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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

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Subspace segmentation with outliers: a Grassmannian approach to the maximum consensus subspace

Subspace segmentation with outliers: a Grassmannian approach...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Nuno Pinho da Silva Joao Paulo Costeira ISR-Instituto Superior Técnico Lisboa Portugal

Segmenting arbitrary unions of linear subspaces is an important tool for computer vision tasks such as motion and image segmentation, SfM or object recognition. We segment subspaces by searching for the orthogonal complement of the subspace supported by the majority of the observations, i.e., the maximum consensus subspace. It is formulated as a grassmannian optimization problem: a smooth, constrained but nonconvex program is immersed into the Grassmann manifold, resulting in a low dimensional and unconstrained program solved with an efficient optimization algorithm. Nonconvexity implies that global optimality depends on the initialization. However, by finding the maximum consensus subspace, outlier rejection becomes an inherent property of the method. Besides robustness, it does not rely on prior global detection procedures (e.g., rank of data matrices), which is the case of most current works. We test our algorithm in both synthetic and real data, where no outlier was ever classified as inlier.

关键词： Image segmentation computer vision Constraint optimization Object recognition Robustness Testing Null space MODIS Vectors Optimization methods

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Extrinsic and Depth Calibration of ToF-cameras

Extrinsic and Depth Calibration of ToF-cameras

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Stefan Fuchs Gerd Hirzinger Institute of Robotics and Mechatronics DLR Germany

Recently, ToF-cameras have attracted attention because of their ability to generate a full 21/2D depth image at video frame rates. thus, ToF-cameras are suitable for real-time 3D tasks such as tracking, visual servoing or object pose estimation. the usability of such systems mainly depends on an accurate camera calibration. In this work a calibration process for ToF-cameras with respect to the intrinsic parameters, the depth measurement distortion and the pose of the camera relative to a robot's endeffector is described. the calibration process is not only based on the monochromatic images of the camera but also uses its depth values that are generated from a chequer-board pattern. the robustness and precision of the presented method is assessed applying it to randomly selected shots and comparing the calibrated measurements to a ground truth obtained from a laser scanner.

关键词： Calibration Cameras Robot vision systems Optical distortion Optical sensors Distortion measurement Time measurement Object detection Image processing Layout

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Modeling the structure of multivariate manifolds: Shape Maps

Modeling the structure of multivariate manifolds: Shape Maps

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Georg Langs Nikos Paragios Laboratoire MAS Ecole Centrale de Paris Equipe GALEN INRIA Saclay-Ile-de-France Chatenay-Malabry France

We propose a shape population metric that reflects the interdependencies between points observed in a set of examples. It provides a notion of topology for shape and appearance models that represents the behavior of individual observations in a metric space, in which distances between points correspond to their joint modeling properties. A Markov chain is learnt using the description lengths of models that describe sub sets of the entire data. the according diffusion map or shape map provides for the metric that reflects the behavior of the training population. With this metric functional clustering, deformation- or motion segmentation, sparse sampling and the treatment of outliers can be dealt with in a unified and transparent manner. We report experimental results on synthetic and real world data and compare the framework with existing specialized approaches.

关键词： Shape Topology Brain modeling Deformable models Buildings Motion segmentation computer vision Biomedical imaging Image segmentation Extraterrestrial measurements

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A Polynomial-Time Bound for Matching and Registration with Outliers

A Polynomial-Time Bound for Matching and Registration with O...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Carl Olsson Olof Enqvist Fredrik Kahl Centre for Mathematical Sciences Lund University Sweden

We present a framework for computing optimal transformations, aligning one point set to another, in the presence of outliers. Example applications include shape matching and registration (using, for example, similarity, affine or projective transformations) as well as multiview reconstruction problems (triangulation, camera pose etc.). While standard methods like RANSAC essentially use heuristics to cope with outliers, we seek to find the largest possible subset of consistent correspondences and the globally optimal transformation aligning the point sets. Based on theory from computational geometry, we show that this is indeed possible to accomplish in polynomial-time. We develop several algorithms which make efficient use of convex programming. the scheme has been tested and evaluated on both synthetic and real data for several applications.

关键词： Polynomials Shape Image reconstruction Cameras Computational geometry Application software Councils Testing computer vision Algorithms

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Coherent Laplacian 3-D protrusion segmentation

Coherent Laplacian 3-D protrusion segmentation

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Fabio Cuzzolin Diana Mateus David Knossow Edmond Boyer Radu Horaud INRIA Rhone-Alpes Montbonnot France

In this paper, an analysis of locally linear embedding (LLE) in the context of clustering is developed. As LLE conserves the local affine coordinates of points, shape protrusions as high-curvature regions of the surface are preserved. Also, LLE's covariance constraint acts as a force stretching those protrusions and making them wider separated and lower dimensional. A novel scheme for unsupervised body-part segmentation along time sequences is thus proposed in which 3-D shapes are clustered after embedding. Clusters are propagated in time, and merged or split in an unsupervised fashion to accommodate changes of the body topology. Comparisons on synthetic, and real data with ground truth, are run with direct segmentation in 3-D by EM clustering and ISOMAP-based clustering. Robust-ness and the effects of topology transitions are discussed.

