Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing

As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate parameters in Markov Random Field (MRF) based stereo formulations, one usually needs to perform approximate probabilistic inference. Message passing algorithms based on variational methods and belief propagation are widely used for approximate inference in MRFs. Conditional Random Fields (CRFs) are discriminative versions of traditional MRFs and have recently been applied to the problem of stereo vision. However, CRF parameter training typically requires expensive inference steps for each iteration of optimization. Inference is particularly slow when there are many discrete disparity levels, due to high state space cardinality. We present a novel CRF for stereo matching with an explicit occlusion model and propose sparse message passing to dramatically accelerate the approximate inference needed for parameter optimization. We show that sparse variational message pas...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Jerod J. Weinman, Lam Tran, Christopher J. Pal
• Keywords: Approximate Inference, Approximate Probabilistic Inference, Computer Vision, ECCV 2008, Expensive Inference Steps, Message Passing, Sparse Variational Message