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Integrating multiple model views for object recognition

Integrating multiple model views for object recognition

引用

conference on computer vision and pattern recognition (cvpr)

作者： V. Ferrari T. Tuytelaars L. Van Gool ETH Zuerich Computer Vision Group (BIWI) Switzerland ESAT-PSI Katholieke Universiteit Leuven Belgium

We present a new approach to appearance-based object recognition, which captures the relationships between multiple model views and exploits them to improve recognition performance. The basic building block is local, viewpoint invariant regions. We propose an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces (GAMs). During modeling, the model views are connected by a large number of region-tracks, each aggregating image regions of a single physical region across the views. At recognition time, GAMs are constructed matching a test image to each model view. The consistency of configurations of GAMs is measured by exploiting the model connections. A genetic algorithm finds covering the object as completely as possible the most consistent configuration. Introducing GAMs as an intermediate grouping level facilitates decision-making and improves discriminative power. As a complementary application, we introduce a novel GAM-based two-view filter and demonstrate its effectiveness in recovering correct matches in the presence of up to 96% mismatches.

关键词： Object recognition Testing Power system modeling Voting computer vision Partitioning algorithms Image recognition Genetic algorithms Decision making Matched filters

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Separating style and content on a nonlinear manifold

Separating style and content on a nonlinear manifold

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Elgammal Chan-Su Lee Department of Computer Science Rutgers University New Brunswick NJ USA

Bilinear and multi-linear models have been successful in decomposing static image ensembles into perceptually orthogonal sources of variations, e.g., separation of style and content. If we consider the appearance of human motion such as gait, facial expression and gesturing, most of such activities result in nonlinear manifolds in the image space. The question that we address in this paper is how to separate style and content on manifolds representing dynamic objects. In this paper we learn a decomposable generative model that explicitly decomposes the intrinsic body configuration (content) as a function of time from the appearance (style) of the person performing the action as time-invariant parameter. The framework we present in this paper is based on decomposing the style parameters in the space of nonlinear functions which map between a learned unified nonlinear embedding of multiple content manifolds and the visual input space.

关键词： Legged locomotion Tensile stress Shape Humans Principal component analysis Head Image analysis computer science Biological system modeling Face recognition

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Automatic cascade training with perturbation bias

Automatic cascade training with perturbation bias

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jie Sun J.M. Rehg A. Bobick GVU Center / College of Computing Georgia Institute of Technology USA

Face detection methods based on cascade architecture have demonstrated fast and robust performance. Cascade learning is aided by the modularity of the architecture in which nodes are chained together to form a cascade. In this paper we present two new cascade learning results which address the decoupled nature of the cascade learning task. First, we introduce a cascade indifference curve framework, which connects the learning objectives for a node to the overall cascade performance. We derive a new cost function for node learning, which yields fully-automatic stopping conditions and improved detection performance. Second, we introduce the concept of perturbation bias, which leverages the statistical differences between target and non-target classes in a detection problem to obtain improved performance and robustness. We derive necessary and sufficient conditions for the success of the method and present experimental results.

关键词： Face detection computer architecture Robustness Cost function computer vision Detectors Sun Sufficient conditions Object detection pattern recognition

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Probabilistic parameter-free motion detection

Probabilistic parameter-free motion detection

引用

conference on computer vision and pattern recognition (cvpr)

作者： T. Veit F. Cao P. Bouthemy IRISA/INRIA Rennes France

We propose an original probabilistic parameter-free method for the detection of independently moving objects in an image sequence. We apply a probabilistic perceptual principle, the Helmholtz principle, whose main advantage is the automatization of the detection decision, by providing a light control of the number of false alarms. Not only does this method localize the moving objects but it also answers the preliminary question of the presence of motion. In particular the method works even when no assumption on motion presence is made. The algorithm is composed of three independent steps: estimation of the dominant image motion, spatial segmentation of object boundaries and independent motion detection itself We emphasize that none of these steps needs any parameter tuning. Results on real image sequences are reported and validate the proposed approach.

关键词： Motion detection Object detection Motion estimation Image sequences Image motion analysis Automatic control Image segmentation Cameras computer vision Motion analysis

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Flexible spatial models for grouping local image features

Flexible spatial models for grouping local image features

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Carneiro A.D. Jepson Department of Computer Science University of Toronto Toronto ONT Canada

A key step for the effective use of local image features (i.e., highly distinctive and robust features) for recognition or image matching is the appropriate grouping of feature matches. Spatial constraints are important in this grouping because, during a recognition process, they allow for the reduction of the number of hypotheses that must be verified and also reduce the number of false positives present in each of these hypotheses. A common choice for this grouping task is to use the Hough transform on the global spatial transformation parameters of the hypothesized matches. Here, instead, we use semi-local spatial constraints which allow for a greater range of shape deformations. A comparison with Hough transform shows that our method is more robust to both rigid and non-rigid deformations. Its functionality is demonstrated in an exemplar-based object recognition system that deals well with severe non-rigid deformations. We also show the efficacy of our flexible spatial grouping far long range motion problems.

