Carpet wear classification based on support vector machine pattern recognition approach

Nowadays, the carpet quality analysis is determined in industry by human experts, because the automated assessment is not capable of matching the human expertise. Therefore, the carpet company demands a reliable and economic standardization of carpet wear level. This paper presents a new strategy for analyzing and classifying the texture of the wear carpet surface of 3D image, where 3D image is produced by 3D laser scanner. 2D image is obtained from 3D data resample on different grid sizes. The features extracted are based on Haralick descriptors of co-occurrence matrix. These features are used as inputs to a classifier system, which is based on support vector machine (SVM). Multi-class classification training based on SVM is applied. The performance of the new technique proposed gives an average of over 92% correct labeling.