A Fully-Pipelined FPGA Design for Tree-Reweighted Message Passing Algorithm

Abstract

A Markov random field (MRF) is a set of random variables demonstrating a Markov property in the form of an undirected graph. Maximum a posteriori probability (MAP) inference is a class of methods that seek solutions of problems modeled by MRF. MRF has been a very popular and powerful tool in computer vision problems such as stereo matching and image segmentation [1]. Finding the optimal solution of the MRF MAP problem is an NP-hard problem. Inference algorithms often involve a heavy computation load. Therefore, most related works have focused on improving the performance and efficiency of algorithms. Hardware-based acceleration is one of the most practical solutions.