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Video segmentation based on graphical models

Video segmentation based on graphical models

引用

conference on computer Vision and pattern recognition (CVPR)

作者： Yang Wang Tele Tan Kia-Fock Loe Institute for Infocomm Research Singapore Department of Computer Science National University of Singapore Singapore

ISBN: (纸本)0769519008

This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatiotemporal constraints. Given consecutive frames, an optimization method is proposed to maximize the conditional probability density of the three fields in an iterative way. Experimental results show that the approach is robust and generates spatiotemporally coherent segmentation results.

关键词： Graphical models Bayesian methods Markov random fields Robustness Layout Video sequences Video compression Motion estimation Merging image segmentation

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Recognizing expression variant faces from a single sample image per class

Recognizing expression variant faces from a single sample im...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： A.M. Martinez Department of Electrical Engineering Ohio State Uinversity OH USA

Although important contributions to face recognition have been reported, few focus on how to robustly recognize expression variant faces from as few as one single training sample per class. Since learning cannot generally be applied when only one sample per class is available, matching techniques (distance measures) are usually employed instead (e.g. correlations). However, distance measures generally attempt to match all features with equal importance (weighting), because not only is it difficult to know which features are more useful (for classification), but when or under which circumstances this happens. For example, when recognizing faces in the original image space (e.g. using the Euclidean distance-correlation), it is not known which pixels are more and which are less appropriate for use. We use the optical flow between the testing and sample images as a measure of how good each pixel is. Pixels that have a small flow will have high weights, pixels with a large flow will have small weights. Our experimental results show that the method proposed in this contribution outperforms the classical Euclidean distance (correlation) measure and the PCA (principal component analysis) approach.

关键词： Face recognition image recognition Pixel Robustness Euclidean distance Principal component analysis System testing Face detection pattern recognition image motion analysis

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Man-made structure detection in natural images using a causal multiscale random field

Man-made structure detection in natural images using a causa...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： S. Kumar M. Hebert The Robotics Institute Carnegie Mellon University Pittsburgh PA USA

This paper presents a generative model based approach to man-made structure detection in 2D (two-dimensional) natural images. The proposed approach uses a causal multiscale random field suggested by Bouman and Shapiro (1994) as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence of the observed data, we propose to capture the local dependencies in the data using a multiscale feature vector. The distribution of the multiscale feature vectors is modeled as mixture of Gaussians. A set of robust multi-scale features is presented that captures the general statistical properties of man-made structures at multiple scales without relying on explicit edge detection. The proposed approach was validated on real-world images from the Corel data set, and a performance comparison with other techniques is presented.

关键词： image edge detection Layout Buildings image retrieval Object detection Robots Gaussian distribution Robustness Robotics and automation Navigation

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Spacetime stereo: a unifying framework for depth from triangulation

Spacetime stereo: a unifying framework for depth from triang...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： J. Davis R. Ramamoorthi S. Rusinkiewicz Honda Research Institute USA Columbia University USA Princeton University USA

Depth from triangulation has traditionally been treated in a number of separate threads in the computer vision literature, with methods like stereo, laser scanning, and coded structured light considered separately. In this paper, we propose a common framework, spacetime stereo, which unifies many of these previous methods. Viewing specific techniques as special cases of this general framework leads to insights regarding the solutions to many of the traditional problems of individual techniques. Specifically, we discuss a number of innovative possible applications such as improved recovery of static scenes under variable illumination, spacetime stereo for moving objects, structured light and laser scanning with multiple simultaneous stripes or patterns, and laser scanning of shiny objects. To suggest the practical utility of the framework, we use it to analyze one of these applications: recovery of static scenes under variable, but uncontrolled, illumination. Based on our analysis, we show that methods derived from the spacetime stereo framework can be used to recover depth in situations in which existing methods perform poorly.

关键词： Stereo vision Layout Lighting Stereo image processing Yarn computer vision Computational geometry Performance analysis Orbital robotics Robot vision systems

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Multilinear subspace analysis of image ensembles

Multilinear subspace analysis of image ensembles

引用

conference on computer Vision and pattern recognition (CVPR)

作者： M.A.O. Vasilescu D. Terzopoulos Department of Computer Science University of Toronto Toronto ONT Canada Courant Institute of Mathematical Sciences New York University New York NY USA

Multilinear algebra, the algebra of higher-order tensors, offers a potent mathematical framework for analyzing ensembles of images resulting from the interaction of any number of underlying factors. We present a dimensionality reduction algorithm that enables subspace analysis within the multilinear framework. This N-mode orthogonal iteration algorithm is based on a tensor decomposition known as the N-mode SVD, the natural extension to tensors of the conventional matrix singular value decomposition (SVD). We demonstrate the power of multilinear subspace analysis in the context of facial image ensembles, where the relevant factors include different faces, expressions, viewpoints, and illuminations. In prior work we showed that our multilinear representation, called TensorFaces, yields superior facial recognition rates relative to standard, linear (PCA/eigenfaces) approaches. We demonstrate factor-specific dimensionality reduction of facial image ensembles. For example, we can suppress illumination effects (shadows, highlights) while preserving detailed facial features, yielding a low perceptual error.

