Evolving boundary detectors for natural images via Genetic Programming

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Boundary detection constitutes a crucial step in many computer vision tasks. We present a novel learning approach to automatically construct a boundary detector for natural images via Genetic Programming (GP). Our approach aims to use GP as a learning framework for evolving computer programs that are evaluated against human-marked boundary maps, in order to accurately detect and localize boundaries in natural images. Our GP system is unique in that it combines filter kernels that were inspired by models of processing in the early stages of the primate visual system, but makes no assumption about what constitutes a boundary, thus avoiding the need to make ad-hoc intuitive definitions. By testing the evolved boundary detectors on a benchmark set of natural images with associated human-marked boundaries, we show performance to be quantitatively competitive with existing computer-vision approaches. Moreover, we show that our evolved detector provides insights into the mechanisms underlying boundary detection in the human visual system.

Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)9781424421756
StatePublished - 1 Jan 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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