Abstract
Computer vision applications have been largely incorporated into robotics and industrial automation, improving quality and safety of processes. Such systems involve pattern classifiers for specific functions that, many times, demand high processing time and large data memory. Robotics applications usually deal with restricted resources platforms, in order to preserve battery time and to reduce weight and costs. To assist those applications, this paper presents an investigation on GLCM (Gray Level Co-occurrence Matrix) features and image size for an SVM (Support Vector Machines) classifier that can reduce computer resources utilization while preserving high classifier accuracy. Experimental results show a computing time on the embedded platform of 80.5 ms, with an accuracy above to 99%, to classify images of 80 \(\times \) 60 pixels.
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Acknowledgment
The authors would like to thank the sponsorship from FUNCAP and CAPES via Grant No. 05/2014 FUNCAP/CAPES.
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Sampaio, F., Silva, E.T., da Silva, L.C., Filho, P.P.R. (2018). An Embedded Classifier for Mobile Robot Localization Using Support Vector Machines and Gray-Level Co-occurrence Matrix. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_20
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DOI: https://doi.org/10.1007/978-3-319-91253-0_20
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