Accelerated learning in machine vision using artificially implanted defects

Abstract

A learning acceleration method is disclosed that can be applied to multiple types and stages of learning to enhance the learning efficiency and outcome. Artificially created training samples can improve representation of all classes in the training set, decrease the difficulty of obtaining sufficient training samples, and decrease the difficulty of unequal sample prevalence. Two specific embodiments of learning acceleration are disclosed: learning accelerated algorithm training and learning accelerated start-up learning. Three objects of interest implantation methods are disclosed: texture mapping of defects, parametric synthesis of negative samples, and manual image editing.