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Motion segmentation with missing data using PowerFactorization and GPCA

Motion segmentation with missing data using PowerFactorizati...

引用

conference on computer vision and pattern recognition (cvpr)

作者： R. Vidal R. Hartley NICTA Australia Center for Imaging Science Johns Hopkins University USA Department of Systems Engineering Australian National University Australia

We consider the problem of segmenting multiple rigid motions from point correspondences in multiple affine views. We cast this problem as a subspace clustering problem in which the motion of each object lives in a subspace of dimension two, three or four. Unlike previous work, we do not restrict the motion subspaces to be four-dimensional or linearly independent. Instead, our approach deals gracefully with all the spectrum of possible affine motions: from two-dimensional and partially dependent to four-dimensional and fully independent. In addition, our method handles the case of missing data, meaning that point tracks do not have to be visible in all images. Our approach involves projecting the point trajectories of all the points into a 5-dimensional space, using the PowerFactorization method to fill in missing data. Then multiple linear subspaces representing independent motions is fitted to the points in R5 using GPCA. We test our algorithm on various real sequences with degenerate and nondegenerate motions, missing data, perspective effects, transparent motions, etc. Our algorithm achieves a misclassification error of less than 5% for sequences with up to 30% of missing data points.

关键词： Motion segmentation computer vision Cameras Transmission line matrix methods Layout Polynomials Shape measurement Noise shaping Power engineering and energy Systems engineering and theory

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Collaborative tracking of multiple targets

Collaborative tracking of multiple targets

引用

conference on computer vision and pattern recognition (cvpr)

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

Coalescence, meaning the tracker associates more than one trajectories to some targets while loses track for others, is a challenging problem for visual tracking of multiple targets, especially when similar targets move close or present occlusions. Existing approaches that are based on joint data association are confronted by the combinatorial complexity due to the concatenation of the state spaces of individual targets. This paper presents a novel collaborative approach with linear complexity to the coalescence problem. The basic idea is the collaborative inference mechanism, in which the estimate of an individual target is not only determined by its own observation and dynamics, but also through the interaction and collaboration with the estimates of its adjacent targets, which leads to a competition mechanism that enables different targets to compete for the common image observations. The theoretical foundation of the new approach is based on Markov networks. Variational analysis of this Markov network reveals a mean field approximation to the posterior density of each target, therefore provides a computationally efficient way for such a difficult inference problem. In addition, a mean field Monte Carlo (MFMC) algorithm is designed to achieve Bayesian inference by simulating the competition among a set of low dimensional particle filters. Compared with the existing solutions, the proposed new collaborative approach stands out by its effectiveness and low computational cost to the coalescence problem, as pronounced in the extensive experiments.

关键词： Collaboration Target tracking Markov random fields Trajectory State-space methods Inference mechanisms computer networks Monte Carlo methods Inference algorithms Algorithm design and analysis

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Learning classifiers from imbalanced data based on biased minimax probability machine

Learning classifiers from imbalanced data based on biased mi...

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conference on computer vision and pattern recognition (cvpr)

作者： Kaizhu Huang Haiqin Yang I. King M.R. Lyu Department of Computer Science and Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

We consider the problem of the binary classification on imbalanced data, in which nearly all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. Traditional machine learning methods seeking an accurate performance over a full range of instances are not suitable to deal with this problem, since they tend to classify all the data into the majority, usually the less important class. Moreover, some current methods have tried to utilize some intermediate factors, e.g., the distribution of the training set, the decision thresholds or the cost matrices, to influence the bias of the classification. However, it remains uncertain whether these methods can improve the performance in a systematic way. In this paper, we propose a novel model named biased minimax probability machine. Different from previous methods, this model directly controls the worst-case real accuracy of classification of the future data to build up biased classifier;. Hence, it provides a rigorous treatment on imbalanced data. The experimental results on the novel model comparing with those of three competitive methods, i.e., the naive Bayesian classifier, the k-nearest neighbor method, and the decision tree method C4.5, demonstrate the superiority of our novel model.

关键词： Machine learning Minimax techniques Costs Sampling methods Classification tree analysis Educational institutions computer science Data engineering Learning systems Bayesian methods

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Variational mixture smoothing for non-linear dynamical systems

Variational mixture smoothing for non-linear dynamical syste...

