Tree structural watershed for stereo matching

Authors:
Xiao Tan;Changming Sun;Xavier Sirault;Robert Furbank;Tuan D. Pham
Affiliations:
SEIT of UNSW Canberra, Canberra, ACT, Australia;CSIRO Mathematics, Informatics and Statistics, NSW, Australia;CSIRO Plant Industry, Canberra, ACT, Australia;CSIRO Plant Industry, Canberra, ACT, Australia;The University of Aizu, Fukushima, Japan
Venue:
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Year:
2012

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Abstract

We present a new method for dense stereo matching based on a tree structural cost volume watershed (TSCVW) and a region combination (RC) process. Given a cost volume as the data cost and an initial segmentation result, the proposed TSCVW method reliably estimates the disparities in a segment by using energy optimization to control plane segmentation and plane fitting. Then the disparities in the incorrectly fitted and occluded regions are refined using our RC process. Experimental results show that our method is very robust to different initial segmentation results and the shape of a segment. The comparison between our algorithm and the current state-of-the-art algorithms on the Middlebury website shows that our algorithm is very competitive.