Post-aggregation stereo matching method using Dempster-Shafer theory

Abstract

Stereo matching is a basic yet important issue in the research of computer vision. A key problem of stereo matching is how to efficiently use the information provided by the neighborhood. In some existing disparity refinement methods, it is observed that the disparity is fused only after having the disparity map, which unfortunately causes the lost of cost information. To make a better disparity fusion, a postaggregation method based on the Dempster-Shafer Theory (DST) is proposed in this paper to replace the traditional Winner-Takes-All (WTA) strategy. DST is used in the postaggregation by keeping and processing the aggregated cost in each disparity. The experiment is done with real road scenes and the results show that our method fits various cost functions, and that final disparity error can be reduced compared to WTA strategy.Index Terms-Stereo Matching, Dempster-Shafer Theory, cross zone.

Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction

“…DST is also known as the theory of belief functions or Evidence theory. As an extension of probability theory and the set-membership approach, DST has shown remarkable applications in divers fields, such as medical image processing (Bloch, 1996;Lelandais et al, 2014;Makni et al, 2014), statistical machine learning (Zhu and Basir, 2005;Denoeux and Smets, 2006;Masson and Denoeux, 2008;Liu et al, 2015), and computer vision (Xu et al, 2014;Wang et al, 2014) etc. DST consists of two main components, i.e., the quantification of a piece of evidence and the combination of different items of evidence.…”