BibTeX entry

@PHDTHESIS{201412Panitnat_Yimyam,
  AUTHOR={Panitnat Yimyam},
  TITLE={Agricultural Produce Grading by Computer Vision
    Based on Genetic Programming},
  SCHOOL={University of Essex},
  MONTH=Dec,
  YEAR=2014,
  URL={http://www.bmva.org/theses/2014/2014-yimyam.pdf},
}

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.