Abstract
In this paper we propose a novel local shape descriptor based on Q-convexity histograms. We investigate three different variants: (1) focusing only on the background points, (2) examining all the points and (3) omitting the zero bin. We study the properties of the variants on a shape and on a texture dataset. In an illustrative example, we compare the classification accuracy of the introduced local descriptor to its global counterpart, and also to a variant of Local Binary Patterns which is similar to our descriptor in the sense that its histogram collects frequencies of local configurations. We show that our descriptor can reach in many cases higher classification accuracy than the others .
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Acknowledgments
Judit Szűcs was supported by the UNKP-19-3-SZTE-291 New National Excellence Program of the Ministry for Innovation and Technology, Hungary. This research was supported by the project “Integrated program for training new generation of scientists in the fields of computer science”, no. EFOP-3.6.3-VEKOP16-2017-00002. This research was supported by grant TUDFO/47138-1/2019-ITM of the Ministry for Innovation and Technology, Hungary.
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Szűcs, J., Balázs, P. (2020). Local Q-Convexity Histograms for Shape Analysis. In: Lukić, T., Barneva, R., Brimkov, V., Čomić, L., Sladoje, N. (eds) Combinatorial Image Analysis. IWCIA 2020. Lecture Notes in Computer Science(), vol 12148. Springer, Cham. https://doi.org/10.1007/978-3-030-51002-2_18
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DOI: https://doi.org/10.1007/978-3-030-51002-2_18
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