Results 1  10
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107
Nonparametric Belief Propagation
 IN CVPR
, 2002
"... In applications of graphical models arising in fields such as computer vision, the hidden variables of interest are most naturally specified by continuous, nonGaussian distributions. However, due to the limitations of existing inf#6F6F3 algorithms, it is of#]k necessary tof#3# coarse, ..."
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Cited by 279 (25 self)
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In applications of graphical models arising in fields such as computer vision, the hidden variables of interest are most naturally specified by continuous, nonGaussian distributions. However, due to the limitations of existing inf#6F6F3 algorithms, it is of#]k necessary tof#3# coarse, discrete approximations to such models. In this paper, we develop a nonparametric belief propagation (NBP) algorithm, which uses stochastic methods to propagate kernelbased approximations to the true continuous messages. Each NBP message update is based on an efficient sampling procedure which can accomodate an extremely broad class of potentialf#l3]k[[z3 allowing easy adaptation to new application areas. We validate our method using comparisons to continuous BP for Gaussian networks, and an application to the stereo vision problem.
Tracking Looselimbed People
, 2004
"... 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 looselyconnected limbs. Conditional probabilities relating the 3D pose of connected limbs are learned from motioncaptured tr ..."
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Cited by 191 (7 self)
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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 looselyconnected limbs. Conditional probabilities relating the 3D pose of connected limbs are learned from motioncaptured 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 nonparametric belief propagation using a variation of particle filtering that can be applied over a general loopy graph. The looselimbed model and decentralized graph structure facilitate the use of lowlevel visual cues. We adopt simple limb and head detectors to provide "bottomup" information that is incorporated into the inference process at every timestep; 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 markerbased motion capture system aligned with the coordinate frame of the calibrated cameras with which we quantitatively evaluate the accuracy of our 3D person tracker.
Finding and tracking people from the bottom up
 In CVPR
, 2003
"... Abstract We ..."
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PAMPAS: RealValued Graphical Models for Computer Vision
, 2003
"... Probabilistic models have been adopted for many computer vision applications, however inference in highdimensional spaces remains problematic. As the statespace of a model grows, the dependencies between the dimensions lead to an exponential growth in computation when performing inference. Many comm ..."
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Cited by 121 (3 self)
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Probabilistic models have been adopted for many computer vision applications, however inference in highdimensional spaces remains problematic. As the statespace of a model grows, the dependencies between the dimensions lead to an exponential growth in computation when performing inference. Many common computer vision problems naturally map onto the graphical model framework; the representation is a graph where each node contains a portion of the statespace and there is an edge between two nodes only if they are not independent conditional on the other nodes in the graph. When this graph is sparsely connected, belief propagation algorithms can turn an exponential inference computation into one which is linear in the size of the graph. However belief propagation is only applicable when the variables in the nodes are discretevalued or jointly represented by a single multivariate Gaussian distribution, and this rules out many computer vision applications.
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces.
 In Proc. of Neural Information Processing Systems (NIPS).
, 2004
"... Figure 1 : Several frames from a motion animation generated by interpolating two scans of a puppet (far left and far right), which were automatically registered using the Correlated Correspondence algorithm. Abstract We present an unsupervised algorithm for registering 3D surface scans of a deforma ..."
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Cited by 113 (4 self)
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Figure 1 : Several frames from a motion animation generated by interpolating two scans of a puppet (far left and far right), which were automatically registered using the Correlated Correspondence algorithm. Abstract We present an unsupervised algorithm for registering 3D surface scans of a deformable object in very different configurations. Our algorithm does not use markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or its alignment. The algorithm finds the correspondences between points in the two meshes using a joint probabilistic model over all point correspondences. This model combines preservation of local mesh geometry with more global constraints that capture the preservation of geodesic distance between corresponding pairs of points in the two meshes. Our approach successfully registers scans that exhibit large transformations, including both movement of articulate parts and nonrigid surface deformations. It applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining surfaces for this partial scan, even if those surfaces were previously only seen in a different configuration. We also show how our results can be used to interpolate between two scans of a nonrigid object in a way that preserves surface geometry, leading to natural motion paths. Finally, we show that a registration of multiple scans in different configurations allows us to automatically identify components in articulate objects.
Loopy belief propagation: Convergence and effects of message errors
 Journal of Machine Learning Research
, 2005
"... Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether due to quantization of the messages or model parameters, from other simplified message or model representations, or ..."
