Modeling multiple object scenarios for feature recognition and classification using cellular neural networks
Date
2009-11-02T09:02:52Z
Authors
Malumedzha, Tendani Calven
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Abstract
Cellular neural networks (CNNs) have been adopted in the spatio-temporal processing
research field as a paradigm of complexity. This is due to the ease of designs for complex
spatio-temporal tasks introduced by these networks. This has led to an increase
in the adoption of CNNs for on-chip VLSI implementations. This dissertation proposes
the use of a Cellular Neural Network to model, detect and classify objects appearing in
multiple object scenes. The algorithm proposed is based on image scene enhancement
through anisotropic diffusion; object detection and extraction through binary edge detection
and boundary tracing; and object classification through genetically optimised
associative networks and texture histograms. The first classification method is based
on optimizing the space-invariant feedback template of the zero-input network through
genetic operators, while the second method is based on computing diffusion filtered
and modified histograms for object classes to generate decision boundaries that can be
used to classify the objects. The primary goal is to design analogic algorithms that
can be used to perform these tasks. While the use of genetically optimized associative
networks for object learning yield an efficiency of over 95%, the use texture histograms
has been found very accurate though there is a need to develop a better technique for
histogram comparisons. The results found using these analogic algorithms affirm CNNs
as well-suited for image processing tasks.