Abstract
Sky \(\diagup \!\!\!\) ground detection plays an important role in many applications such as unmanned control vehicle, dehazing process, cloud detection, for instance. This paper proposes a supervised sky-ground classification technique to color images. The novelty of the proposal is to evaluate the efficiency of whiteness indexes on the classification task. The strategy of the proposal consists in evaluating the power of whiteness indices in classification task. Eleven whiteness indices are used as features to feed a SVM classifier. Experimental results onto 1200 images and numerical evaluations have highlighted that the combination of five whiteness indices is a interesting strategy to classify the sky and the ground.
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de Mattos, F., Beuren, A.T., de Souza, B.M.N., De Souza Britto, A., Facon, J. (2018). Supervised Approach to Sky and Ground Classification Using Whiteness-Based Features. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_20
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DOI: https://doi.org/10.1007/978-3-030-02840-4_20
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