Abstract
Coarseness is a very important textural concept that has been widely analyzed in computer vision for years. However, a model which allows to represent different perception degrees of this textural concept in the same way that humans perceive texture is needed. In this paper we propose a model that associates computational measures to human perception by learning an appropriate function. To do it, different measures representative of coarseness are chosen and subjects assessments are collected and aggregated. Finally, a function that relates these data is fitted.
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Chamorro-Martínez, J., Galán-Perales, E., Prados-Suárez, B., Soto-Hidalgo, J.M. (2007). Perceptually-Based Functions for Coarseness Textural Feature Representation. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_74
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DOI: https://doi.org/10.1007/978-3-540-72847-4_74
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