Neural network based stereo matching algorithm utilizing vertical disparity

Shih Hung Yang*, Cheng Yu Ho, Yon-Ping Chen

*Corresponding author for this work

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

3 Scopus citations

Abstract

This paper presents a stereo matching algorithm utilizing vertical disparity (SMAVD) in solving the matching problem of stereo vision. SMAVD adopts a two-dimensional Hopfield neural network (HNN) to match the stereo pairs according to the energy function developed to describe three constraints including uniqueness, similarity and compatibility. The similarity of one matched pair is measured according to the difference of its neighboring pixels. The compatibility between two matched pairs is determined from not only smoothness and geometric comparisons but also vertical disparity comparison to improve the matching accuracy. Moreover, SMAVD uses a genetic algorithm to design the parameters of the nonlinear functions employed in the similarity and compatibility measures. By applying the updating rule, the HNN could obtain the correct matched pairs satisfying the constraints. The experimental results on the image pairs acquired from a binocular robot demonstrate that SMAVD could achieve high correct matching percentage with less computation time.

Original languageEnglish
Title of host publicationProceedings - IECON 2010, 36th Annual Conference of the IEEE Industrial Electronics Society
Pages1155-1160
Number of pages6
DOIs
StatePublished - 1 Dec 2010
Event36th Annual Conference of the IEEE Industrial Electronics Society, IECON 2010 - Glendale, AZ, United States
Duration: 7 Nov 201010 Nov 2010

Publication series

NameIECON Proceedings (Industrial Electronics Conference)

Conference

Conference36th Annual Conference of the IEEE Industrial Electronics Society, IECON 2010
CountryUnited States
CityGlendale, AZ
Period7/11/1010/11/10

Fingerprint Dive into the research topics of 'Neural network based stereo matching algorithm utilizing vertical disparity'. Together they form a unique fingerprint.

Cite this