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" Inference " problems arise in statistical physics, computer vision, error-correcting coding theory , and AI. We explain the principles behind the belief propagation (BP) algorithm, which is an efficient way to solve inference problems based on passing local messages. We develop a unified approach with examples, notation, and graphical models borrowed from(More)
The pattern of local image velocities on the retina encodes important environmental information. Although humans are generally able to extract this information, they can easily be deceived into seeing incorrect velocities. We show that these 'illusions' arise naturally in a system that attempts to estimate local image velocity. We formulated a model of(More)
This is an updated and expanded version of TR2000-26, but it is still in draft form. Belief propagation (BP) was only supposed to work for tree-like networks but works surprisingly well in many applications involving networks with loops, including turbo codes. However, there has been little understanding of the algorithm or the nature of the solutions it(More)
An ellipse rotating rigidly about its center may appear to rotate rigidly or to deform nonrigidly so that it appears gelatinous. We use this ambiguous stimulus to study how motion information is propagated across space. We find that features that are quite far from the contour of the ellipse may have a strong influence on the percept of the ellipse,(More)
Loopy belief propagation (BP) has been successfully used in a number of difficult graphical models to find the most probable configuration of the hidden variables. In applications ranging from protein folding to image analysis one would like to find not just the best configuration but rather the top M. While this problem has been solved using the junction(More)
In order to estimate the motion of an object, the visual system needs to combine multiple local measurements , each o f w h i c h carries some degree of ambiguity. W e present a model of motion perception whereby measurements from diierent image regions are combined according to a Bayesian estimator | the estimated motion maximizes the posterior probability(More)
Local belief propagation rules of the sort proposed by P earl (1988) are guaranteed to converge to the optimal beliefs for singly connected networks. Recently, a n umber of researchers have empirically demonstrated good performance of these same algorithms on networks with loops, but a theoretical understanding of this performance has yet to be achieved.(More)
Because of the aperture problem, local motion measurements must be combined across space. However, not all motions should be combined. Some arise from distinct objects and should be segregated, and some are due to occlusion and should be discounted because they are spurious. Humans have little difficulty ignoring spurious motions at occlusions and correctly(More)
— Compressed sensing [7], [6] is a recent set of mathematical results showing that sparse signals can be exactly reconstructed from a small number of linear measurements. Interestingly, for ideal sparse signals with no measurement noise, random measurements allow perfect reconstruction while measurements based on principal component analysis (PCA) or(More)