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79
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 48 (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.
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 46 (12 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
Approximate Bayesian Multibody Tracking
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Visual tracking of multiple targets is a challenging problem, especially when efficiency is an issue. Occlusions, if not properly handled, are a major source of failure. Solutions supporting principled occlusion reasoning have been proposed but are yet unpractical for online applications. This pape ..."
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Cited by 40 (3 self)
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Visual tracking of multiple targets is a challenging problem, especially when efficiency is an issue. Occlusions, if not properly handled, are a major source of failure. Solutions supporting principled occlusion reasoning have been proposed but are yet unpractical for online applications. This paper presents a new solution which effectively manages the tradeoff between reliable modeling and computational efficiency. The Hybrid JointSeparable (HJS) filter is derived from a joint Bayesian formulation of the problem, and shown to be efficient while optimal in terms of compact belief representation. Computational efficiency is achieved by employing a Markov random field approximation to joint dynamics and an incremental algorithm for posterior update with an appearance likelihood that implements a physicallybased model of the occlusion process. A particle filter implementation is proposed which achieves accurate tracking during partial occlusions, while in case of complete occlusion tracking hypotheses are bound to estimated occlusion volumes. Experiments show that the proposed algorithm is efficient, robust and able to resolve long term occlusions between targets with identical appearance.
Using temporal coherence to build models of animals
 In ICCV
, 2003
"... This paper describes a system that can build appearance models of animals automatically from a video sequence of the relevant animal with no explicit supervisory information. The video sequence need not have any form of special background. Animals are modeled as a 2D kinematic chain of rectangular s ..."
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Cited by 27 (2 self)
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This paper describes a system that can build appearance models of animals automatically from a video sequence of the relevant animal with no explicit supervisory information. The video sequence need not have any form of special background. Animals are modeled as a 2D kinematic chain of rectangular segments, where the number of segments and the topology of the chain are unknown. The system detects possible segments, clusters segments whose appearance is coherent over time, and then builds a spatial model of such segment clusters. The resulting representation of the spatial configuration of the animal in each frame can be seen either as a track — in which case the system described should be viewed as a generalized tracker, that is capable of modeling objects while tracking them — or as the source of an appearance model which can be used to build detectors for the particular animal. This is because knowing a video sequence is temporally coherent — i.e. that a particular animal is present through the sequence — is a strong supervisory signal. The method is shown to be successful as a tracker on video sequences of real scenes showing three different animals. For the same reason it is successful as a tracker, the method results in detectors that can be used to find each animal fairly reliably within the Corel collection of images. 1.
Rapid inference on a novel and/or graph for object detection, segmentation and parsing
 in Advances in Neural Information Processing Systems
, 2007
"... In this paper we formulate a novel AND/OR graph representation capable of describing the different configurations of deformable articulated objects such as horses. The representation makes use of the summarization principle so that lower level nodes in the graph only pass on summary statistics to th ..."
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Cited by 23 (9 self)
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In this paper we formulate a novel AND/OR graph representation capable of describing the different configurations of deformable articulated objects such as horses. The representation makes use of the summarization principle so that lower level nodes in the graph only pass on summary statistics to the higher level nodes. The probability distributions are invariant to position, orientation, and scale. We develop a novel inference algorithm that combined a bottomup process for proposing configurations for horses together with a topdown process for refining and validating these proposals. The strategy of surround suppression is applied to ensure that the inference time is polynomial in the size of input data. The algorithm was applied to the tasks of detecting, segmenting and parsing horses. We demonstrate that the algorithm is fast and comparable with the state of the art approaches. 1
Message errors in belief propagation
 In Advances in Neural Information Processing Systems
, 2004
"... Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether from quantization or other simplified message representations or from stochastic approximation methods. Introducing ..."
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Cited by 21 (7 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 from quantization or other simplified message representations or from stochastic approximation methods. Introducing such errors into the BP message computations has the potential to adversely affect the solution obtained. We analyze this effect with respect to a particular measure of message error, and show bounds on the accumulation of errors in the system. This leads both to convergence conditions and error bounds in traditional and approximate BP message passing. 1
Robust contour matching via the order preserving assignment problem
 IEEE Trans. on Image Processing
, 2004
"... A common approach to determining corresponding points on two shapes is to compute the cost of each possible pairing of points and solve the assignment problem (weighted bipartite matching) for the resulting cost matrix. We consider the problem of solving for point correspondences when the shapes of ..."
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Cited by 19 (0 self)
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A common approach to determining corresponding points on two shapes is to compute the cost of each possible pairing of points and solve the assignment problem (weighted bipartite matching) for the resulting cost matrix. We consider the problem of solving for point correspondences when the shapes of interest are each defined by a single, closed contour. A modification of the standard assignment problem is proposed whereby the correspondences are required to preserve the ordering of the points induced from the shapes ’ contours. Enforcement of this constraint leads to significantly improved correspondences. Robustness with respect to outliers and shape irregularity is obtained by required only a fraction of feature points to be matched. Furthermore, the minimum matching size may be specified in advance. We present efficient dynamic programming algorithms to solve the proposed optimization problem. Experiments on the Brown and MPEG7 shape databases demonstrate the effectiveness of the proposed method relative to the standard assignment problem. 1
Learning Object Shape: From Drawings to Images
 In CVPR ’06
, 2006
"... We consider the important challenge of recognizing a variety of deformable object classes in images. Of fundamental importance and particular difficulty in this setting is the problem of “outlining ” an object, rather than simply deciding on its presence or absence. A major obstacle in learning a mo ..."
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Cited by 18 (2 self)
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We consider the important challenge of recognizing a variety of deformable object classes in images. Of fundamental importance and particular difficulty in this setting is the problem of “outlining ” an object, rather than simply deciding on its presence or absence. A major obstacle in learning a model that will allow us to address this task is the need for handsegmented training images. In this paper we present a novel landmarkbased, piecewiselinear model of the shape of an object class. We then formulate a learning approach that allows us to learn this model with minimal user supervision. We circumvent the need for handsegmentation by transferring the shape “essence ” of an object from drawings to complex images. We show that our method is able to automatically and effectively learn and localize a variety of object classes. 1