Created by W.Langdon from gp-bibliography.bib Revision:1.5229
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