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Creating edge detectors by evolutionary reinforcement learning

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Creating edge detectors by evolutionary reinforcement learning
— In this article we present results from experiments where a edge detector was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is constructed as a neural network that takes as input the pixel values from a given image region—the same way that standard edge detectors do. However, it does not have any perimage parameters. A comparison between the evolved neural networks and two standard algorithms, the Sobel and Canny edge detectors, shows very good results.
Nils T. Siebel, Sven Grünewald, Gerald Sommer
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where CEC
Authors Nils T. Siebel, Sven Grünewald, Gerald Sommer
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