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Kinematic jump processes for monocular 3D human tracking

Kinematic jump processes for monocular 3D human tracking

引用

conference on computer Vision and pattern recognition (CVPR)

作者： C. Sminchisescu B. Triggs Department of Computer Science Artificial Intelligence Laboratory University of Toronto Toronto Canada INRIA Rhône-Alpes GRAVIR Montbonnot Saint Martin France

A major difficulty for 3D (three-dimensional) human body tracking from monocular image sequences is the near nonobservability of kinematic degrees of freedom that generate motion in depth. For known link (body segment) lengths, the strict nonobservabilities reduce to twofold 'forwards/backwards flipping' ambiguities for each link. These imply 2/sup # links/ formal inverse kinematics solutions for the full model, and hence linked groups of O(2/sup # links/) local minima in the model-image matching cost function. Choosing the wrong minimum leads to rapid mistracking, so for reliable tracking, rapid methods of investigating alternative minima within a group are needed. Previous approaches to this have used generic search methods that do not exploit the specific problem structure. Here, we complement these by using simple kinematic reasoning to enumerate the tree of possible forwards/backwards flips, thus greatly speeding the search within each linked group of minima. Our methods can be used either deterministically, or within stochastic 'jump-diffusion' style search processes. We give experimental results on some challenging monocular human tracking sequences, showing how the new kinematic-flipping based sampling method improves and complements existing ones.

关键词： Kinematics Humans Search methods Sampling methods image sequences Cost function Particle tracking Filtering Constraint optimization Annealing

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Scene detection in Hollywood movies and TV shows

Scene detection in Hollywood movies and TV shows

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conference on computer Vision and pattern recognition (CVPR)

作者： Z. Rasheed M. Shah School of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

A scene can be defined as one of the subdivisions of a play in which the setting is fixed, or when it presents continuous action in one place. We propose a novel two-pass algorithm for scene boundary detection, which utilizes the motion content, shot length and color properties of shots as the features. In our approach, shots are first clustered by computing Backward Shot Coherence (BSC) - a shot color similarity measure that detects Potential Scene Boundaries (PSBs) in the videos. In the second pass we compute Scene Dynamics (SD), a function of shot length and the motion content in the potential scenes. In this pass, a scene merging criteria has been developed to remove weak PSBs in order to reduce over segmentation. We also propose a method to describe the content of each scene by selecting one representative image. The segmentation of video data into number of scenes facilitates an improved browsing of videos in electronic form, such as video on demand, digital libraries, Internet. The proposed algorithm has been tested on a variety of videos that include five Hollywood movies, one sitcom, and one interview program and promising results have been obtained.

关键词： Layout Motion pictures TV Gunshot detection systems image segmentation Clustering algorithms Motion detection Merging Video on demand Software libraries

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image compression in real-time multiprocessor systems using divisive K-means clustering

Image compression in real-time multiprocessor systems using ...

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International conference on Integration of Knowledge Intensive Multi-Agent Systems (KIMAS)

作者： D. Fradkin I.B. Muchnik S. Streltsov Department of Computer Science State University of New Jersey Rutgers Piscataway NJ USA DIMACS State University of New Jersey Rutgers Piscataway NJ USA Mercury Computer Systems Inc. Chelmsford MA USA

In recent years, clustering became one of the fundamental methods of large dataset analysis. In particular, clustering is an important component of real-time image compression and exploitation algorithms, such as vector quantization, segmentation of SAR, EO/IR, and hyperspectral imagery, group tracking, and behavior pattern analysis. Thus, development of fast scalable real-time clustering algorithms is important to enable exploitation of imagery coming from surveillance and reconnaissance airborne platforms. Clustering methods are widely used in pattern recognition, data compression, data mining, but the problem of using them in real-time systems has not been a focus of most algorithm designers. We describe a practical clustering procedure that is designed specifically for compression of 2D images and can satisfy stringent requirements of real-time onboard processing.

