Adaptive Analog Disparity Model using Ocular Dominance Map
Pages 9 - 14
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.
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Index Terms
- Adaptive Analog Disparity Model using Ocular Dominance Map
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Published In
February 2015
269 pages
ISBN:9781450320023
DOI:10.1145/2708463
- General Chair:
- Sankar K. Pal,
- Program Chairs:
- Malay K. Kundu,
- Santanu Chaudhury,
- Soma Mitra,
- Debasis Mazumdar
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- Dept. of Science and Techn., Government of India: Department of Science and Technology, Government of India
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Association for Computing Machinery
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Publication History
Published: 26 February 2015
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PerMIn '15
PerMIn '15: 2nd International Conference on Perception and Machine Intelligence
February 26 - 27, 2015
West Bengal, Kolkata, India
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