Theses and Dissertations - UTB/UTPA
Date of Award
5-2001
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Manufacturing Engineering
First Advisor
Dr. Subhash Bose
Second Advisor
Dr. Mounir Ben Ghalia
Third Advisor
Dr. Robert Freeman
Abstract
A neural network classifier for separating clods from onions during harvesting has been developed. The separator consists of a multi-layer feedforward network that maps textural features computed from gray-scale images of onions and clods into the correct object. Texture features were computed from co-occurrence matrices that specify the spatial relationship of pixel values in an image. The textural features selected for this application consist of homogeneity, contrast, variance, and energy. The network was trained using the back-propagation algorithm. Based on the textural features classification, the effect of changing the network configuration on separation effectiveness was investigated. Factors including network topology and combination of textural feature measures forming the inputs of the network were systematically analyzed. Thirty three different network configurations were evaluated. The best separation effectiveness was obtained with three-layer (3-2-1) network with input set consisting of energy, contrast, and homogeneity feature measures. The separation effectiveness for 3-2-1 network topology was 96 percent. An analysis of integration of the neural network-based vision system with a mechanical separator is presented.
Granting Institution
University of Texas-Pan American
Comments
Copyright 2001 Demian Morquin. All Rights Reserved.
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