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Proceedings Paper

Hybrid Hopfield neural network application for 3D object recognition
Author(s): Jung H. Kim; Timothy D. Brooks; Evi H. Park; Celestine A. Ntuen; Shiu M. Cheung; Wagih H. Makky
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Paper Abstract

Automation is the driving force behind many technological advances. One of the major areas of automation research is machine vision. Machine vision centers on object recognition as a means of perceiving a real world environment. Addressing machine vision issues through the application of neural networks is the focus of much contemporary research. Neural networks model the computational units of the human brain, process information in parallel, and as such are extremely well suited for emulation of the human perception process. Thus a neural network approach is presented as a solution to the 3D object recognition problem. Specifically, a hybrid Hopfield network (HHN) previously used to solve 2D occluded object recognition problems is adapted to the 3D object recognition problem. Local and relational features are proposed for use in a HHN graph matching algorithm. Finally, 3D single and multiple input object recognition is realized.

Paper Details

Date Published: 1 February 1994
PDF: 12 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172548
Show Author Affiliations
Jung H. Kim, North Carolina A&T State Univ. (United States)
Timothy D. Brooks, North Carolina A&T State Univ. (United States)
Evi H. Park, North Carolina A&T State Univ. (United States)
Celestine A. Ntuen, North Carolina A&T State Univ. (United States)
Shiu M. Cheung, FAA Technical Ctr. (United States)
Wagih H. Makky, FAA Technical Ctr. (United States)

Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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