A NEW LIKELIHOOD FUNCTION FOR STEREO MATCHING - How to Achieve Invariance to Unknown Texture, Gains and Offsets?
Ferdinand van der Heijden, Luuk Spreeuwers, Sanja Damjanović · 2009
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 finetuned 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.