Understanding of GP-Evolved Motion Detectors
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Song:2013:IEEEcim,
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author = "Andy Song and Qiao Shi and Wei Yin",
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journal = "IEEE Computational Intelligence Magazine",
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title = "Understanding of GP-Evolved Motion Detectors",
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year = "2013",
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volume = "8",
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number = "1",
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month = feb,
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pages = "46--55",
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size = "7 pages",
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abstract = "Evolving solutions for machine vision applications has
gained more popularity in the recent years. One area is
evolving programs by Genetic Programming (GP) for
motion detection, which is a fundamental component of
most vision systems. Despite the good performance, this
approach is not widely accepted by mainstream vision
application developers. One of the reasons is that
these GP generated programs are often difficult to
interpret by humans. This study analyses the reasons
behind the good performance and shows that the
behaviours of these evolved motion detectors can be
explained. Their capabilities of ignoring uninteresting
motions, differentiating fast motions from slow
motions, identifying genuine motions from moving
background and handling noises are not random. On
simplified problems we can reveal the behaviours of
these programs. By understanding the evolved detectors,
we can consider evolution as a good approach for
creating motion detection modules.",
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keywords = "genetic algorithms, genetic programming, computer
vision, image motion analysis, object detection,
GP-evolved motion detector, evolution approach, machine
vision application, motion detection, motion
differentiation, vision system, Detectors, Human
factors, Machine vision, Motion detection, Noise
measurement, Videos",
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DOI = "doi:10.1109/MCI.2012.2228594",
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ISSN = "1556-603X",
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notes = "Also known as \cite{6410722}",
- }
Genetic Programming entries for
Andy Song
Qiao Shi
Wei Yin
Citations