An adaptive cost aggregation method based on bilateral filter and Canny edge detector with segmented area for stereo matching

Abstract

In traditional stereo matching method global method is more accurate but spending more time and have more correct rate in occlusion area On the contrary local method is usually fast but have bad performance and easily is influence by noise This paper proposed a novel method to compute disparity between two images It is based on local method but its cost be aggregated in like-global way This aggregation is processed by a weight map which created by bilateral filter concept Every pixel transfer its own cost information to all pixels on the same object but this information would be restricted by weight map After finishing preliminary depth map we use L-R check to find occlusion and mismatch pixel Then fix occlusion by the smallest disparity nearby At last we use bilateral filter clean up whole depth map All of above computing process can be parallelized on GPU machine or cloud sever Although this algorithm is designed for low-level machine it still exerts high performance in the world of high-level hardware

Date of Award	2014 Aug 18
Original language	English
Supervisor	Pau-Choo Chung (Supervisor)