Toward a pixel-parallel architecture for graph cuts inference on FPGA

Abstract

The method of Graph Cuts converts a Maximum a Posteriori (MAP) inference problem on a Markov Random Field (MRF) into a network flow, which can be solved efficiently. Many computer vision problems can be conveniently cast as an inference task to find most likely labels for pixels. The method is widely used, but computationally burdensome. Prior accelerator… (More)
DOI: 10.23919/FPL.2017.8056757

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Cite this paper

@article{Gao2017TowardAP, title={Toward a pixel-parallel architecture for graph cuts inference on FPGA}, author={Tianqi Gao and Jungwook Choi and Shang-nien Tsai and Rob A. Rutenbar}, journal={2017 27th International Conference on Field Programmable Logic and Applications (FPL)}, year={2017}, pages={1-4} }