关键词： image analysis Tensile stress Algebra Lighting Algorithm design and analysis Matrix decomposition Singular value decomposition Face recognition Principal component analysis Facial features

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Feature selection by maximum marginal diversity: optimality and implications for visual recognition

Feature selection by maximum marginal diversity: optimality ...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： N. Vasconcelos Department of Electrical and Computer Engineering University of California San Diego USA

We have recently shown that: 1) the infomax principle for the organization of perceptual systems leads to visual recognition architectures that are nearly optimal in the minimum Bayes error sense, and 2) a quantity which plays an important role in infomax solutions is the marginal diversity (MD) - the average distance between the class-conditional density of each feature and their mean. Since MD is a discriminant quantity and can be computed with great efficiency, the principle of maximum marginal diversity (MMD) was suggested for discriminant feature selection. In this paper, we study the optimality (in the infomax sense) of the MMD principle and analyze its effectiveness for feature selection in the context of visual recognition. In particular, 1) we derive a close form relation between the optimal infomax and MMD solutions, and 2) show that there is a family of classification problems for which the two are identical. Examination of this family in light of recent studies on the statistics of natural images suggests that the equivalence conditions are likely to hold for the problem of visual recognition. We present experimental evidence supporting the conclusions that: 1) MD is a good predictor for the recognition ability of a given set of features; 2) MMD produces features that are more discriminant than those obtained with currently predominant criteria such as energy compaction; and 3) the extracted features are detectors of visual attributes that are perceptually relevant for low-level image classification.

关键词： image recognition Compaction Feature extraction computer vision Character generation computer architecture computer errors Statistics Detectors image classification

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Probabilistic tracking in joint feature-spatial spaces

Probabilistic tracking in joint feature-spatial spaces

引用

conference on computer Vision and pattern recognition (CVPR)

作者： A. Elgammal R. Duraiswami L.S. Davis Department of Computer Science Rutgers University Piscataway NJ USA UMIACS University of Maryland College Park MD USA Department of Computer Science University of Maryland College Park MD USA

In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.

关键词： Target tracking computer science Educational institutions Robustness computer vision Region 6 Deformable models Random variables Kernel Measurement uncertainty

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Analyzing appearance and contour based methods for object categorization

Analyzing appearance and contour based methods for object ca...

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conference on computer Vision and pattern recognition (CVPR)

作者： B. Leibe B. Schiele Perceptual Computing and Computer Vision Group ETH Zurich Switzerland

Object recognition has reached a level where we can identify a large number of previously seen and known objects. However, the more challenging and important task of categorizing previously unseen objects remains largely unsolved. Traditionally, contour and shape based methods are regarded most adequate for handling the generalization requirements needed for this task. Appearance based methods, on the other hand, have been successful in object identification and detection scenarios. Today little work is done to systematically compare existing methods and characterize their relative capabilities for categorizing objects. In order to compare different methods we present a new database specifically tailored to the task of object categorization. It contains high-resolution color images of 80 objects from 8 different categories, for a total of 3280 images. It is used to analyze the performance of several appearance and contour based methods. The best categorization result is obtained by an appropriate combination of different methods.

关键词： Object recognition Shape computer vision image databases Performance analysis Face detection image segmentation Psychology Humans Object detection

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Perception-based 3D triangle mesh segmentation using fast marching watersheds

Perception-based 3D triangle mesh segmentation using fast ma...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： A.F. Koschan Imaging Robotics and Intelligent Systems Laboratory University of Tennessee Knoxville Tennessee

In this paper, we describe an algorithm called fast marching watersheds that segments a triangle mesh into visual parts. This computer vision algorithm leverages a human vision theory known as the minima rule. Our implementation computes the principal curvatures and principal directions at each vertex of a mesh, and then our hill-climbing watershed algorithm identifies regions bounded by contours of negative curvature minima. These regions fit the definition of visual parts according to the minima rule. We present evaluation analysis and experimental results for the proposed algorithm.

关键词： image segmentation computer vision Humans Robustness Intelligent robots Data structures Intelligent systems Laboratories Algorithm design and analysis Surface fitting

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Practical non-parametric density estimation on a transformation group for vision

Practical non-parametric density estimation on a transformat...

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conference on computer Vision and pattern recognition (CVPR)

作者： E.G. Miller C. Chefd'hotel Computer Science Division University of California Berkeley Berkeley CA USA Siemens AG Corporate Research and Development Princeton NJ USA Odyssée Laboratory INRIA Sophia-Antipolis France

It is now common practice in machine vision to define the variability in an object's appearance in a factored manner, as a combination of shape and texture transformations. In this context, we present a simple and practical method for estimating non-parametric probability densities over a group of linear shape deformations. Samples drawn from such a distribution do not lie in a Euclidean space, and standard kernel density estimates may perform poorly. While variable kernel estimators may mitigate this problem to some extent, the geometry of the underlying configuration space ultimately demands a kernel, which accommodates its group structure. In this perspective, we propose a suitable invariant estimator on the linear group of non-singular matrices with positive determinant. We illustrate this approach by modeling image transformations in digit recognition problems, and present results showing the superiority of our estimator to comparable Euclidean estimators in this domain.

关键词： Shape Kernel Deformable models Costs Independent component analysis Machine vision computer science Information geometry image recognition Probability

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