The self-aware matching measure for stereo

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

33 Scopus citations

Abstract

We revisit stereo matching functions, a topic that is considered well understood, from a different angle. Our goal is to discover a transformation that operates on the cost or similarity measures between pixels in binocular stereo. This transformation should produce a new matching curve that results in higher matching accuracy. The desired transformation must have no additional parameters over those of the original matching function and must result in a new matching function that can be used by existing local, global and semi-local stereo algorithms without having to modify the algorithms. We propose a transformation that meets these requirements, taking advantage of information derived from matching the input images against themselves. We analyze the behavior of this transformation, which we call Self-Aware Matching Measure (SAMM), on a diverse set of experiments on data with ground truth. Our results show that the SAMM improves the performance of dense and semi-dense stereo. Moreover, as opposed to the current state of the art, it does not require distinctiveness to match pixels reliably.

Original languageEnglish
Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
Pages1841-1848
Number of pages8
DOIs
StatePublished - 2009
Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
Duration: 29 Sep 20092 Oct 2009

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference12th International Conference on Computer Vision, ICCV 2009
Country/TerritoryJapan
CityKyoto
Period29/09/092/10/09

Fingerprint

Dive into the research topics of 'The self-aware matching measure for stereo'. Together they form a unique fingerprint.

Cite this