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Context and Hierarchy in a Probabilistic Image Model

Context and Hierarchy in a Probabilistic Image Model

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ya Jin S. Geman Division of Applied Mathematics Brown University USA

It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/whole hierarchy. We propose a mathematical framework (a "composition machine") for constructing probabilistic hierarchical image models, designed to accommodate arbitrary contextual relationships, and we build a demonstration system for reading Massachusetts license plates in an image set collected at Logan Airport. The demonstration system detects and correctly reads more than 98% of the plates, with a negligible rate of false detection. Unlike a formal grammar, the architecture of a composition machine does not exclude the sharing of sub-parts among multiple entities, and does not limit interpretations to single trees (e.g. a scene can have multiple license plates, or no plates at all). In this sense, the architecture is more like a general Bayesian network than a formal grammar. On the other hand, unlike a Bayesian network, the distribution is non-Markovian, and therefore more like a probabilistic context-sensitive grammar. The conceptualization and construction of a composition machine is facilitated by its formulation as the result of a series of non-Markovian perturbations of a "Markov backbone."

关键词： Context modeling Layout computer vision Biological system modeling Mathematics Licenses Bayesian methods Machine vision Mathematical model Airports

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3D shape from anisotropic diffusion

3D shape from anisotropic diffusion

引用

conference on computer vision and pattern recognition (cvpr)

作者： P. Favaro S. Osher S. Soatto L. Vese Department of Electrical Engineering Washington University Saint Louis MO USA Department of Applied Mathematics UCLA Los Angeles CA USA Department of Computer Science UCLA Los Angeles CA USA

We cast the problem of inferring the 3D shape of a scene from a collection of defocused images in the framework of anisotropic diffusion. We propose an algorithm that can estimate the shape of a scene by inferring the diffusion coefficient of a heat equation. The method is optimal, as we pose it as the minimization of a certain cost functional based on the input images, and fast. Furthermore, we also extend our algorithm to the case of multiple images, and derive a 3D scene segmentation algorithm that can work in the presence of pictorial camouflage.

关键词： Shape computer society computer vision pattern recognition

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A variational approach to scene reconstruction and image segmentation from motion-blur cues

A variational approach to scene reconstruction and image seg...

引用

conference on computer vision and pattern recognition (cvpr)

作者： P. Favaro S. Soatto Department of Computer Science UCLA Los Angeles CA USA

In this paper we are interested in the joint reconstruction of geometry and photometry of scenes with multiple moving objects from a collection of motion-blurred images. We make simplifying assumptions on the photometry of the scene (we neglect complex illumination effects) and infer the motion field of the scene, its depth map, and its radiance. In particular, we choose to partition the image into regions where motion is well approximated by a simple planar translation. We model motion-blurred images as the solution of an anisotropic diffusion equation, whose initial conditions depend on the radiance and whose diffusion tensor encodes the depth map of the scene and the motion field. We propose an algorithm to infer the unknowns of the model by minimizing the discrepancy between the measured images and the ones synthesized via diffusion. Since the problem is ill-posed, we also introduce additional Tikhonov regularization terms.

关键词： Layout Image reconstruction Image segmentation Image resolution Photometry Cameras computer science Computational geometry Lighting Anisotropic magnetoresistance

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3D Facial Expression recognition Based on Primitive Surface Feature Distribution

3D Facial Expression Recognition Based on Primitive Surface ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jun Wang Lijun Yin Xiaozhou Wei Yi Sun Department of Computer Science State University of New York Binghamton NY USA

The creation of facial range models by 3D imaging systems has led to extensive work on 3D face recognition [19]. However, little work has been done to study the usefulness of such data for recognizing and understanding facial expressions. Psychological research shows that the shape of a human face, a highly mobile facial surface, is critical to facial expression perception. In this paper, we investigate the importance and usefulness of 3D facial geometric shapes to represent and recognize facial expressions using 3D facial expression range data. We propose a novel approach to extract primitive 3D facial expression features, and then apply the feature distribution to classify the prototypic facial expressions. In order to validate our proposed approach, we have conducted experiments for person-independent facial expression recognition using our newly created 3D facial expression database. We also demonstrate the advantages of our 3D geometric based approach over 2D texture based approaches in terms of various head poses.

关键词： Face recognition Face detection Shape Humans Data mining Head Skin Prototypes Feature extraction computer vision

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Articulated models from video

Articulated models from video

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conference on computer vision and pattern recognition (cvpr)

作者： N. Krahnstoever R. Sharma GE Research Schenectady NY USA Department of Computer Science and Engineering 220 Pond Laboratory Pennsylvania State University University Park PA USA

Past research on model-based tracking of articulated targets has neglected to address the problems of model-acquisition and initialization. However, for model-based approaches to ever become practical and autonomous, these important issues need to be addressed Towards this goal, this paper-presents a model-acquisition framework for acquiring articulated models directly from monocular video. Both structure, shape, and appearance of articulated models are estimated In addition, the initialization problem is solved by estimating pose information for at least one frame of a sequence, allowing subsequent model-based tracking. The presented work is based on basic assumptions and hence not restricted towards specific types of targets. It has in particular the ability to process human as well as non-human targets and makes no assumptions with respect to the structure of the kinematic tree or complexity. This work hence presents a set of systematic solutions to the problems of model-acquisition and initialization that bridge the gap between state of the art model-based tracking approaches and practical applications.

