Evaluating Multi-scale Over-segment and Its Contribution to Real Scene Stereo Matching by High-Order MRFs
Yiran Xie, Rui Cao, Hanyang Tong, Nianjun Liu, Sheng Liu · 2010
The paper is to propose a framework to qualitatively and quantitatively evaluate five of state-of-the-art over-segment approaches. Moreover upon over-segments evaluation, an efficient approach is developed for dense stereo matching through robust higher-order MRFs and graph cut based optimization, which combines the conventional data and smoothness terms with the robust higher-order potential term. The experimental results on real-scene data sets clearly demonstrate that our over-segment-based higher-order stereo matching approach outperforms conventional stereo matching algorithms, as well as how over-segments improve the stereo matching process.