Automatic texture inspection in the classification of papers and cloths with neural networks method

The purpose of this paper is to present an automatic inspection method of colors and textures classification of paper and cloth objects.

In this system, the color image is transformed from RGB model to other suitable color model with one of the components being chosen as the gray‐level image for extracting textures. The gray‐level image is decomposed into four child images using wavelet transformation. Two child images capable of detecting variations along columns and rows are used to generate 0° and 90° co‐occurrence matrices, respectively. Some of the distinguishable texture features are derived from the two co‐occurrence matrixes. Finally, the test image is classified using neural networks. Nine color papers and eight color cloths are used to test the developed classification method.

The results show that recognition rate higher than 97.86 percent can be achieved if color and texture features are both used as the inputs to the networks.

The paper presents a new approach for testing materials. The multipurpose measurement application with unsophisticated and economical equipment can be confirmed in online inspection of papers and cloth manufacturing.