Neuromorphic VLSI vision system for real-time texture segregation

Neural Networks : the Official Journal of the International Neural Network Society
Kazuhiro Shimonomura, Tetsuya Yagi

Abstract

The visual system of the brain can perceive an external scene in real-time with extremely low power dissipation, although the response speed of an individual neuron is considerably lower than that of semiconductor devices. The neurons in the visual pathway generate their receptive fields using a parallel and hierarchical architecture. This architecture of the visual cortex is interesting and important for designing a novel perception system from an engineering perspective. The aim of this study is to develop a vision system hardware, which is designed inspired by a hierarchical visual processing in V1, for real time texture segregation. The system consists of a silicon retina, orientation chip, and field programmable gate array (FPGA) circuit. The silicon retina emulates the neural circuits of the vertebrate retina and exhibits a Laplacian-Gaussian-like receptive field. The orientation chip selectively aggregates multiple pixels of the silicon retina in order to produce Gabor-like receptive fields that are tuned to various orientations by mimicking the feed-forward model proposed by Hubel and Wiesel. The FPGA circuit receives the output of the orientation chip and computes the responses of the complex cells. Using this system, ...Continue Reading

References

Jan 1, 1991·Vision Research·M S Landy, J R Bergen
Oct 23, 2001·Neural Networks : the Official Journal of the International Neural Network Society·S C LiuR Douglas
Aug 23, 2005·Neural Networks : the Official Journal of the International Neural Network Society·Kazuya Tohyama, Kunihiko Fukushima
Aug 27, 2005·IEEE Transactions on Neural Networks·Kazuhiro Shimonomura, Tetsuya Yagi
Mar 11, 2006·IEEE Transactions on Neural Networks·Seiji Kameda, Tetsuya Yagi
Feb 7, 2007·IEEE Transactions on Neural Networks·R Jacob VogelsteinGert Cauwenberghs
Feb 5, 2008·IEEE Transactions on Neural Networks·S Kameda, T Yagi

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Citations

Feb 8, 2011·IEEE Transactions on Neural Networks·Aurel A Lazar, Eftychios A Pnevmatikakis
Sep 9, 2008·Neural Networks : the Official Journal of the International Neural Network Society·Jan G BjaalieShiro Usui

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