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Is correspondence search in human stereo vision a coarse-to-fine process?

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Mallot,  HA
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Gillner,  S
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Mallot, H., Gillner, S., & Arndt, P. (1996). Is correspondence search in human stereo vision a coarse-to-fine process? Biological Cybernetics, 74(2), 95-106. doi:10.1007/BF00204198.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-EBD4-4
Abstract
One possible strategy for the solution of the correspondence problem of stereo matching is the coarse-to-fine mechanism: The matching process starts with a lowpassfiltered version of the stereogram where only a few, high-contrast image features can be extracted and the probability of false matches is therefore low. In subsequent stages, information from higher spatial frequencies is used gradually to improve the correspondence data obtained on the coarser scales. Coarse-to-fine strategies predict that information from coarse scale is used to disambiguate matching information on finer scales. We have tested this prediction by means of the wallpaper illusion using periodic intensity-profiles with different matching ambiguities on different spatial scale. Our psychophysical experiments show (i) that unambiguous information at coarse scale is not always used to disambiguate finer scale information, (ii) that unambiguous fine-scale information can be used to disambiguate coarsescale information and (iii) that low spatial frequency is more efficient for disambiguation than higher frequency. We conclude that the human stereo vision system does not always proceed from coarse to fine. As an alternative scheme for scale-space integration, we discuss more symmetric schemes such as maximum likelihood combinations of data from different channels.