Authors:
Ferdinand van der Heijden
1
;
Luuk J. Spreeuwers
2
and
Sanja Damjanovic
1
Affiliations:
1
University of Twente, Netherlands
;
2
University Of Twente, Netherlands
Keyword(s):
Likelihood, NCC, Probabilistic framework, HMM, Stereo reconstruction.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Stereo Vision and Structure from Motion
Abstract:
We introduce a new likelihood function for window-based stereo matching. This likelihood can cope with unknown textures, uncertain gain factors, uncertain offsets, and correlated noise. The method can be fine-tuned to the uncertainty ranges of the gains and offsets, rather than a full, blunt normalization as in NCC (normalized cross correlation). The likelihood is based on a sound probabilistic model. As such it can be directly used within a probabilistic framework. We demonstrate this by embedding the likelihood in a HMM (hidden Markov model) formulation of the 3D reconstruction problem, and applying this to a test scene. We compare the reconstruction results with the results when the similarity measure is the NCC, and we show that our likelihood fits better within the probabilistic frame for stereo matching than NCC.