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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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Learning with constrained and unlabelled data

Learning with constrained and unlabelled data

引用

conference on computer vision and pattern recognition (cvpr)

作者： T. Lange M.H.C. Law A.K. Jain J.M. Buhmann Institute of Computational Science ETH Zurich Zurich Switzerland Department of Computer Science and Engineering Michigan State University East Lansing MI USA

Classification problems abundantly arise in many computer vision tasks eing of supervised, semi-supervised or unsupervised nature. Even when class labels are not available, a user still might favor certain grouping solutions over others. This bias can be expressed either by providing a clustering criterion or cost function and, in addition to that, by specifying pairwise constraints on the assignment of objects to classes. In this work, we discuss a unifying formulation for labelled and unlabelled data that can incorporate constrained data for model fitting. Our approach models the constraint information by the maximum entropy principle. This modeling strategy allows us (i) to handle constraint violations and soft constraints, and, at the same time, (ii) to speed up the optimization process. Experimental results on face classification and image segmentation indicates that the proposed algorithm is computationally efficient and generates superior groupings when compared with alternative techniques.

关键词： computer vision Cost function Image segmentation Labeling computer science Fitting Entropy Constraint optimization Face detection Object recognition

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Discriminating 3D faces by statistics of depth differences

Discriminating 3D faces by statistics of depth differences

引用

8th Asian conference on computer vision

作者： Huang, Yonggang Wang, Yunhong Tan, Tieniu Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9783540763895

In this paper, we propose an efficient 3D face recognition method based on statistics of range image differences. Each pixel value of range image represents normalized depth value of corresponding point on facial surface, and so depth differences between two range images' pixels of the same position on face can straightforwardly describe the differences between two faces' structures. Here, we propose to use histogram proportion of depth differences to discriminate intra and inter personal differences for 3D face recognition. Depth differences are computed from a neighbor district instead of direct subtraction to avoid the impact of non-precise registration. Furthermore, three schemes are proposed to combine the local rigid region(nose) and holistic face to overcome expression variation for robust recognition. Promising experimental results are achieved on the 3D dataset of FRGC2.0, which is the most challenging 3D database so far.

关键词： Face recognition

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Sensor planning for a trinocular active vision system

Sensor planning for a trinocular active vision system

引用

conference on computer vision and pattern recognition (cvpr)

作者： P. Lehel E.E. Hemayed A.A. Farag CVIP Lab University of Louisville KY USA

We present an algorithm to solve the sensor planning problem for a trinocular, active vision system. This algorithm uses an iterative optimization method to first solve for the translation between the three cameras an... 详细信息

关键词： Sensor systems Machine vision Cameras Robot vision systems Iterative algorithms System testing Robot sensing systems Humans Mechanical sensors Mechanical factors

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A tensor decomposition for geometric grouping and segmentation

A tensor decomposition for geometric grouping and segmentati...

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conference on computer vision and pattern recognition (cvpr)

作者： V.M. Govindu Visakhapatnam Andhra Pradesh India

While spectral clustering has been applied successfully to problems in computer vision, their applicability is limited to pairwise similarity measures that form a probability matrix. However many geometric problems with parametric forms require more than two observations to estimate a similarity measure, e.g. epipolar geometry. In such cases we can only define the probability of belonging to the same cluster for an n-tuple of points and not just a pair, leading to an n-dimensional probability tensor. However spectral clustering methods are not available for tensors. In this paper we present an algorithm to infer a similarity matrix by decomposing the n-dimensional probability tensor. Our method exploits the super-symmetry of the probability tensor to provide a randomised scheme that does not require the explicit computation of the probability tensor. Our approach is fast and accurate and its applicability is illustrated on two significant problems, namely perceptually salient geometric grouping and parametric motion segmentation (like affine, epipolar etc).

关键词： Tensile stress computer vision Clustering methods Matrix decomposition Information geometry Clustering algorithms Motion segmentation Laplace equations Venus Data analysis

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Automatic view recognition in echocardiogram videos using parts-based representation

Automatic view recognition in echocardiogram videos using pa...

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conference on computer vision and pattern recognition (cvpr)

作者： S. Ebadollahi Shih-Fu Chang H. Wu Department of Electrical Engineering Columbia University NY USA College of Phyisicians and Suregons Columbia University NY USA

Indexing echocardiogram videos at different levels of structure is essential for providing efficient access to their content for browsing and retrieval purposes. We present a novel approach for the automatic identification of the views of the heart from the content of the echocardiogram videos. In this approach the structure of the heart is represented by the constellation of its parts (chambers) under the different views. The statistical variations of the parts in the constellation and their spatial relationships are modeled using Markov random field models. A discriminative method is then used for view recognition, which fuses the assessments of a test image, by all the view-models. To the best of our knowledge, this is the first work addressing the analysis of the echocardiogram videos for the purpose of indexing their content. The method presented could be used for multiple-object recognition when their parts represent the objects and there are structural similarities between them.

