Identification and Classification of Color Textures

This article describes, how color textures can be reliably detected and classified in the production process independent of external parameters such as brightness, object positions (translation), angulars (rotation), object distances (scaling) or curved surfaces (rotation + scaling). The methods described here are also suitable for reliably classifying at least 18 color textures even if they differ only slightly from each other optically. The online classification of color textures is a classic task in the wood, furniture and textile industry. For example, un- wanted defects or partial soiling on moving webs can be reliably detected regard- less of fluctuations in brightness and/or shadows during process operation. Algo- rithms has been developed for teach-in with RGB-HSI-transform, set fewer seg- ments on the color textures of each class with e.g. 24x24 Pixel, use suitable transformations {HSI}, e.g. 2D-FFT for formation characteristic 2D spectral mountains in these segments, extraction of statistical features and setting up the individual classifiers. Algorithms has been developed for identification & classification in process op- eration with extraction of statistical characteristics and methods of robust classi- fication. The implementation of the methods, the triggering of the color cameras, the processing of the color information including the output of the results to the process control is done with the data analysis program Xeidana®.