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IEEE computer Society workshop on Mathematical Methods in Biomedical Image Analysis

2006 Conference on Computer Vision and Pattern Recognition W...

引用

2006 conference on computer vision and pattern recognition workshops 2006年 2006卷 24-25页

作者： Golland, Polina Rueckert, Daniel

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Image Intensification for Low-Light Face recognition

Image Intensification for Low-Light Face Recognition

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： D.A. Socolinsky L.B. Wolff A.J. Lundberg Equinox Corporation Baltimore MD USA Equinox Corporation New York NY USA

This paper presents the first published systematic study of face recognition performance as a function of light level using intensified near infrared imagery. This technology is the most prevalent in both civilian and military night vision equipment, and provides enough intensification for human operators to perform standard tasks under extremely low-light conditions. We describe a comprehensive data collection effort undertaken by the authors to image subjects under carefully controlled illumination and quantify the performance of standard face recognition algorithms on visible and intensified imagery as a function of light level. Performance comparisons for automatic face recognition are reported using the standardized implementations from the CSU Face Identification Evaluation System. The results contained in this paper should constitute the initial step for analysis and deployment of face recognition systems designed to work in low-light level condi

关键词： Image intensifiers Face recognition Infrared imaging Optical imaging Military standards Night vision Lighting control Image recognition Military equipment Humans

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Generalized Multiclass AdaBoost and Its Applications to Multimedia Classification

Generalized Multiclass AdaBoost and Its Applications to Mult...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wei Hao Jiebo Luo Kodak Research Laboratories Eastman Kodak (Japan) Limited USA

AdaBoost has received considerable attention in the vision and multimedia research community in recent years. It is originally designed for two-class classification problems. To handle multiple classes, many AdaBoost extensions have been developed primarily based on various schemes for reducing multiclass classification to multiple two-class problems. From a statistical prospective, AdaBoost can be viewed as a forward stepwise additive model using an exponential loss function. In this paper, we derive a generalized form of AdaBoost for multiclass classification based on a multiclass exponential loss function. To prove its effectiveness, we benchmarked a number of multimedia problems of different nature. Experimental results show that the new boosting algorithm outperforms other multiclass alternatives. In addition, the generalized boosting algorithm can be used to either boost a multiclass classifier, or build a multiclass classifier from a binary one.

关键词： Boosting Classification algorithms Machine learning algorithms Machine learning Layout Training data computer vision pattern recognition conferences Testing

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A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis

A Comprehensive Empirical Study on Linear Subspace Methods f...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Caifeng Shan Shaogang Gong P.W. McOwan Department of Computer Science Queen''s Mary University of London London UK

Automatic facial expression analysis is a vital component of intelligent Human-computer Interaction (HCI). In this paper, we present a extensive empirical study on linear subspace methods for facial expression analysis. Locality Preserving Projections (LPP) and Orthogonal Neighborhood Preserving Projections (ONPP) are first time applied to facial expression analysis. We systematically examine a number of linear subspace methods, and show that, in our comparative studies, the Supervised LPP (SLPP) is superior in supervised methods, while ONPP performs best in unsupervised learning.

关键词： Linear discriminant analysis Principal component analysis Independent component analysis Human computer interaction computer science Unsupervised learning Learning systems Neural networks Bayesian methods Support vector machines

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wikiTable: finger driven interaction for collaborative knowledge-building workspaces

wikiTable: finger driven interaction for collaborative knowl...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S. Baraldi A.D. Bimbo L. Landucci A. Valli Dipartimento di Sistemi e Informatica Università degli Studi di Firenze Italy Dipartimento di Sistemi e Informatica Università degli Studi di Firenze

We present an interactive workspace featuring visionbased gesture recognition that allows multiple users to collaborate in the creation of a concept map. The workspace integrates web-based and face-to-face scenarios in knowledge-building activities like brainstorming or problem solving sessions. A wiki serves as the repository for knowledge elements, which are presented to co-located users in form of a concept map visualized on a table. The computer vision module tracks multiple hands and fingers on the table surface using fingertip detection and tracking algorithm. The concept map and the wiki are synchronized in real-time, providing notifications to both co-located and distributed users and allowing a community shared awareness that enhances and enriches the knowledge building experience.