关键词： Videos Heart Ultrasonic imaging Indexing Testing Ultrasonic transducers Educational institutions Content based retrieval Markov random fields Image recognition

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PCA-SIFT: a more distinctive representation for local image descriptors

PCA-SIFT: a more distinctive representation for local image ...

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conference on computer vision and pattern recognition (cvpr)

作者： Yan Ke R. Sukthankar School of Computer Science Carnegie Mellon University USA Intel Research Pittsburgh USA

Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid (June 2003) recently evaluated a variety of approaches and identified the SIFT [D. G. Lowe, 1999] algorithm as being the most resistant to common image deformations. This paper examines (and improves upon) the local image descriptor used by SIFT. Like SIFT, our descriptors encode the salient aspects of the image gradient in the feature point's neighborhood; however, instead of using SIFT's smoothed weighted histograms, we apply principal components analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation. We also present results showing that using these descriptors in an image retrieval application results in increased accuracy and faster matching.

关键词： Image retrieval computer vision Object recognition Principal component analysis Filters computer science Object detection Image registration Histograms Robustness

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3-D model-based tracking of humans in action: a multi-view approach

3-D model-based tracking of humans in action: a multi-view a...

引用

conference on computer vision and pattern recognition (cvpr)

作者： D.M. Gavrila L.S. Davis Computer Vision Laboratory CfAR University of Maryland College Park MD USA

We present a vision system for the 3-D model-based tracking of unconstrained human movement. Using image sequences acquired simultaneously from multiple views, we recover the 3-D body pose at each time instant without the use of markers. The pose-recovery problem is formulated as a search problem and entails finding the pose parameters of a graphical human model whose synthesized appearance is most similar to the actual appearance of the real human in the multi-view images. The models used for this purpose are acquired from the images. We use a decomposition approach and a best-first technique to search through the high dimensional pose parameter space. A robust variant of chamfer matching is used as a fast similarity measure between synthesized and real edge images. We present initial tracking results from a large new Humans-in-Action (HIA) database containing more than 2500 frames in each of four orthogonal views. They contain subjects involved in a variety of activities, of various degrees of complexity, ranging from the more simple one-person hand waving to the challenging two-person close interaction in the Argentine Tango.

关键词： Humans Biological system modeling pattern recognition computer vision Laboratories Educational institutions World Wide Web Machine vision Tracking Image sequences

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UMD Experiments with FRGC Data

UMD Experiments with FRGC Data

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： G. Aggarwal S. Biswas R. Chellappa Center for Automation Research University of Maryland College Park MD USA

Although significant work has been done in the field of face recognition, the performance of state-of-the art face recognition algorithms is not good enough to be effective in operational systems. Though most algorithms work well for controlled images, they are quite susceptible to changes in illumination and pose. Face recognition Grand Challenge (FRGC) is an effort to examine such issues to suitably guide future research in the area. This paper describes the efforts made at UMD in this direction. We present our results on several experiments suggested in FRGC. We believe that though pattern classification techniques play an extremely significant role in automatic face recognition under controlled conditions, physical modeling is required to generalize across varying situations. Accordingly, we describe a generative approach to recognize faces across varying illumination. Unlike most current methods, our method does not ignore shadows. Instead we use them to our benefit by modeling attached shadows in our formulation.

关键词： Face recognition Lighting Shape Automatic control Reflectivity Automation Educational institutions Art pattern classification Humans

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A fast linear shape from shading

A fast linear shape from shading

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conference on computer vision and pattern recognition (cvpr)

作者： P.-S. Tsai M. Shah Department of Computer Science University of Central Florida Orlando FL USA

A method for computing depth from a single shaded image is presented. Discrete approximations for p and q using finite differences are used, and the reflectance in Z/sub ij/ is linearized. The method is faster, since each operation is purely local. In addition, it gives good results for spherical surfaces, in contrast to other linear methods.< >

关键词： Shape Reflectivity Cost function Light sources Linearity computer science Iterative algorithms Councils Brightness Voting

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Multibody motion segmentation based on simulated annealing

Multibody motion segmentation based on simulated annealing

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conference on computer vision and pattern recognition (cvpr)

作者： Zhimin Fan Jie Zhou Ying Wu Department of Automation Tsinghua University China Department of ECE Northwestern University IL USA

The problem of multibody motion segmentation is an important and challenging issue in computer vision. In this paper, a novel segmentation technique based on simulated annealing (SA) is proposed. According to the fact that under linear projection models, feature points of multibody reside in multiple subspaces, firstly, a meaningful energy function is proposed, which favors the correct formation of those subspaces, and some subspaces are generated as the initial state. Then, two strategies of subspace evolution and transformation are developed to optimize the energy function in a manner of simulated annealing. The ultimate configuration of these subspaces will reveal the inherent multiple subspace structure embedded in the data space. The classification of data points to these subspaces is equivalent to multibody grouping. The global optimization process results in an increase of robustness with noise tolerance. The method is also effective in degenerate cases. Promising results on synthetic and real data are presented.

关键词： Motion segmentation computer vision Simulated annealing Computational modeling Automation Noise robustness Shape Clustering algorithms Application software Image segmentation

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