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Cited by 104 (9 self)
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Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether due to quantization of the messages or model parameters, from other simplified message or model representations, or from stochastic approximation methods. The introduction of such errors into the BP message computations has the potential to affect the solution obtained adversely. We analyze the effect resulting from message approximation under two particular measures of error, and show bounds on the accumulation of errors in the system. This analysis leads to convergence conditions for traditional BP message passing, and both strict bounds and estimates of the resulting error in systems of approximate BP message passing. 1
Nonparametric belief propagation for selflocalization of sensor networks
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 2005
"... Automatic selflocalization is a critical need for the effective use of adhoc sensor networks in military or civilian applications. In general, selflocalization involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. distance measurements b ..."
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Cited by 98 (3 self)
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Automatic selflocalization is a critical need for the effective use of adhoc sensor networks in military or civilian applications. In general, selflocalization involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. distance measurements between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of intersensor communication. We demonstrate that the information used for sensor localization is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then present and demonstrate the utility of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multimodal uncertainty. Using simulations of small to moderatelysized sensor networks, we show that NBP may be made robust to outlier measurement errors by a simple model augmentation, and that judicious message construction can result in better estimates. Furthermore, we provide an analysis of NBP’s communications requirements, showing that typically only a few messages per sensor are required, and that even low bitrate approximations of these messages can have little or no performance impact.
Visual hand tracking using nonparametric belief propagation
 Propagation,” IEEE Workshop on Generative Model Based Vision
, 2004
"... Abstract — This paper develops probabilistic methods for visual tracking of a threedimensional geometric hand model from monocular image sequences. We consider a redundant representation in which each model component is described by its position and orientation in the world coordinate frame. A prio ..."
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Cited by 71 (1 self)
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Abstract — This paper develops probabilistic methods for visual tracking of a threedimensional geometric hand model from monocular image sequences. We consider a redundant representation in which each model component is described by its position and orientation in the world coordinate frame. A prior model is then defined which enforces the kinematic constraints implied by the model’s joints. We show that this prior has a local structure, and is in fact a pairwise Markov random field. Furthermore, our redundant representation allows color and edgebased likelihood measures, such as the Chamfer distance, to be similarly decomposed in cases where there is no self–occlusion. Given this graphical model of hand kinematics, we may track the hand’s motion using the recently proposed nonparametric belief propagation (NBP) algorithm. Like particle filters, NBP approximates the posterior distribution over hand configurations as a collection of samples. However, NBP uses the graphical structure to greatly reduce the dimensionality of these distributions, providing improved robustness. Several methods are used to improve NBP’s computational efficiency, including a novel KDtree based method for fast Chamfer distance evaluation. We provide simulations showing that NBP may be used to refine inaccurate model initializations, as well as track hand motion through extended image sequences. I.
A New Look at Survey Propagation and its Generalizations
"... We study the survey propagation algorithm [19, 5, 4], which is an iterative technique that appears to be very effective in solving random kSAT problems even with densities close to threshold. We first describe how any SAT formula can be associated with a novel family of Markov random fields (MRFs), ..."
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Cited by 66 (11 self)
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We study the survey propagation algorithm [19, 5, 4], which is an iterative technique that appears to be very effective in solving random kSAT problems even with densities close to threshold. We first describe how any SAT formula can be associated with a novel family of Markov random fields (MRFs), parameterized by a real number ρ. We then show that applying belief propagation— a wellknown “messagepassing” technique—to this family of MRFs recovers various algorithms, ranging from pure survey propagation at one extreme (ρ = 1) to standard belief propagation on the uniform distribution over SAT assignments at the other extreme (ρ = 0). Configurations in these MRFs have a natural interpretation as generalized satisfiability assignments, on which a partial order can be defined. We isolate cores as minimal elements in this partial
Beyond trees: Common factor models for 2D human pose recovery
 In ICCV
, 2005
"... Tree structured models have been widely used for determining the pose of a human body, from either 2D or 3D data. While such models can effectively represent the kinematic constraints of the skeletal structure, they do not capture additional constraints such as coordination of the limbs. Tree struct ..."
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Cited by 66 (1 self)
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Tree structured models have been widely used for determining the pose of a human body, from either 2D or 3D data. While such models can effectively represent the kinematic constraints of the skeletal structure, they do not capture additional constraints such as coordination of the limbs. Tree structured models thus miss an important source of information about human body pose, as limb coordination is necessary for balance while standing, walking, or running, as well as being evident in other activities such as dancing and throwing. In this paper we consider the use of undirected graphical models that augment a tree structure with latent variables in order to account for coordination between limbs. We refer to these as commonfactor models, since they are constructed by using factor analysis to identify additional correlations in limb position that are not accounted for by the kinematic tree structure. These commonfactor models have an underlying tree structure and thus a variant of the standard Viterbi algorithm for a tree can be applied for efficient estimation. We present some experimental results contrasting commonfactor models with tree models, and quantify the improvement in pose estimation for 2D image data. 1.