Evolving boundary detectors for natural images via Genetic Programming
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gp-bibliography.bib Revision:1.8081
- @InProceedings{DBLP:conf/icpr/KadarBS08,
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author = "Ilan Kadar and Ohad Ben-Shahar and Moshe Sipper",
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title = "Evolving boundary detectors for natural images via
Genetic Programming",
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booktitle = "19th International Conference on Pattern Recognition,
ICPR 2008",
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year = "2008",
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month = dec # " 8-11",
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pages = "1--4",
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address = "Tampa, Florida, USA",
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keywords = "genetic algorithms, genetic programming, computer
vision, learning (artificial intelligence), boundary
detection, boundary detectors, computer vision, filter
kernels, human visual system, human-marked boundaries,
human-marked boundary maps, learning approach, learning
framework, natural images, primate visual system",
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isbn13 = "978-1-4244-2175-6",
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DOI = "doi:10.1109/ICPR.2008.4761581",
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abstract = "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.",
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notes = "Also known as \cite{4761581}",
- }
Genetic Programming entries for
Ilan Kadar
Ohad Ben-Shahar
Moshe Sipper
Citations