recognition is a great challenge in practice. Subspace learning method is one of the dominant methods and has achieved great success in face recognition area. In subspace learning, many researches have found that correlation similarity (e.g. cosine distance) usually achieves better classification results than L2 distance with nearest neighbor (NN) classifier in Euclidean space. However, in traditional methods, most of them are devoted to optimize the objective function based on L2 distance, which is not coincident with the classification rule. It is reasonable to obtain better results by optimizing the objective function with correlation metric directly. In this paper, following traditional linear discriminant analysis (LDA), we redefine the between and with-in class scatter with correlation metric and propose an efficient Stepwise Correlation metric based Discriminant Analysis (SCDA) method to derive the sub-optimal discriminant subspace to be classified with correlation similarity. Moreover, we propose a novel weighted fusion mechanism to learn the optimal combination of multi-probe images to be classified. Extensive experiments on PIE and extended Yale-B databases validate the effectiveness of SCDA and the learning based weighted image fusion method.

关键词： Image analysis Image fusion Face recognition Linear discriminant analysis Learning systems Nearest neighbor searches Neural networks Optimization methods Scattering Image databases

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Curvature Scale Space Technique in computer vision: Basic Concept and Theoretical Developments

Curvature Scale Space Technique in Computer Vision: Basic Co...

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Chinese conference on pattern recognition/1st CJK Joint Workshop on pattern recognition

作者： Zhong, Baojiang Ma, Kai-Kuang Liu, Wenzhong Nanjing Univ Aeronaut & Astronaut Dept Math Nanjing 211100 Peoples R China Nanjing Univ State Key Lab Novel Software Engn Nanjing 210093 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781424441990

Scale space techniques have attracted much attention in the field of computer vision and image processing. In particular, the curvature scale space (CSS) technique was selected in the MPEG-7 standard due to a number of nice properties. In this paper the scale space concept is first explained in detail and its significance in solving shape-based vision problems is clarified. A brief survey of the theoretical developments of the CSS technique in the past few decades is then presented.

关键词： Scale space curvature scale space curve evolution shape representation corner detection MPEG-7

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A multiple objectives optimization approach for QoS-based web services compositions

A multiple objectives optimization approach for QoS-based we...

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ieee International conference on e-Business Engineering, ICEBE 2009;ieee Int. workshops - AiR 2009;SOAIC 2009;SOKMBI 2009;ASOC 2009

作者： Lan, Ci-Wei Chen, Rick C. S. Su, Addison Y. S. Huang, Angus F. M. Yang, Stepehen J. H. Chung, Jen-Yao Service Technology and Management Research Center National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan T.J. Watson Research Center IBM New York NY United States

ISBN: (纸本)9780769538426

While a lot of Web Services are available nowadays, Quality-of-Service (QoS) becomes a crucial concern to distinguish alternatives from each other. QoS-based service composition aims to determining optimal combinations among exponential candidates with consideration of overall non-functional performance. In this paper, a multiple objectives optimization approach is proposed for the decision-making process. First, we design a pattern-wise replacement method to derive composite QoS objectives of a given XML-encoded workflow process. According to the process descriptions, the composite QoS objectives will be available by synthesizing non-functional metrics of primitive patterns repeatedly. Second, we employ the Ε-Pareto dominance relations to discriminate the overall QoS performance of combinations. Thus user can freely set any desirable Ε values to define the precision of service composition's QoS performance. Finally, we present a genetic algorithm to find out the optimal combinations through evolutionary computation. The experimental results have shown that the proposed approach is more efficient and effective in terms of user-defined Ε values for different QoS objectives. © 2009 ieee.

关键词： Quality of service

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