Adaptive Analog Disparity Model using Ocular Dominance Map

Abstract

Biological vision system extracts depth from the difference in the left and right eye images. Numerous algorithms and their hardware implementations that compute disparity in real time have been proposed. However, most of them compute disparity through complicated functions that are difficult to realize in hardware and are biologically unrealistic. The brain most likely uses simpler methods to extract depth information and hence newer methodologies that could perform stereopsis with brain like elegance need to be explored. Physiological findings support the presence of disparity tuned cells in the visual cortex and show that the perception of depth evolves with experience and is not present at the time of birth. Therefore adaptively learning disparities may indeed be the algorithm underlying depth computations in the developing brain. This paper proposes a novel VLSI design using time-staggered Winner Take All to adaptively create disparity tuned cells.