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To design a system with multi vision sensors, we should to solve the correspondence problem.
Feature-based methods for non-rigid registration frequently encounter the correspondence problem.
Deriving depth information is simpler than in a binocular stereo system because the correspondence problem is minimized.
One reason is that these previous studies used stimuli that presented little perceptual ambiguity, so the correspondence problem had only one solution.
In addition, the mobile robot self-localisation is obtained by a stereo approach that uses the centres of matching in the two images to solve in a simple way the correspondence problem in the 3D position estimation.
Neural explanations are given for psychophysical data concerning: contrast variations of dichoptic masking and the correspondence problem, the effect of interocular contrast differences on stereoacuity, Panum's limiting case, the Venetian blind illusion, stereopsis with polarity-reversed stereograms, da Vinci stereopsis, and various lightness illusions.
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This paper addresses the motion correspondence problem: the problem of finding corresponding point measurements in an image sequence solely based on positional information.
The key aspect of our illusion is that there are two possible solutions to the multisensory correspondence problem (or the 'assignment problem') [43], [44].
In this paper, we first apply the homography property to solve the point correspondence problem among multiple images taken from different viewpoints for image inpainting.
The motion correspondence problem is most difficult when the target points are densely moving.
Planar contours overlayed on image data are easily manipulated and linked contours reduce the amount of user interaction.1 This method solves the contour to contour correspondence problem and can capture extrema of objects in a more flexible way than manual segmentation of a stack of 2D images.
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