关键词： Target tracking Shape Humans computer vision Motion analysis Cameras Video sequences Motion segmentation computer science Kinematics

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New, faster, more controlled fitting of implicit polynomial 2D curves and 3D surfaces to data

New, faster, more controlled fitting of implicit polynomial ...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhibin Lei D.B. Cooper Division of Engineering Brown University Providence RI USA

Denote a point in the plane by z=(z,y) and a polynomial of nth degree in z by f(z) /spl Sigma//sub i,j//spl ges/o/sub 1/i+j/spl les/n(a/sub ij/x/sup i/y/sup j/). Denote by Z(f) the set of points for which f(z)=0. Z(f) is the 2D curve represented by f(z). In this paper, we present a new approach to fitting 2D curves to data in the plane (or 3D surfaces to range data) which has significant advantages over presently known methods. It requires considerably less computation and the resulting curve can be forced to lie close to the data set at prescribed points provided that there is an nth degree polynomial that can reasonably approximate the data. Linear programming is used to do the fitting. The approach can incorporate a variety of distance measures and global geometric constraints.

关键词： Surface fitting Curve fitting Polynomials Linear programming Data engineering Databases Shape computer graphics Geometrical optics Object recognition

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BoostMap: A method for efficient approximate similarity rankings

BoostMap: A method for efficient approximate similarity rank...

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conference on computer vision and pattern recognition (cvpr)

作者： V. Athitsos J. Alon S. Sclaroff G. Kollios Computer Science Department Boston University Boston MA USA

This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, ID embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures: In both systems, in quantitative experiments, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings.

关键词： Image retrieval Image databases Information retrieval Database systems Time measurement Extraterrestrial measurements Weight measurement Machine learning Multidimensional systems Performance loss

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Making a Long Video Short: Dynamic Video Synopsis

Making a Long Video Short: Dynamic Video Synopsis

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conference on computer vision and pattern recognition (cvpr)

作者： A. Rav-Acha Y. Pritch S. Peleg Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

The power of video over still images is the ability to represent dynamic activities. But video browsing and retrieval are inconvenient due to inherent spatio-temporal redundancies, where some time intervals may have no activity, or have activities that occur in a small image region. Video synopsis aims to provide a compact video representation, while preserving the essential activities of the original video. We present dynamic video synopsis, where most of the activity in the video is condensed by simultaneously showing several actions, even when they originally occurred at different times. For example, we can create a "stroboscopic movie", where multiple dynamic instances of a moving object are played simultaneously. This is an extension of the still stroboscopic picture. Previous approaches for video abstraction addressed mostly the temporal redundancy by selecting representative key-frames or time intervals. In dynamic video synopsis the activity is shifted into a significantly shorter period, in which the activity is much denser. Video examples can be found online in http://***/synopsis

关键词： Birds Videoconference Object detection Legged locomotion Displays Layout Video sequences computer science Power engineering and energy Image retrieval

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Acceleration Strategies for Gaussian Mean-Shift Image Segmentation

Acceleration Strategies for Gaussian Mean-Shift Image Segmen...

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conference on computer vision and pattern recognition (cvpr)

作者： M.A. Carreira-Perpinan Department of Computer Science & Electrical Engineering OGI Oregon Health & Science University USA

Gaussian mean-shift (GMS) is a clustering algorithm that has been shown to produce good image segmentations (where each pixel is represented as a feature vector with spatial and range components). GMS operates by defining a Gaussian kernel density estimate for the data and clustering together points that converge to the same mode under a fixed-point iterative scheme. However, the algorithm is slow, since its complexity is O(kN2), where N is the number of pixels and k the average number of iterations per pixel. We study four acceleration strategies for GMS based on the spatial structure of images and on the fact that GMS is an expectation-maximisation (EM) algorithm: spatial discretisation, spatial neighbourhood, sparse EM and EM-Newton algorithm. We show that the spatial discretisation strategy can accelerate GMS by one to two orders of magnitude while achieving essentially the same segmentation; and that the other strategies attain speedups of less than an order of magnitude.

关键词： Acceleration Image segmentation Kernel Clustering algorithms Pixel Image converters Iterative algorithms Bandwidth computer science Equations

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Shape representation and recognition from curvature

Shape representation and recognition from curvature

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conference on computer vision and pattern recognition (cvpr)

作者： G. Dudek J.K. Tsotsos Research Centre for Intelligent Machines McGill University Montreal QUE Canada Department of Computer Science University of Toronto Toronto ONT Canada

An approach for describing objects for the purpose of recognition is developed. The authors deal with two key issues: building natural descriptions of curved objects, and making these descriptions compact and abstract. Typical examples of the types of curve one is able to describe as both qualitatively similar, yet discriminably different, are shown. Methods based on curvature extrema alone are likely to find three of the four of these shapes indistinguishable, while methods based on approaches such as shape templates may be oblivious to their similarity. The central ideas of the approach are outlined.< >

关键词： Shape Smoothing methods Robustness Art Interpolation Dynamic programming Target recognition Data mining Noise measurement Noise shaping

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