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An Analog Vision Chip Applicable to Real-Time Image Processings in Indoor Illumination
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861356IEEE-INNS-ENNS International Joint Co ...
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Seiji Kameda, Tetsuya Yagi, "An Analog Vision Chip Applicable to Real-Time Image Processings in Indoor Illumination," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 3, pp. 3489, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.861356,
author = {Seiji Kameda and Tetsuya Yagi},
title = {An Analog Vision Chip Applicable to Real-Time Image Processings in Indoor Illumination},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {3},
year = {2000},
issn = {1098-7576},
pages = {3489},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861356},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - An Analog Vision Chip Applicable to Real-Time Image Processings in Indoor Illumination
SN - 1098-7576
SP
EP
A1 - Seiji Kameda,
A1 - Tetsuya Yagi,
PY - 2000
VL - 3
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
Seiji Kameda, Kyushu Institute of Technology
Tetsuya Yagi, Kyushu Institute of Technology
The retina is a part of the central nervous system in the vertebrate and plays important roles in early stage of visual information processing. The retina computes the image with a completely different algorithm/architecture from the one, which most engineers are familiar with using the unique algorithm/architecture, the retina can perform real-time image processing with very low power dissipation.
Citation:
Seiji Kameda, Tetsuya Yagi, "An Analog Vision Chip Applicable to Real-Time Image Processings in Indoor Illumination," ijcnn, vol. 3, pp.3489, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000
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