Classification of Textures for Autonomous Cleaning Robots Based on the GLCM and Statistical Local Texture Features

  • Andrzej Seul
  • Krzysztof Okarma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 764)


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.


Texture analysis GLCM Haralick features Cleaning robots 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

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