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Local Q-concavity histograms for binary image classification and reconstruction

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Abstract

In this paper, we propose a novel vector-form image descriptor that measures the so-called Q-concavity of a binary image under all possible positions of a sliding window of fixed size. In this way, a local Q-concavity histogram (LQH) is created. We propose three techniques to speed up the process of obtaining such histograms. Then, we also present a strategy to determine the proper window size and the appropriate number of histogram bins to achieve the greatest classification power of the descriptor. To show another application of the descriptor, we also solve a binary image reconstruction problem using LQH as prior information. The reconstruction is formulated as a discrete global optimization problem, which is then solved by simulated annealing. We also conduct experiments to show the usefulness of this approach.

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Acknowledgements

Judit Szűcs was supported by the ÚNKP-20-4-SZTE-598 New National Excellence Program of the Ministry for Innovation and Technology from the Source of the National Research, Development and Innovation Fund. 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-VEKOP-16-2017-00002. The project has been supported by the European Union and co-funded by the European Social Fund. The authors thank Dániel Melkvi and Gábor Magyar for conducting preliminary experiments with vector-form Q-concavity descriptors. This research was supported by grant NKFIH-1279-2/2020 of the Ministry for Innovation and Technology, Hungary.

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  1. Department of Image Processing and Computer Graphics, University of Szeged, Árpád tér 2., Szeged, 6720, Hungary

    Judit Szűcs & Péter Balázs

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  1. Judit Szűcs
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  2. Péter Balázs
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Correspondence to Judit Szűcs.

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Szűcs, J., Balázs, P. Local Q-concavity histograms for binary image classification and reconstruction. Vis Comput 38, 4221–4234 (2022). https://doi.org/10.1007/s00371-021-02290-4

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