Abstract
The paper proposes a genetic-algorithm-based learning strategy that models membership functions of the fuzzy attributes of surfaces in a model based machine vision system. The objective function aims at enhancing recognition performance in terms of maximizing the degree of discrimination among classes. As a result, the accuracy of recognizing known instances of objects and generalisation capability by recognizing unknown instances of known objects are greatly improved. The performance enhancement of a model based object recognition system consisting of a set of synthetic range images is established by incorporating a dynamic off-line learning mechanism using a genetic algorithm in the feedback path of the system.
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