Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Primitive Hierarchical (MPH) Stereo Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Belief Propagation and Revision in Networks with Loops
Belief Propagation and Revision in Networks with Loops
Learning and Inferring Image Segmentations using the GBP Typical Cut Algorithm
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple frame motion inference using belief propagation
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Computer Vision and Image Understanding
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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.