Evaluation of the State of Cutting Tools According to Its Texture Using LOSIB and LBP Variants

The FRESVIDA project deals with the life assessment of cutting tools working under severe conditions using digital image processing techniques. The description of texture in materials through artificial vision techniques is very useful for this goal. There are several works based on Local Binary Patterns (LBP) and many variants such as Local Binary Pattern Variance (LBPV) or Diamond-LBP Code (DLBPCS) that have proved to be effective when distinguishing materials according to their texture. The Outex dataset comprises images from 24 materials acquired under different illumination conditions, becoming the present reference dataset for texture evaluation. In this work, several descriptors have been extracted based on the LBP from the Outex dataset, as well as their combination with LOSIB (Local Oriented Statistical Information Booster). All of them have been classified with Support Vector Machine (SVM) to assess which one is more useful for the above-mentioned task. In this case, all descriptors achieve a lower performance level combined with LOSIB because Outex is a data set that studies rotation invariances.