Abstract
In the paper a texture classification method utilizing the Gray Level Co-occurrence Matrix (GLCM) is proposed which can be applied for autonomous cleaning robots. Our approach is based on the analysis of chosen Haralick features calculated locally together with their selected statistical properties allowing to determine the additional features used for classification purposes. To verify the presented approach a dedicated color image dataset containing textures selected from the Amsterdam Library of Textures (ALOT) representing surfaces typical for the autonomous cleaning robots has been used. The results obtained for various color models and three different classifiers confirm the influence of the color model as well as the advantages of the proposed extended GLCM based approach.
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Seul, A., Okarma, K. (2019). Classification of Textures for Autonomous Cleaning Robots Based on the GLCM and Statistical Local Texture Features. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_40
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DOI: https://doi.org/10.1007/978-3-319-91189-2_40
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