Abstract
Gaussian-based edge detectors have been developed for many years, but there are still problems with how to set scales for Gaussian filters and how to combine Gaussian filters. In order to address both problems, a Genetic Programming (GP) system is proposed to automatically choose scales for Gaussian filters and automatically combine Gaussian filters. In this study, the GP system is utilised to construct rotation invariant Gaussian-based edge detectors based on a benchmark image dataset. The experimental results show that the GP evolved Gaussian-based edge detectors are better than the Gaussian gradient and rotation invariant surround suppression to extract edge features.
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Fu, W., Johnston, M., Zhang, M. (2013). Automatic Construction of Gaussian-Based Edge Detectors Using Genetic Programming. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_37
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DOI: https://doi.org/10.1007/978-3-642-37192-9_37
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