Abstract
This paper describes the generation and selection of visual feature detectors. The feature detectors are randomly generated, and are built out of components, some having functionality inspired on observations of animal visual pathways. The input for the feature detectors consists of non-synthetic images, while the selectionist pressure comes from the amount of information the feature detectors generate. The experimental setup is described and some results are given.
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© 1999 Springer-Verlag Berlin Heidelberg
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Belpaeme, T. (1999). Evolving Visual Feature Detectors. In: Floreano, D., Nicoud, JD., Mondada, F. (eds) Advances in Artificial Life. ECAL 1999. Lecture Notes in Computer Science(), vol 1674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48304-7_34
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DOI: https://doi.org/10.1007/3-540-48304-7_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66452-9
Online ISBN: 978-3-540-48304-5
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