A Coarse-and-Fine Bayesian Belief Propagation for Correspondence Problems in Computer Vision

Abstract

We present the use of a multi-resolution, coarse-and-fine, pyramid image architecture to solve correspondence problems in various computer vision modules including shape recognition through contour matching, stereovision, and motion estimation. The algorithm works with a grid matching and an inter-grid correspondence model by message passing in a Bayesian belief propagation (BBP) network. The local smoothness and other constraints are expressed within each resolution scale grid and also between grids in a single paradigm. Top-down and bottom-up matching are concurrently performed for each pair of adjacent levels of the image pyramid level in order to find the best matched features at each level simultaneously. The coarse-and-fine algorithm uses matching results in each layer to constrain the process in its 2 adjacent upper and lower layers by measuring the consistency between corresponding points among adjacent layers so that good matches at different resolution scales constrain one another. The coarse-and-fine method helps avoid the local minimum problem by bringing features closer at the coarse level and yet providing a complete solution at the finer level. The method is used to constrain the solution with examples in shape retrieval, stereovision, and motion estimation to demonstrate its desirable properties such as rapid convergence, the ability to obtain near optimal solution while avoiding local minima, and immunity to error propagation found in the coarse-to-fine approach. ...