关键词： Robustness Image databases Spatial databases Image recognition Object recognition Gabor filters Object detection computer science Image matching Shape

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Gibbs likelihoods for Bayesian tracking

Gibbs likelihoods for Bayesian tracking

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Roth L. Sigal M.J. Black Department of Computer Science Brown University Providence RI USA

Bayesian methods for visual tracking model the likelihood of image measurements conditioned on a tracking hypothesis. Image measurements may, for example, correspond to various filter responses at multiple scales and orientations. Most tracking approaches exploit ad hoc likelihood models while those that exploit more rigorous, learned, models often make unrealistic assumptions about the underlying probabilistic model. Such assumptions cause problems for Bayesian inference when an unsound likelihood is combined with an a priori probability distribution. Errors in modeling the likelihood can lead to brittle tracking results, particularly when using non-parametric inference techniques such as particle filtering. We show how assumptions of conditional independence of filter responses are violated in common tracking scenarios, lead to incorrect likelihood models, and cause problems for Bayesian inference. We address the problem of modeling more principled likelihoods using Gibbs learning. The learned models are compared with naive Bayes methods which assume conditional independence of the filter responses. We show how these Gibbs models can be used as an effective image likelihood, and demonstrate them in the context of particle filter-based human tracking.

关键词： Bayesian methods computer society computer vision pattern recognition

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Graphical models for graph matching

Graphical models for graph matching

引用

conference on computer vision and pattern recognition (cvpr)

作者： T.S. Caetano T. Caelli D.A.C. Barone Instituto de Informática Universidade Federal do Rio Grande do Sul Porto Alegre Rio Grande do Sul Brazil Department of Computing Science University of Alberta Edmonton AB Canada

This paper explores a formulation for attributed graph matching as an inference problem over a hidden Markov random field. We approximate the fully connected model with simpler models in which optimal inference is feasible, and contrast them to the well-known probabilistic relaxation method, which can operate over the complete model but does not assure global optimality. The approach is well suited for applications in which there is redundancy in the binary attributes of the graph, such as in the matching of straight line segments. Results demonstrate that, in this application, the proposed models have superior robustness over probabilistic relaxation under additive noise conditions.

关键词： Graphical models Hidden Markov models Least squares approximation pattern recognition Context modeling Dynamic programming Layout Relaxation methods Noise robustness Additive noise

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Window-based, discontinuity preserving stereo

Window-based, discontinuity preserving stereo

引用

conference on computer vision and pattern recognition (cvpr)

作者： M. Agrawal L.S. Davis SRI International Inc. Menlo Park CA USA Department of Computer Science University of Maryland College Park MD USA

Traditionally, the problem of stereo matching has been addressed either by a local window-based approach or a dense pixel-based approach using global optimization. In this paper we present an algorithm which combines window-based local matching into a global optimization framework. Our local matching algorithm assumes that local windows can have at most two disparities. Under this assumption, the local matching can be performed very efficiently using graph cuts. The global matching is formulated as minimization of an energy term that takes into account the matching constraints induced by the local stereo algorithm. Fast, approximate minimization of this energy is achieved through graph cuts. The key feature of our algorithm is that it preserves discontinuities both during the local as well as global matching phase.

关键词： Stereo vision Layout computer vision Interpolation Iterative algorithms computer science Educational institutions Minimization methods Application software Light sources

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Statistical feature fusion for gait-based human recognition

Statistical feature fusion for gait-based human recognition

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ju Han B. Bhanu Center for Research in Intelligent Systems University of California Riverside CA USA

This paper presents a novel approach for human recognition by combining statistical gait features from real and synthetic templates. Real templates are directly computed from training silhouette sequences, while synthetic templates are generated from training sequences by simulating silhouette distortion. A statistical feature extraction approach is used for learning effective features from real and synthetic templates. Features learned from real templates characterize human walking properties provided in training sequences, and features learned from synthetic templates predict gait properties under other conditions. A feature fusion strategy is therefore applied at the decision level to improve recognition performance. We apply the proposed approach to USF HumanID Database. Experimental results demonstrate that the proposed fusion approach not only achieves better performance than individual approaches, but also provides large improvement in performance with respect to the baseline algorithm.

关键词： Humans Biometrics Biological system modeling Legged locomotion Fingerprint recognition Character recognition pattern recognition Frequency measurement Phase measurement Intelligent systems

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A model for dynamic shape and its applications

A model for dynamic shape and its applications

引用

conference on computer vision and pattern recognition (cvpr)

作者： Che-Bin Liu N. Ahuja Beckman Institute University of Illinois Urbana-Champaign Urbana-Champaign IL USA

Variation in object shape is an important visual cue for deformable object recognition and classification. In this paper, we present an approach to model gradual changes in the 2-D shape of an object. We represent 2-D region shape in terms of the spatial frequency content of the region contour using Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the shape changes. Specifically, we use autoregressive model of the coefficient series. We demonstrate the efficacy of the model on several applications. First, we use the model parameters as discriminating features for object recognition and classification. Second, we show the use of the model for synthesis of dynamic shape using the model learned from a given image sequence. Third, we show that, with its capability of predicting shape, the model can be used to predict contours of moving regions, which can be used as initial estimates for the contour, based tracking methods.

关键词： Shape Predictive models Object recognition Fires Video sequences Image sequences computer vision Fourier transforms Algorithm design and analysis Parameter estimation

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