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conference on computer vision and pattern recognition (cvpr)

作者： C. Sminchisescu A. Jepson Department of Computer Science Artificial Intelligence Laboratory University of Toronto Toronto Canada

We present an algorithm for computing joint state, smoothed, density estimates for non-linear dynamical systems in a Bayesian setting. Many visual tracking problems can be formulated as probabilistic inference over time series, but we are not aware of mixture smoothers that would apply to weakly identifiable models, where multimodality is persistent rather than transient (e.g. monocular 3D human tracking). Such processes, in principle, exclude iterated Kalman smoothers, whereas flexible MCMC methods or sample based particle smoothers encounter computational difficulties: accurately locating an exponential number of probable joint state modes representing high-dimensional trajectories, rapidly mixing between those or resampling probable configurations missed during filtering. In this paper we present an alternative, layered, mixture density smoothing algorithm that exploits the accuracy of efficient optimization within a Bayesian approximation framework. The distribution is progressively refined by combining polynomial time search over the embedded network of temporal observation likelihood peaks, MAP continuous trajectory estimates, and Bayesian variational adjustment of the resulting joint mixture approximation. Our results demonstrate the effectiveness of the method on the problem of inferring multiple plausible 3D human motion trajectories from monocular video.

关键词： Smoothing methods Bayesian methods Fault location Humans Inference algorithms State estimation Kalman filters Filtering Approximation algorithms Polynomials

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Fast, integrated person tracking and activity recognition with plan-view templates from a single stereo camera

Fast, integrated person tracking and activity recognition wi...

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conference on computer vision and pattern recognition (cvpr)

作者： M. Harville Dalong Li Hewlett Packard Laboratories Palo Alto CA USA Center for Signal and Image Processing Georgia Institute of Technology Atlanta GA USA

Plan-view projection of real-time depth imagery can improve the statistics of its intrinsic 3D data, and allows for cleaner separation of occluding and closely-interacting people. We build a probabilistic, real-time multi-person tracking system upon a plan-view image substrate that well preserves both shape and size information of foreground objects. The tracking's robustness derives in part from its "plan-view template" person models, which capture detailed properties of people's body configurations. We demonstrate that these same person models, obtained with a single compact stereo camera unit, may also be used for fast recognition of body pose and activity. Principal components analysis is used to extract plan-view "eigenposes", onto which person models, extracted during tracking, are projected to produce a compact representation of human body configuration. We then formulate pose recognition as a classification problem, and use support vector machines (SVMs) to quickly distinguish between, for example, different directions people are facing, and different body poses such as standing, sitting, bending over, crouching, and reaching. The SVM outputs are transformed to probabilities and integrated across time in a probabilistic framework for real-time activity recognition.

关键词： Cameras Biological system modeling Support vector machines Support vector machine classification Statistics Real time systems Shape Robustness Principal component analysis Humans

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Tracking loose-limbed people

Tracking loose-limbed people

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conference on computer vision and pattern recognition (cvpr)

作者： L. Sigal S. Bhatia S. Roth M.J. Black M. Isard Department of Computer Science Brown University of Providence RI USA Microsoft Research Silicon Valley Mountain View CA USA

We pose the problem of 3D human tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loosely-connected limbs. Conditional probabilities relating the 3D pose of connected limbs are learned from motion-captured training data. Similarly, we learn probabilistic models for the temporal evolution of each limb (forward and backward in time). Human pose and motion estimation is then solved with non-parametric belief propagation using a variation of particle filtering that can be applied over a general loopy graph. The loose-limbed model and decentralized graph structure facilitate the use of low-level visual cues. We adopt simple limb and head detectors to provide "bottom-up" information that is incorporated into the inference process at every time-step; these detectors permit automatic initialization and aid recovery from transient tracking failures. We illustrate the method by automatically tracking a walking person in video imagery using four calibrated cameras. Our experimental apparatus includes a marker-based motion capture system aligned with the coordinate frame of the calibrated cameras with which we quantitatively evaluate the accuracy of our 3D person tracker.

关键词： Motion analysis Tracking Probability Filtering Graph theory Inference mechanisms

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Estimation, smoothing, and characterization of apparent diffusion coefficient profiles from High Angular Resolution DWI

Estimation, smoothing, and characterization of apparent diff...

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conference on computer vision and pattern recognition (cvpr)

作者： Y. Chen W. Guo Q. Zeng X. Yan F. Huang H. Zhang G. He B.C. Vemuri Y. Liu Department of Mathematics University of Florida USA Department of Psychiatry and Neuroscience University of Florida USA Department of Computer and Information Science and Engineering University of Florida USA

We present a new variational framework for recovery of apparent diffusion coefficient (ADC)from High Angular Resolution Diffusion-weighted (HARD) MRI. The model approximates the ADC profiles by a 4th order spherical harmonic series (SHS), whose coefficients are obtained by solving a constrained minimization problem. By minimizing the energy functional, the ADC profiles are estimated and regularized simultaneously across the entire volume. In this model, feature preserving smoothing is achieved by minimizing a non-standard growth functional, and the estimation is based on the original Stejskal-Tanner equation. The antipodal symmetry and positiveness of the ADC are also accommodated into the model. Furthermore, coefficients of the SHS and the variance of ADC profiles from its mean are used to characterize the diffusion anisotropy. The effectiveness of the proposed model is depicted via application to both simulated and HARD MRI human brain data. The characterization of non-Gaussian diffusion based on the proposed model showed consistency with known neuroanatomy.

