- author = "Tony Belpaeme",
- title = "Evolution of Visual Feature Detectors",
- booktitle = "Late Breaking Papers at EvoISAP'99: the First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing",
- year = "1999",
- editor = "Riccardo Poli and Stefano Cagnoni and Hans-Michael Voigt and Terry Fogarty and Peter Nordin",
- pages = "1--10",
- address = "Goteborg, Sweden",
- month = "28 " # may,
- organisation = "EvoNet",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://arti.vub.ac.be/~tony/papers/EvoIASP99.ps.gz",
-
URL = "
http://citeseer.ist.psu.edu/362631.html",
- abstract = "This paper describes how sets of visual feature detectors are evolved starting from simple primitives. The primitives, of which some are inspired on visual processing observed in mammalian visual pathways, are combined using genetic programming to form a feed-forward feature-extraction hierarchy. Input to the feature detectors consists of a series of real-world images, containing objects or faces. The results show how each set of feature detectors self-organizes into a set which is capable of returning feature vectors for discriminating the input images. We discuss the influence of different settings on the evolution of the feature detectors and explain some phenomena.",
-
notes = "EvoIASP'99 Available as CSRP-99-10 from the School of
Computer Science, University of Birmingham, Edgbaston,
Birmingham B15 2TT, UK.
STGP. Information returned by each (of 5) feature detector, entropy of the output vector p4 {"}if the outputs are weel spread, meaning the feature detectors return useful information, the fitness will be high. Explicit parsimony preasure, but not needed p8? LilGP.",