关键词： image coding Real time systems Multiprocessing.systems Clustering algorithms Data analysis Vector quantization image segmentation Hyperspectral imaging pattern analysis Surveillance

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Object removal by exemplar-based inpainting

Object removal by exemplar-based inpainting

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conference on computer Vision and pattern recognition (CVPR)

作者： A. Criminisi P. Perez K. Toyama Microsoft Research Limited Cambridge UK Microsoft Corporation Redmond WA USA

A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: (i) "texture synthesis" algorithms for generating large image regions from sample textures, and (ii) "inpainting" techniques for filling in small image gaps. The former work well for "textures" - repeating two dimensional patterns with some stochasticity; the latter focus on linear "structures" which can be thought of as one dimensional patterns, such as lines and object contours. This paper presents a novel and efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. Computational efficiency is achieved by a block-based sampling process. A number of examples on real and synthetic images demonstrate the effectiveness of our algorithm in removing large occluding objects as well as thin scratches. Robustness with respect to the shape of the manually selected target region is also demonstrated. Our results compare favorably to those obtained by existing techniques.

关键词： Filling image restoration Digital images image generation Computational efficiency image sampling Robustness Shape Humans Sampling methods

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Line reconstruction from many perspective images by factorization

Line reconstruction from many perspective images by factoriz...

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conference on computer Vision and pattern recognition (CVPR)

作者： D. Matinec T. Pajdla Dept. of Cybernetics Czech Technical University in Prague Prague Czech Rep

This paper proposes a method for line reconstruction from many perspective images by factorization of a matrix containing line correspondences. No point correspondences are used. We formulate the reconstruction from line correspondences in the language of Plucker line coordinates. The reconstruction is posed as the factorization of 3m /spl times/ n matrix S into the product S = QL of 3m /spl times/ 6 projection matrix Q and 6 /spl times/ n line matrix L, both satisfying Klein identities. The matrix S contains coordinates of lines detected in perspective images. Similarly to reconstruction from point correspondences in perspective images, the matrix S has to be properly rescaled before it can be factorized. We propose a scaling of image line coordinates based on trifocal tensors that are analogical to the scaling proposed by Sturm and Triggs (1996) for points. We propose an SVD based factorization enforcing Klein identities on Q and L in a noise-free situation. We show experiments on real data that suggest that a good reconstruction may be obtained even if data is noisy and the identities are not enforced exactly. We also discuss an extension of the method for images with occlusions.

关键词： image reconstruction Jacobian matrices Cameras Layout Robustness Cybernetics Tensile stress Geometry Equations

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Simultaneous estimation of left ventricular motion and material properties with maximum a posteriori strategy

Simultaneous estimation of left ventricular motion and mater...

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conference on computer Vision and pattern recognition (CVPR)

作者： Huafeng Liu Pengcheng Si Department of Electrical and Electronic Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Hong Kong University of Science and Technology Kowloon HK Dept. of Electr. & Electron. Eng. Hong Kong Univ. of Sci. & Technol. Kowloon China

ISBN: (纸本)0769519008

In addition to its technical merits as a challenging nonrigid motion and structural integrity analysis problem, quantitative estimation of cardiac regional functions and material characteristics has significant physiological and clinical values. We earlier developed a stochastic finite element framework for the simultaneous estimation of myocardial motion and material parameters from medical image sequences with an extended Kalman filter approach. In this paper, we present a computational strategy for the framework based upon the maximum a posteriori estimation principles, realized through the extended Kalman smoother, that produce a sequence of kinematics state and material parameter estimates from the entire sequence of observations. The system dynamics equations of the heart is constructed using a biomechanical model with stochastic parameters, and the tissue material and deformation parameters are jointly estimated from the periodic imaging data. Experiments with canine magnetic resonance images have been conducted with very promising results, as validated through comparison to the histological staining of post mortem myocardium.

关键词： Motion estimation Material properties Myocardium Biological materials Conducting materials Magnetic materials Parameter estimation Motion analysis image motion analysis Stochastic processes

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Flux invariants for shape

Flux invariants for shape

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conference on computer Vision and pattern recognition (CVPR)

作者： P. Dimitrov J.N. Damon K. Siddiqi School of Computer Science McGill University Montreal Canada Department of Mathematics Chapel Hill North Carolina State University Chapel Hill NC USA

We consider the average outward flux through a Jordan curve of the gradient vector field of the Euclidean distance function to the boundary of a 2D shape. Using an alternate form of the divergence theorem, we show that in the limit as the area of the region enclosed by such a curve shrinks to zero, this measure has very different behaviors at medial points than at non-medial ones, providing a theoretical justification for its use in the Hamilton-Jacobi skeletonization algorithm of Siddiqi et al. (2002). We then specialize to the case of shrinking circular neighborhoods and show that the average outward flux measure also reveals the object angle at skeletal points. Hence, formulae for obtaining the boundary curves, their curvatures, and other geometric quantities of interest, can be written in terms of the average outward flux limit values at skeletal points. Thus this measure can be viewed as a Euclidean invariant for shape description: it can be used to both detect the skeleton from the Euclidean distance function, as well as to explicitly reconstruct the boundary from it. We illustrate our results with several numerical simulations.

