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2008
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Evolving boundary detectors for natural images via Genetic Programming

11 years 25 days ago
Evolving boundary detectors for natural images via Genetic Programming
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 computervision approaches. Moreover, we show that our evolved detector provides insights into the mechanisms underlying...
Ilan Kadar, Moshe Sipper, Ohad Ben-Shahar
Added 05 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Ilan Kadar, Moshe Sipper, Ohad Ben-Shahar
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