关键词： Smoothing methods Magnetic resonance imaging Anisotropic magnetoresistance Equations Brain modeling Q measurement Performance evaluation Diffusion tensor imaging Helium Mathematics

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Non-rigid shape and motion recovery: degenerate deformations

Non-rigid shape and motion recovery: degenerate deformations

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conference on computer vision and pattern recognition (cvpr)

作者： Jing Xiao T. Kanade The Robotics Institute Carnegie Mellon University Pittsburgh PA USA

This paper studies the problem of 3D non-rigid shape and motion recovery from a monocular video sequence, under the degenerate deformations. The shape of a deformable object is regarded as a linear combination of certain shape bases. When the bases are non-degenerate, i.e. of full rank-3, a closed-form solution exists by enforcing linear constraints on both the camera rotation and the shape bases. In practice, degenerate deformations occur often, i.e. some bases are of rank 1 or 2. For example, cars moving or pedestrians walking independently on a straight road refer to rank-1 deformations of the scene. This paper quantitatively shows that, when the shape is composed of only rank-3 and rank-1 bases, i.e. the 3D points either are static or independently move along straight lines, the linear rotation and basis constraints are sufficient to achieve a unique solution. When the shape bases contain rank-2 ones, imposing only the linear constraints results in an ambiguous solution space. In such cases, we propose an alternating linear approach that imposes the positive semi-definite constraint to determine the desired solution in the solution space. The performance of the approach is evaluated quantitatively on synthetic data and qualitatively on real videos.

关键词： Shape Layout Cameras Video sequences Closed-form solution Legged locomotion Robots Roads Application software Human computer interaction

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The world in an eye [eye image interpretation]

The world in an eye [eye image interpretation]

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conference on computer vision and pattern recognition (cvpr)

作者： Ko Nishino S.K. Nayar Department of Computer Science Columbia University NY USA

This paper provides a comprehensive analysis of exactly what visual information about the world is embedded within a single image of an eye. It turns out that the cornea of an eye and a camera viewing the eye form a catadioptric imaging system. We refer to this as a corneal imaging system. Unlike a typical catadioptric system, a corneal one is flexible in that the reflector (cornea) is not rigidly attached to the camera. Using a geometric model of the cornea based on anatomical studies, its 3D location and orientation can be estimated from a single image of the eye. Once this is done, a wide-angle view of the environment of the person can be obtained from the image. In addition, we can compute the projection of the environment onto the retina with its center aligned with the gaze direction. This foveated retinal image reveals what the person is looking at. We present a detailed analysis of the characteristics of the corneal imaging system including field of view, resolution and locus of viewpoints. When both eyes of a person are captured in an image, we have a stereo corneal imaging system. We analyze the epipolar geometry of this stereo system and show how it can be used to compute 3D structure. The framework we present in this paper for interpreting eye images is passive and non-invasive. It has direct implications for several fields including visual recognition, human-machine interfaces, computer graphics and human affect studies.

关键词： Cornea Image analysis Cameras Retina Information analysis Solid modeling Image resolution Eyes Computational geometry Man machine systems

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High-speed videography using a dense camera array

High-speed videography using a dense camera array

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conference on computer vision and pattern recognition (cvpr)

作者： B. Wilburn N. Joshi V. Vaish M. Levoy M. Horowitz Department of Electrical Engineering University of Stanford Stanford CA USA Department of Computer Science University of Stanford Stanford CA USA

We demonstrate a system for capturing multi-thousand frame-per-second (fps) video using a dense array of cheap 30 fps CMOS image sensors. A benefit of using a camera array to capture high-speed video is that we can scale to higher speeds by simply adding more cameras. Even at extremely high frame rates, our array architecture supports continuous streaming to disk from all of the cameras. This allows us to record unpredictable events, in which nothing occurs before the event of interest that could be used to trigger the beginning of recording. Synthesizing one high-speed video sequence using images from an array of cameras requires methods to calibrate and correct those cameras' varying radiometric and geometric properties. We assume that our scene is either relatively planar or is very far away from the camera and that the images can therefore be aligned using projective transforms. We analyze the errors from this assumption and present methods to make them less visually objectionable. We also present a new method to automatically color match our sensors. Finally, we demonstrate how to compensate for spatial and temporal distortions caused by the electronic rolling shutter, a common feature of low-end CMOS sensors.

关键词： Cameras Sensor arrays CMOS image sensors Streaming media Disk recording Video recording Video sequences Radiometry Layout Error analysis

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