Stochastic implementation of the disparity energy model for depth perception

Kaushik Boga, Naoya Onizawa, François Leduc-Primeau, Kazumichi Matsumiya, Takahiro Hanyu, Warren J. Gross

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We implement a binocular vision system based on a disparity-energy model that emulates the hierarchical multi-layered neural structure in the primary visual cortex. Layer 1 performs difference-of-Gaussian filtering that mimicks the center-surround receptive fields (RF) in the retina, layer 2 performs Gabor filtering mimicking the orientation selective filtering performed by simple cells and layer 3 has complex cells tuned to detecting 5 different disparities. A VLSI architecture is developed based on stochastic computing that is compact and adder-free. Even with a short stream length, the proposed architecture achieves better disparity detection than a floating-point version by using a modified disparity-energy model. A 1 × 100 pixel processing system is synthesized using TSMC 65nm CMOS technology and achieves up to 79% reduction in area-delay product compared to a fixed point implementation.

Original languageEnglish
Title of host publicationElectronic Proceedings of the 2015 IEEE International Workshop on Signal Processing Systems, SiPS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467396042
DOIs
Publication statusPublished - 2015 Dec 2
EventIEEE International Workshop on Signal Processing Systems, SiPS 2015 - Hangzhou, China
Duration: 2015 Oct 142015 Oct 16

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
Volume2015-December
ISSN (Print)1520-6130

Other

OtherIEEE International Workshop on Signal Processing Systems, SiPS 2015
CountryChina
CityHangzhou
Period15/10/1415/10/16

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture

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