Improving Stereo Performance in Regions of Low Texture
Author(s) -
K. Moravec,
Richard Harvey,
Jenny Bangham
Publication year - 1998
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.12.82
Subject(s) - window (computing) , computer science , texture (cosmology) , artificial intelligence , matching (statistics) , computer vision , set (abstract data type) , operator (biology) , pattern recognition (psychology) , image (mathematics) , mathematics , statistics , biochemistry , chemistry , repressor , transcription factor , gene , programming language , operating system
In images with low texture the performance of conventional dense stereo can be poor. The usual solution to this is to use a large window but this itself can be problematic as the large window can blur important features and hence lead to errors in the disparity estimate. Here it is shown that, not only do connected set morphology operators overcome this problem, they perform best in regions of low texture. A further observation is that, since the operators give a heirarchical decompostion, there is a possibility of not only using these operators to choose a new window, but also to motivate a new matching method.
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