关键词： Laplace equations Shape Topology Robustness Humans Biological system modeling Clustering algorithms Motion analysis computer vision Application software

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Globally Optimal Shape-based Tracking in Real-time

Globally Optimal Shape-based Tracking in Real-time

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： thomas Schoenemann Daniel Cremers Department of Computer Science University of Bonn Germany

Most algorithms for real-time tracking of deformable shapes provide sub-optimal solutions for a suitable energy minimization task: the search space is typically considered too large to allow for globally optimal solutions. In this paper we show that - under reasonable constraints on the object motion - one can guarantee global optimality while maintaining real-time requirements. the problem is cast as finding the optimal cycle in a graph spanned by the prior template and the image. the underlying combinatorial algorithm is implemented on state-of-the-art graphics hardware. Solutions on FPGAs are conceivable. Experimental results demonstrate long-term tracking of cars in real-time, while coping with challenging weather conditions. In particular, we show that the proposed tracking algorithm is highly robust to illumination changes and that it outperforms local tracking methods such as the level set method.

关键词： Shape computer vision Image segmentation Runtime Level set Optimization methods computer Society pattern recognition computer science Minimization methods

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Discriminative Human Action Segmentation and recognition using Semi-Markov Model

Discriminative Human Action Segmentation and Recognition usi...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Qinfeng Shi Li Wang Li Cheng Alex Smola NICTA Australian National University Canberra Australia South-East University Nanjing China

Given an input video sequence of one person conducting a sequence of continuous actions, we consider the problem of jointly segmenting and recognizing actions. We propose a discriminative approach to this problem under a semi-Markov model framework, where we are able to define a set of features over input-output space that captures the characteristics on boundary frames, action segments and neighboring action segments, respectively. In addition, we show that this method can also be used to recognize the person who performs in this video sequence. A Viterbi-like algorithm is devised to help efficiently solve the induced optimization problem. Experiments on a variety of datasets demonstrate the effectiveness of the proposed method.

关键词： Humans Video sequences Hidden Markov models Support vector machines Shape Surveillance Inference algorithms Character recognition Tracking

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An Importance Sampling Approach to Learning Structural Representations of Shape

An Importance Sampling Approach to Learning Structural Repre...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Andrea Torsello Dipartimento di informatica Università E2809CCaFoscariE2809D di Venezia Italy

this paper addresses the problem of learning archetypal structural models from examples. this is done by providing a generative model for graphs where the distribution of observed nodes and edges is governed by a set of independent Bernoulli trials with parameters to be estimated, however, the correspondences between sample node and model nodes is not known and must be estimated from local structure. the parameters are estimated maximizing the likelihood of the observed graphs, marginalizing it over all possible node correspondences. this is done adopting an importance sampling approach to limit the exponential explosion of the set of correspondences. the approach is used to summarize the variation in two different structural abstraction of shape: Delaunay graph over a set of image features and shock graphs. the experiments show that the approach can be used to recognize structures belonging to a same class.

关键词： Monte Carlo methods Shape Parameter estimation Electric shock Training data Graphical models Explosions computer vision Layout Concrete

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Hybrid Body Representation for Integrated Pose recognition, Localization and Segmentation

Hybrid Body Representation for Integrated Pose Recognition, ...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Cheng Chen Guoliang Fan School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

We propose a hybrid body representation that represents each typical pose by both template-like view information and part-based structural information. Specifically, each body part as well as the whole body are represented by an off-line learned shape model where both region-based and edge-based priors are combined in a coupled shape representation. Part-based spatial priors are represented by a "star" graphical model. this hybrid body representation can synergistically integrate pose recognition, localization and segmentation into one computational flow. Moreover, as an important step for feature extraction and model inference, segmentation is involved in the low-level, mid-level and high-level vision stages, where top-down prior knowledge and bottom-up data processing is well integrated via the proposed hybrid body representation.

关键词： Shape Image segmentation Humans Biological system modeling Feature extraction Deformable models Image recognition Object recognition Graphical models Data processing

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Enforcing Non-Positive Weights for Stable Support Vector Tracking

Enforcing Non-Positive Weights for Stable Support Vector Tra...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Simon Lucey Robotics Institute Carnegie Mellon University USA

In this paper we demonstrate that the support vector tracking (SVT) framework first proposed by Avidan is equivalent to the canonical Lucas-Kanade (LK) algorithm with a weighted Euclidean norm. From this equivalence we empirically demonstrate that in many circumstances the canonical SVT approach is unstable, and characterize these circumstances theoretically. We then propose a novel "non-positive support kernel machine" (NSKM) to circumvent this limitation and allow the effective use of discriminative classification within the weighted LK framework. this approach ensures that the pseudo-Hessian realized within the weighted LK algorithm is positive semidefinite which allows for fast convergence and accurate alignment/tracking. A further benefit of our proposed method is that the NSKM solution results in a much sparser kernel machine than the canonical SVM leading to sizeable computational savings and much improved alignment performance.

关键词： Kernel Support vector machines Iterative algorithms Principal component analysis computer vision Employment Robots Costs Linear approximation Parameter estimation

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