Agricultural Produce Grading by Computer Vision Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{201412Panitnat_Yimyam,
  author =       "Panitnat Yimyam",
  title =        "Agricultural Produce Grading by Computer Vision Based
                 on Genetic Programming",
  school =       "computer science and electronic engineering, Essex
                 University",
  year =         "2015",
  address =      "UK",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Jasmine",
  URL =          "https://www.essex.ac.uk/csee/news_and_seminars/newsEvent.aspx?e_id=7344",
  URL =          "http://www.bmva.org/theses:2014",
  URL =          "http://www.bmva.org/theses:201412panitnat_yimyam",
  URL =          "http://www.bmva.org/thesis-archive/2014/2014-yimyam.pdf",
  size =         "256 pages",
  abstract =     "An objective of computer vision is to imitate the
                 ability of the human visual system. Computer vision has
                 been put forward to produce a wide range of
                 applications. Most vision software does not proceed
                 alone; machine learning is usually involved in many
                 vision systems. Some vision systems are developed to
                 replace human working because they operate more
                 reliably, precisely and speedily, and some tasks are
                 dangerous for humans.

                 This thesis presents contributions to extend a vision
                 system based on genetic programming to solve
                 classification problems. Instances in the field of
                 agricultural produce are employed to verify the system
                 performance. A new method is proposed to determine the
                 shape and appearance of reconstructed 3D objects. The
                 reconstruction is based on using 2D images taken by a
                 few cameras in arbitrary positions. Furthermore, new
                 techniques are presented to extract properties of 3D
                 objects; morphological, coloured and textural
                 features.

                 New techniques are proposed to incorporate new features
                 and new classes of samples into a GP classifier. For
                 the former, the new feature is accommodated into an
                 existing solution by mutation. For the latter, as
                 generating a multi-class classifier is based on a
                 binary decomposition approach, a binary classifier of
                 the new class is produced and executed before the
                 series of the original binary classifiers. Both cases
                 are intended to be done with less computation than
                 evolving a new classifier from scratch.",
}

Genetic Programming entries for Panitnat Yimyam

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