关键词： Euclidean distance Skeleton Shape measurement Numerical simulation computer vision computer science Mathematics Area measurement image reconstruction Biomedical imaging

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The optimal distance measure for object detection

The optimal distance measure for object detection

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conference on computer Vision and pattern recognition (CVPR)

作者： S. Mahamud M. Hebert Department of Computer Science Carnegie Mellon University Pittsburgh PA USA

We develop a multi-class object detection framework whose core component is a nearest neighbor search over object part classes. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the misclassification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input image measurements belong to different classes. In practice, we model the optimal distance measure using a linear logistic model that combines the discriminative powers of more elementary distance measures associated with a collection of simple to construct feature spaces like color, texture and local shape properties. Furthermore, in order to perform search over large training sets efficiently, the same framework was extended to find hamming distance measures associated with simple discriminators. By combining this discrete distance model with the continuous model, we obtain a hierarchical distance model that is both fast and accurate. Finally, the nearest neighbor search over object part classes was integrated into a whole object detection system and evaluated against an indoor detection task yielding good results.

关键词： Object detection Nearest neighbor searches Neural networks Shape measurement computer vision Testing Solid modeling Runtime computer science Power system modeling

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Tracking appearances with occlusions

Tracking appearances with occlusions

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conference on computer Vision and pattern recognition (CVPR)

作者： Ying Wu Ting Yu Gang Hua Department of Electrical & Computer Engineering Northwestern University Evanston IL USA

ISBN: (纸本)0769519008

Occlusion is a difficult problem for appearance-based target tracking, especially when we need to track multiple targets simultaneously and maintain the target identities during tracking. To cope with the occlusion problem explicitly, this paper proposes a dynamic Bayesian network, which accommodates an extra hidden process for occlusion and stipulates the conditions on which the image observation likelihood is calculated. The statistical inference of such a hidden process can reveal the occlusion relations among different targets, which makes the tracker more robust against partial even complete occlusions. In addition, considering the fact that target appearances change with views, another generative model for multiple view representation is proposed by adding a switching variable to select from different view templates. The integration of the occlusion model and multiple view model results in a complex dynamic Bayesian network, where extra hidden processes describe the switch of targets' templates, the targets' dynamics, and the occlusions among different targets. The tracking and inferring algorithms are implemented by the sampling-based sequential Monte Carlo strategies. Our experiments show the effectiveness of the proposed probabilistic models and the algorithms.

关键词： Target tracking Bayesian methods Hidden Markov models Switches Monte Carlo methods Inference algorithms Maintenance engineering Robustness Application software Human computer interaction

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Sirface vs. Fisherface: recognition using class specific linear projection

Sirface vs. Fisherface: recognition using class specific lin...

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ieee International conference on image processing

作者： Yangrong Ling Xiangrong Yin S.M. Bhandarkar Department of Computer Science University of Georgia Athens GA USA Department of Statistics University of Georgia Athens GA USA

Using a novel data dimension reduction method proposed in statistics, we develop an appearance-based face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as coordinate in a high-dimensional space. However, since faces are not truly Lambertian surfaces and indeed produce self-shadowing, images deviates from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation using Sliced inverse regression (SIR) [K.C. Li, 1991]. Our face recognition algorithm termed as Sirface produces well-separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expression. Sirface is shown to be equivalent to the well known Fisherface algorithm [P.N. Belhumeur, et al., 1997] in the subspace sense. However, Sirface is shown to produce the optimal reduced subspace (with the fewest dimensions) resulting in a lower error rate and reduced computational expense. Experimental results comparing Sirface to Fisherface on the Yale face database are presented.

关键词： Face recognition Statistics Error analysis Light sources Lighting Solid modeling Light scattering computer science pattern classification Pixel

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