Tree structural watershed for stereo matching

Xiao Tan*, Changming Sun, Xavier Sirault, Robert Furbank, Tuan D. Pham

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of IVCNZ 2012 - The 27th Image and Vision Computing New Zealand Conference
Pages340-345
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event27th Image and Vision Computing New Zealand Conference, IVCNZ 2012 - Dunedin, New Zealand
Duration: 26 Nov 201228 Nov 2012

Publication series

NameACM International Conference Proceeding Series

Conference

Conference27th Image and Vision Computing New Zealand Conference, IVCNZ 2012
Country/TerritoryNew Zealand
CityDunedin
Period26/11/1228/11/12

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