关键词： Fingers Collaborative work computer vision Visualization pattern recognition conferences Face detection Communication channels Containers Displays

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Planar-to-Curved-Surface Image Registration

Planar-to-Curved-Surface Image Registration

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S. Gefen N. Kiryati L. Bertrand J. Nissanov College of Medicine Philadelphia PA USA Electrical Engineering School Tel-Aviv University Ramat-Aviv Israel

A 2D-to-3D non-rigid intensity-based registration method is proposed. This planar-to-curved-surface image alignment algorithm matches experimental data of twodimensional images with their corresponding images overlaid on a curved-surface within a volumetric image. This PDE-based 2D-to-3D registration technique allows for inter-modality matching of sectional data with a volumetric image of homologous objects. We demonstrate the feasibility of this method through simulations and in the application of atlas-based segmentation of a mouse brain histological and MRI images

关键词： Image registration Brain Educational institutions Biomedical imaging USA Councils Equations Mice Rail to rail inputs computer vision pattern recognition

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A Robust Video Object Tracking by Using Active Contours

A Robust Video Object Tracking by Using Active Contours

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M.S. Allili D. Ziou Département d'informatique Faculté des Sciences Université de Sherbrook Sherbrooke QUE Canada

In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region features distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences.

关键词： Robustness Active contours Video sequences Level set computer vision Object detection Tracking Calculus Application software Video surveillance

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Boundary Extraction in Natural Images Using Ultrametric Contour Maps

Boundary Extraction in Natural Images Using Ultrametric Cont...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： P. Arbelaez CEREMADE UMR CNRS 7534 Université Paris Dauphine Paris France

This paper presents a low-level system for boundary extraction and segmentation of natural images and the evaluation of its performance. We study the problem in the framework of hierarchical classification, where the geometric structure of an image can be represented by an ultrametric contour map, the soft boundary image associated to a family of nested segmentations. We define generic ultrametric distances by integrating local contour cues along the regions boundaries and combining this information with region attributes. Then, we evaluate quantitatively our results with respect to ground-truth segmentation data, proving that our system outperforms significantly two widely used hierarchical segmentation techniques, as well as the state of the art in local edge detection.

关键词： Image segmentation Humans Image edge detection computer vision Object recognition Layout Classification tree analysis Visual perception Data mining Geometry

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Audio-Visual Foreground Extraction for Event Characterization

Audio-Visual Foreground Extraction for Event Characterizatio...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M. Cristani M. Bicego V. Murino Dipartimento di Informatica University of Verona Verona Italy DEIR University of Sassari Sassari Italy

This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone. Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage, and coupled with an audio BG/FG modelling scheme. The audiovisual association is performed on-line, by exploiting the concept of synchrony. Experimental tests carrying out classification and clustering of events show all the potentialities of the proposed approach, also in comparison with the results obtained by using the single modalities.

关键词： Layout Surveillance Information analysis Event detection Data mining Humans pattern recognition Object detection Image analysis Cameras

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Reconfigurable Streaming Architectures for Embedded Smart Cameras

Reconfigurable Streaming Architectures for Embedded Smart Ca...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S.M. Chai N. Bellas G. Kujawa T. Ziomek L. Dawson T. Scaminaci M. Dwyer D. Linzmeier Motorola Laboratories Embedded Systems Research

Smart cameras using FPGAs require an automation method to simplify the design process and to ensure both computation and memory performance are met. Reconfigurable logic allows exploration of different hardware accelerators and memory-hierarchy configurations based on application needs. This paper presents a streaming architecture template that is generated from high level program descriptions. A smart camera development platform, the software architecture, and demonstration template are also described.

关键词： Smart cameras Hardware computer architecture Field programmable gate arrays Application software computer vision Programming profession Control systems Process design Design optimization

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