Advertisement
Published in:
2012 | OriginalPaper | Chapter
Stereo Matching by Fusion of Local Methods and Spatial Weighted Window
Authors : Thi Dinh Tran, Hong Phuc Nguyen, Quang Vinh Dinh
Published in: Multi-disciplinary Trends in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
In this paper, we proposed two window-based methods, spatial weight shiftable window and spatial weight multiple window, for correspondence problem in stereo matching. The spatial weight shiftable window is an improvement of a shiftable window method while the spatial weight multiple window is an enhancement of a multiple window method. They combine spatial weighted window for each support window, and they hence can work well in the regions of disparity discontinuity or object boundaries. The window costs in our approaches is calculated by deploying spatial weighted window for each support window, and the similarity is finally selected by a Winner-Takes-All strategy. The experimental results for the Middleburry images illustrated that the proposed algorithms outperform test local stereo algorithms.