Abstract
In this work, we present a novel methodology for texture image recognition using a partial differential equation modeling. More specifically, we employ the pseudo-parabolic equation to provide a dynamics to the digital image representation and collect local descriptors from those images evolving in time. For the local descriptors we employ the magnitude and signal binary patterns and a simple histogram of these features was capable of achieving promising results in a classification task. We compare the accuracy over well established benchmark texture databases and the results demonstrate competitiveness, even with the most modern deep learning approaches. The achieved results open space for future investigation on this type of modeling for image analysis, especially when there is no large amount of data for training deep learning models and therefore model-based approaches arise as suitable alternatives.
Supported by São Paulo Research Foundation (FAPESP), National Council for Scientific and Technological Development, Brazil (CNPq), and PETROBRAS - Brazil.
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Acknowledgements
J. B. Florindo gratefully acknowledges the financial support of São Paulo Research Foundation (FAPESP) (Grant #2016/16060-0) and from National Council for Scientific and Technological Development, Brazil (CNPq) (Grants #301480/2016-8 and #423292/2018-8). E. Abreu gratefully acknowledges the financial support of São Paulo Research Foundation (FAPESP) (Grant #2019/20991-8), from National Council for Scientific and Technological Development - Brazil (CNPq) (Grant #2 306385/2019-8) and PETROBRAS - Brazil (Grant #2015/00398-0). E. Abreu and J. B. Florindo also gratefully acknowledge the financial support of Red Iberoamericana de Investigadores en Matemáticas Aplicadas a Datos (MathData).
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Florindo, J.B., Abreu, E. (2021). An Application of a Pseudo-Parabolic Modeling to Texture Image Recognition. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_30
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