Engineering of computer vision algorithms using evolutionary algorithms

Abstract

Computer vision algorithms are currently developed by looking up the available operators from the literature and then arranging those operators such that the desired task is performed. This is often a tedious process which also involves testing the algorithm with different lighting conditions or at different sites. We have developed a system for the automatic generation of computer vision algorithms at interactive frame rates using GPU accelerated image processing. The user simply tells the system which object should be detected in an image sequence. Simulated evolution, in particular Genetic Programming, is used to automatically generate and test alternative computer vision algorithms. Only the best algorithms survive and eventually provide a solution to the user's image processing task. © 2009 Springer Berlin Heidelberg.