Genetic learning based texture surface inspection

This paper presents a novel approach of visual inspection for texture surface defects. It is based on the measure of texture energy acquired by a kind if high performance 2D detection mask, which is learned by genetic algorithms. Experimental results of texture defect inspection on textile images are presented to illustrate the merit and feasibility of the proposed method.<br /

An Adaptive Texture and Shape Based Defect Classification

In this paper classification of surface defects is considered. The classification system consists of several classifiers whose outputs are combined in order to produce the final classification. The self-organizing maps (SOMs) are used as classifiers. Each SOM is taught unsupervised with examples of defects. Classification is based on the internal structure and the shape characteristics of defects. Texture features from the co-occurrence matrix and the gray level histogram are used to describe the internal structure. The set of simple shape descriptors is used for shape characterization The results of experiments with base paper defects are encouraging. 1. Introduction Recognition of objects is one of the basic tasks in computer vision applications. The goal of object recognition is to find a description which contains sufficient information to distinguish between different target objects. Recognition is usually based on gray levels or colors and shape characteristics [14, 12] of targe..