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Video epitomes

Video epitomes

引用

conference on computer vision and pattern recognition (cvpr)

作者： V. Cheung B.J. Frey N. Jojic Electrical and Computer Engineering University of Toronto Toronto ONT Canada Machine Learning and Applied Statistics Microsoft Research Limited Redmond WA USA

Recently, "epitomes" were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. In this paper, we describe how epitomes can be used to model video data and we describe significant computational speedups that can be incorporated into the epitome inference and learning algorithm. In the case of videos, epitomes are estimated so as to model most of the small space-time cubes from the input data. Then, the epitome can be used for various modeling and reconstruction tasks, of which we show results for video super-resolution, video interpolation, and object removal. Besides computational efficiency, an interesting advantage of the epitome as a representation is that it can be reliably estimated even from videos with large amounts of missing data. We illustrate this ability on the task of reconstructing the dropped frames in video broadcast using only the degraded video.

关键词： Statistics Video compression Probability Image reconstruction Optical computing Data analysis Libraries Spatial resolution Image resolution Biological system modeling

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Shape matching and object recognition using low distortion correspondences

Shape matching and object recognition using low distortion c...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A.C. Berg T.L. Berg J. Malik Department of Electrical Engineering and Computer Science University of California Berkeley USA

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.

关键词： Shape Object recognition computer vision Algorithms Deformable models Quadratic programming Cost function Spline Nearest neighbor searches Face detection

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Hallucinating faces: TensorPatch super-resolution and coupled residue compensation

Hallucinating faces: TensorPatch super-resolution and couple...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Wei Liu Dahua Lin Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China Microsoft Research Asia Beijing China

ISBN: (纸本)0769523722

In this paper, we propose a new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models. Considering that image patches reflect the combined effect of personal characteristics and patch-location, we first formulate a TensorPatch model based on multilinear analysis to explicitly model the interaction between multiple constituent factors. Motivated by locally linear embedding, we develop an enhanced multilinear patch hallucination algorithm, which efficiently exploits the local distribution structure in the sample space. To better preserve face subtle details, we derive the coupled PCA algorithm to learn the relation between high-resolution residue and low-resolution residue, which is utilized for compensate the error residue in hallucinated images. Experiments demonstrate that our framework on one hand well maintains the global facial structures, on the other hand recovers the detailed facial traits in high quality.

关键词： Tensile stress Face detection Image resolution Principal component analysis Inference algorithms Algebra Asia Image analysis Humans Rendering (computer graphics)

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The modified pbM-estimator method and a runtime analysis technique for the RANSAC family

The modified pbM-estimator method and a runtime analysis tec...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Rozenfeld I. Shimshoni Department of Industrial Engineering and Management Technion Israel Institute of Technology-Technion Haifa Israel

Robust regression techniques are used today in many computer vision algorithms. Chen and Meer recently presented a new robust regression technique named the projection based M-estimator. Unlike other methods in the RANSAC family of techniques, where performance depends on a user supplied scale parameter, in the pbM-estimator technique this scale parameter is estimated automatically from the data using kernel smoothing density estimation. In this work we improve the performance of the pbM-estimator by changing its cost function. Replacing the cost function of the pbM-estimator with the changed one yields the modified pbM-estimator. The cost function of the modified pbM-estimator is more stable relative to the scale parameter and is also a better classifier. Thus we get a more robust and effective technique. A new general method to estimate the runtime of robust regression algorithms is proposed. Using it we show, that the modified pbM-estimator runs 2 -3 times faster than the pbM-estimator. Experimental results of fundamental matrix estimation are presented demonstrating the correctness of the proposed analysis method and the advantages of the modified pbM-estimator.

关键词： Runtime Robustness Cost function Testing computer vision Sampling methods Industrial engineering Engineering management Parameter estimation Kernel

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Chaotic search of the object for robot vision

Chaotic search of the object for robot vision

引用

SICE Annual conference 2005

作者： Imamura, Satoshi Asakura, Toshiyuki University of Fukui Bunkyo 3-9-1 Fukui City Fukui Japan

This research in concerned with image recognition on a robot vision by using Chaotic Search. As a nonlinear dynamical system, BVP model is treated, which shows the behavior of neuron in biological system. This model has the edge of chaos, which exists on the boundary between a periodic solution and a chaos solution. This edge of chaos is an important area to maintain a flexible organization. In this research, the Chaotic Search is applied to image recognition utilizing the edge of chaos. First, the occurrence of chaos in BVP model is examined using the Lyapunov exponent. Second, in order to perform image recognition, a method of Chaotic Search is proposed in which it is distinguished whether it is a target, using a method of pattern matching. Finally, through simulation studies, it is verified that Chaotic Search is possible both for fixed and moving targets. © 2005 SICE.

关键词： computer vision

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Hybrid models for human motion recognition

Hybrid models for human motion recognition

引用

conference on computer vision and pattern recognition (cvpr)

作者： C. Fanti L. Zelnik-Manor P. Perona Department of Electrical Engineering California Institute of Technology Pasadena CA USA

Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the human motion model as a triangulated graph. Previous approaches learned models based just on positions and velocities of the body parts while ignoring their appearance. Moreover, a heuristic approach was commonly used to obtain translation invariance. In this paper we suggest an improved approach for learning such models and using them for human motion recognition. The suggested approach combines multiple cues, i.e., positions, velocities and appearance into both the learning and detection phases. Furthermore, we introduce global variables in the model, which can represent global properties such as translation, scale or view-point. The model is learned in an unsupervised manner from unlabelled data. We show that the suggested hybrid probabilistic model (which combines global variables, like translation, with local variables, like relative positions and appearances of body parts), leads to: (i) faster convergence of learning phase, (it) robustness to occlusions, and, (Hi) higher recognition rate.

