Disparity Gradients and Depth Scaling
MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
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The binocular perception of shape and of depth relations between objects can change considerably if the viewing direction is changed only by a small angle. We explored this effect psychophysically and found a strong depth reduction effect for large disparity gradients. The effect is found to be strongest for horizontally oriented stimuli, and stronger for line stimuli than for points. This depth scaling effect is discussed in a computational framework of stereo based on a Baysian approach which allows to integrate information from different types of matching primitives weighted according to their robustness. Keywords Stero Artificial intelligence Computer vision Depth scaling Disparity gradients Computational vision.