Genetic programming for edge detection based on figure of merit

Authors:
Wenlong Fu;Mark Johnston;Mengjie Zhang
Affiliations:
Victoria University of Wellington, Wellington, New Zealand;Victoria University of Wellington, Wellington, New Zealand;Victoria University of Wellington, New Zealand, Wellington, New Zealand
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

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Abstract

The figure of merit (FOM) is popular for testing an edge detector's performance, but there are very few reports using FOM as an evaluation method in the learning stage of supervised learning methods. In this study, FOM is investigated as a fitness function in Genetic Programming (GP) for edge detection. Since FOM has some drawbacks from type II errors, new fitness functions are developed based on FOM in order to address these weaknesses. Experimental results show that FOM can be used to evolve GP edge detectors that perform better than the Sobel detector, and the new fitness functions clearly improve the ability of GP edge detectors to find edge points and give a single response on edges, compared with the fitness function using FOM.