In human stereovision, a vivid sensation of depth is created by the
small relative displacements of objects in the retinal images of the left
and right eye. Utilizing only two different views of a scene, the range
of disparities which simple disparity units can estimate is severely limited
by aliasing effects. A new computational approach to stereo vision utilizes
these aliasing effects in a coherence detection scheme to allow the calculation
of dense disparity maps over a large range of disparities with plausible
biological hardware. The fast, non-iterative algorithm creates within a
single network structure simultaneously a disparity map exhibiting hyperacuity,
a verification count of the disparity data and fuses the left and right
input images to the cyclopean view of the scene.
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