Optimization of a Hardware Implementation for Pulse Coupled Neural Networks for Image Applications
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Sarciada-2010-Optimization of a hardware.pdf
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Author(s)
Warde, Cardinal
Sarciada, Jesus Gimeno
Rivera, Horacio Lamela
Date Issued
April 2010
Journal
Proceedings of SPIE--the International Society for Optical Engineering; v.7703
Publisher
SPIE
Citation
Sarciada, Jesus Gimeno, Horacio Lamela Rivera, and Cardinal Warde. “Optimization of a hardware implementation for pulse coupled neural networks for image applications.” Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII. Ed. Harold H. Szu & F. Jack Agee. ©2010 SPIE
Version
Final published version
Abstract
Pulse Coupled Neural Networks are a very useful tool for image processing and visual applications, since it has the advantages of being invariant to image changes as rotation, scale, or certain distortion. Among other characteristics, the PCNN changes a given image input into a temporal representation which can be easily later analyzed for pattern recognition. The structure of a PCNN though, makes it necessary to determine all of its parameters very carefully in order to function optimally, so that the responses to the kind of inputs it will be subjected are clearly discriminated allowing for an easy and fast post-processing yielding useful results. This tweaking of the system is a taxing process. In this paper we analyze and compare two methods for modeling PCNNs. A purely mathematical model is programmed and a similar circuital model is also designed. Both are then used to determine the optimal values of the several parameters of a PCNN: gain, threshold, time constants for feed-in and threshold and linking leading to an optimal design for image recognition. The results are compared for usefulness, accuracy and speed, as well as the performance and time requirements for fast and easy design, thus providing a tool for future ease of management of a PCNN for different tasks.
Subjects
PCNN
Image processing
Hardware implementation
Pulse coupled neural networks
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1117/12.850778
