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A Comparison of 3D Biometric Modalities

A Comparison of 3D Biometric Modalities

引用

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

作者： D.L. Woodard T.C. Faltemier Ping Yan P.J. Flynn K.W. Bowyer Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

Recently, researchers have focused on the use of threedimensional data as a source of distinguishing features for personal identification. In this paper, the recognition performance of three three-dimensional biometric modalities (face, ear, and finger surface) is compared. In addition, we combine the modalities in an attempt to improve overall recognition performance. The Iterative Closest Point (ICP) algorithm and root mean square (RMS) registration error are used to measure match quality in each case. The experimental results using a dataset of multi-modal biometric samples collected from a group of 85 individuals are presented.

关键词： Biometrics Ear Fingers Iterative closest point algorithm Face recognition Iterative algorithms Lighting Principal component analysis Image segmentation Image recognition

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Robust Statistical Estimation and Segmentation of Multiple Subspaces

Robust Statistical Estimation and Segmentation of Multiple S...

引用

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

作者： A.Y. Yang S.R. Rao Yi Ma Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana-Champaign IL USA

We study the problem of estimating a mixed geometric model of multiple subspaces in the presence of a significant amount of outliers. The estimation of multiple subspaces is an important problem in computer vision, particularly for segmenting multiple motions in an image sequence. We first provide a comprehensive survey of robust statistical techniques in the literature, and identify three main approaches for detecting and rejecting outliers. Through a careful examination of these approaches, we propose and investigate three principled methods for robustly estimating mixed subspace models: random sample consensus, the influence function, and multivariate trimming. Using a benchmark synthetic experiment and a set of real image sequences, we conduct a thorough comparison of the three methods

关键词： Robustness Motion estimation computer vision Image segmentation Layout Image sequences Solid modeling Geometry Face detection Cameras

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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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A Proposed Pipelined-Architecture for FPGA-Based Affine-Invariant Feature Detectors

A Proposed Pipelined-Architecture for FPGA-Based Affine-Inva...

引用

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

作者： C. Cabani W.J. MacLean Department of Electrical & Computer Engineering University of Toronto Toronto ONT Canada

This paper describes a hardware architecture for an FPGAbased implementation of affine-invariant image feature detectors, following the algorithm of Mikolajczyk & Schmid. The architecture mimics the structure of the algorithm by implementing a multi-scale Harris corner detector which feeds candidate points into an iterative procedure to determine the local affine shape of the feature’s neighbourhood (up to an undetermined rotation). Since the algorithm is iterative, and since we desire a high throughput rate, the iterations are "unrolled" into a sequence of identical computation blocks arranged in a pipeline architecture. The modularity of the resulting architecture allows for scaling the implementation to devices of different resource capacity, as well as partitioning the algorithm over several devices. The final implementation, when completed, will be part of a smart-camera system which outputs features at the same time as the associated images.

关键词： computer vision Iterative algorithms Detectors Partitioning algorithms computer architecture Hardware Feeds Shape Throughput Pipelines

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Segmentation of Neighboring Structures by Modeling Their Interaction

Segmentation of Neighboring Structures by Modeling Their Int...

引用

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

作者： Pingkun Yan Mubarak Shah School of Computer Science University of Central Florida USA

The similarity of pixel intensities, presence of noise, existence of partial volume effect and so on make segmentation of neighboring structures in medical image a challenging task. In this paper, we present a novel approach for segmenting multiple neighboring organs simultaneously by modeling interaction between them. Our method is motivated by the observation that radiologists mark ambiguous boundaries by considering all surrounding anatomic structures. In the proposed interaction model, the connectedness, competition and repulsion between organs are analyzed. By quantitatively defining these interaction components, an energy functional is formulated and structures are obtained by minimizing the energy. An energy minimization algorithm based on multiway graph cuts is proposed, which obtains an approximation within a known factor of the global minimum. The promising experimental results on various medical images demonstrate the performance of our segmentation algorithm.

关键词： Image segmentation Deformable models Biomedical imaging Level set Minimization methods Anatomical structure computer vision Topology computer science Pixel

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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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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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Boundary Extraction in Natural Images Using Ultrametric Contour Maps

Boundary Extraction in Natural Images Using Ultrametric Cont...

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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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Belief Propagation Driven Method for Facial Gestures recognition in Presence of Occlusions

Belief Propagation Driven Method for Facial Gestures Recogni...

引用

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

作者： Wei-Kai Liao I. Cohen Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

In this paper, we present a framework for automatic facial gestures recognition in a highly interactive environment. We propose to use a region-based representation of the face, and model the interdependencies of these regions using a graphical model. A parametric modeling of the local facial deformations and the use of a graphical model facilitate the characterization of intra-region dynamic patterns and the inter-region dependencies. We augment this formalism with a belief propagation algorithm to infer missing data, as well as correcting for erroneous estimations of the local deformations. The resulting approach hence handles complete observation and partial occlusion cases in an unified way, and allows for the recognition of facial gestures in presence of head motion and partial occlusions of the face. Experimental results on recognizing facial expression in various real world situations, such as non-frontal views, moving head, and occlusions illustrate the proposed approach.

关键词： Belief propagation Face recognition Graphical models Face detection Intelligent robots Parametric statistics computer vision Robotics and automation Intelligent systems Emotion recognition

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ImprovingWeb-based Image Search via Content Based Clustering

ImprovingWeb-based Image Search via Content Based Clustering

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

作者： N. Ben-Haim B. Babenko S. Belongie Department of Computer Science and Engineering University of California San Diego USA

Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content. We propose a novel approach named ReSPEC (Re-ranking Sets of Pictures by Exploiting Consistency), that is a hybrid of the two methods. Our algorithm first retrieves the results of a keyword query from an existing image search engine, clusters the results based on extracted image features, and returns the cluster that is inferred to be the most relevant to the search query. Furthermore, it ranks the remaining results in order of relevance.

关键词： Search engines Image retrieval Clustering algorithms Content based retrieval Feature extraction Object recognition computer science Joining processes Region 2 Humans

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