关键词： Humans Biological system modeling Video sequences Convergence Robustness Phase detection Motion detection Joints Maximum likelihood detection Maximum likelihood estimation

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A new active contour method based on elastic interaction

A new active contour method based on elastic interaction

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yang Xiang A.C.S. Chung Jian Ye Department of Mathematics Hong Kong University of Science and Technology Hong Kong China

Image segmentation is defined as partitioning an image into non-overlapping regions based on the intensity or texture. The active contour methods provide an effective way for segmentation, in which the boundary of an object (usually with large image gradient value) is detected by an evolving curve. But, these methods have limitations due to the fact that real images may have objects with complex geometric structures and shapes, and are often corrupted by noise. Developing more robust and accurate active contour methods has been an active research area since the idea of the methods was proposed. In this paper, we propose a new active contour method and apply the method to medical image segmentation. This new method uses a long-ranged interaction between image boundaries and the moving curves, which is inspired by the elastic interaction between line defects in solids (dislocations). The new method is more efficient and effective, especially in detecting thin, weak and blurred structures such as the images of blood vessels.

关键词： Active contours Image segmentation Level set Biomedical imaging Equations Mathematics computer science Object detection Shape Noise shaping

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Polarization multiplexing for bidirectional imaging

Polarization multiplexing for bidirectional imaging

引用

conference on computer vision and pattern recognition (cvpr)

作者： O.G. Cula K.J. Dana D.K. Pai D. Wang Computer Science Department Rutgers University Piscataway NJ USA

Our goal is to incorporate polarization in appearance-based modeling in an efficient and meaningful way. Polarization has been used in numerous prior studies for separating diffuse and specular reflectance components, but in this work we show that it also can be used to separate surface reflectance contributions from individual light sources. Our approach is called polarization multiplexing and it has significant impact in appearance modeling and bidirectional imaging where the image as a function of illumination direction is needed. Multiple unknown light sources can illuminate the scene simultaneously, and the individual contributions to the overall surface reflectance can be estimated. To develop the method of polarization multiplexing, we use a relationship between light source direction and intensity modulation. Inverting this transformation enables the individual intensity contributions to be estimated. In addition to polarization multiplexing, we show that phase histograms from the intensity modulations can be used to estimate scene properties including the number of light sources.

关键词： Optical polarization Light sources Reflectivity Lighting Intensity modulation Optical modulation Layout Cameras Rough surfaces Surface roughness

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Nonlinear face recognition based on maximum average margin criterion

Nonlinear face recognition based on maximum average margin c...

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conference on computer vision and pattern recognition (cvpr)

作者： Baochang Zhang Xilin Chen Shiguang Shan Wen Gao Computer School Harbin Institute of Technology China ICT-ISVISION Joint Research and Development Laboratory for face recognition ICT Chinese Academy and Sciences China

ISBN: (纸本)0769523722

This paper proposes a novel nonlinear discriminant analysis method named by kernerlized maximum average margin criterion (KMAMC), which has combined the idea of support vector machine with the kernel fisher discriminant analysis (KFD). We also use a simple method to prove the relationship between both kernel methods. The difference of KMAMC from traditional KFD methods include: (1) the within-class and between-class scatter matrices are computed based on the support vectors instead of all the samples; (2) multiple centers are exploited instead of the single center in computing the two scatter matrices; (3) the discriminant criteria is formulated as subtracting the trace of within-class scatter matrix from that of the between-class scatter matrix, therefore, the tedious singularity problem is avoided. These features have made KMAMC more practical for real-world applications. Our experiments on two face databases, the FERET and CAS-PEAL face database, have illustrated its excellent performance compared with some traditional methods such as Eigenface, Fisherface, and KFD.

关键词： Face recognition Scattering Kernel Principal component analysis Support vector machines Spatial databases Feature extraction Research and development Content addressable storage Linear discriminant analysis

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Probabilistic kernels for the classification of auto-regressive visual processes

Probabilistic kernels for the classification of auto-regress...

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conference on computer vision and pattern recognition (cvpr)

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

We present a framework for the classification of visual processes that are best modeled with spatio-temporal autoregressive models. The new framework combines the modeling power of a family of models known as dynamic textures and the generalization guarantees, for classification, of the support vector machine classifier. This combination is achieved by the derivation of a new probabilistic kernel based on the Kullback-Leibier divergence (KL) between Gauss-Markov processes. In particular, we derive the KL-kernel for dynamic textures in both 1) the image space, which describes both the motion and appearance components of the spatio-temporal process, and 2) the hidden state space, which describes the temporal component alone. Together, the two kernels cover a large variety of video classification problems, including the cases where classes can differ in both appearance and motion and the cases where appearance is similar for all classes and only motion is discriminant. Experimental evaluation on two databases shows that the new classifier achieves superior performance over existing solutions.

关键词： Kernel Support vector machines Support vector machine classification State-space methods Gaussian processes Visual databases Layout Tracking Fires Educational institutions

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