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COMPLEX ASSOCIATIVE MEMORY NEURAL NETWORK MODEL FOR INVARIANT PATTERN RECOGNITION
 

Summary: COMPLEX ASSOCIATIVE MEMORY NEURAL NETWORK MODEL
FOR INVARIANT PATTERN RECOGNITION
Abdul Ahad S. Awwal and Farid Ahmed
Wright State University
Computer Science & Engineering Department
Dayton, OH 45435.
Abstract
A complex associative memory neural network
(CAMN2) model is proposed for the recognition of
handwritten characters. The input and the stored
patterns here are derived from the complex valued
representation of the boundary of the characters.
The stored vector representation is formulated based
on 1-D representation of an optical pattern recogni-
tion filter. Retrieval of stored patterns from a noisy
and shifted input is accomplished by using the cor-
relation in the inverse fourier domain. An adaptive
thresholding scheme is then applied to obtain a 1-
step convergence. The number of convergence of
patterns, usually measured as the storage capacity

  

Source: Ahmed, Farid - Department of Electrical Engineering and Computer Science, Catholic University of America

 

Collections: Engineering