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Proceedings Paper

Autonomous neuromorphic image understanding system using graph representation: lower-level design
Author(s): Nikolaos G. Bourbakis; J. Sukaro Mertoguno
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Paper Abstract

In this paper, a neuromorphic multilayer architecture, called KYDON, is presented. In particular, the structural design of the layer nodes and the low and high level vision tasks performed by KYDON nets are described. KYDON architecture has 'k' layers of nodes connected in full hexagonal mesh connectivity. The lowest layer capture images from the environment by employing 2-D photoarray. The top most layer deals with image interpretation and understanding. The intermediate layers perform various process to bridge the bottom most layer to the top most layer. KYDON use graph to represent the knowledge, extracted from the image. An important feature of KYDON is that KYDON does not have any host computer or control processor to handle I/O and other miscellaneous tasks.

Paper Details

Date Published: 10 June 1993
PDF: 11 pages
Proc. SPIE 1904, Image Modeling, (10 June 1993); doi: 10.1117/12.146688
Show Author Affiliations
Nikolaos G. Bourbakis, SUNY/Binghamton (United States)
J. Sukaro Mertoguno, SUNY/Binghamton (United States)


Published in SPIE Proceedings Vol. 1904:
Image Modeling
Lawrence A. Ray; James R. Sullivan, Editor(s)

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