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Object Selection in Computer Vision: From Multi-thresholding to Percolation Based Scene Representation

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Computer Vision in Advanced Control Systems-5

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 175))

Abstract

We consider several approaches to the multi-threshold analysis of monochromatic images and consequent interpretation of its results in computer vision systems. The key aspect of our analysis is that it is based on a complete scene reconstruction leading to the object based scene representation inspired by principles from percolation theory. As a generalization of the conventional image segmentation, the proposed reconstruction leads to a multi-scale hierarchy of objects, thus allowing embedded objects to be represented at different scales. Using this reconstruction, we next suggest a direct approach to the object selection as a subset of the reconstructed scene based on a posteriori information obtained by multi-thresholding at the cost of the algorithm performance. We consider several geometric invariants as selection algorithm variables and validate our approach explicitly using prominent examples of synthetic models, remote sensing images, and microscopic data of biological samples.

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Acknowledgements

We like to acknowledge partial support of this research by the Ministry of Science and Higher Education of the Russian Federation in the framework of the basic state assignment of St. Petersburg Electrotechnical University (project No. 2.5475.2017/6.7), as well as, by the Russian Science Foundation (project No. 16-19-00172).

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Authors and Affiliations

  1. Saint-Petersburg Electrotechnical University (LETI), 5, Prof. Popova str., 197376, Saint Petersburg, Russian Federation

    Vladimir Yu. Volkov & Mikhail I. Bogachev

  2. State University of Aerospace Instrumentation, 67, Bolshaya Morskaya, 190000, Saint Petersburg, Russian Federation

    Vladimir Yu. Volkov

  3. Kazan Federal University, 18 Kremlyovskaya st., 420008, Kazan, Russian Federation

    Mikhail I. Bogachev & Airat R. Kayumov

Authors
  1. Vladimir Yu. Volkov
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  2. Mikhail I. Bogachev
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  3. Airat R. Kayumov
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Correspondence to Vladimir Yu. Volkov .

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Editors and Affiliations

  1. Department of Informatics and Computer Techniques, Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  2. University of Canberra, Canberra, SA, Australia

    Lakhmi C. Jain

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Volkov, V.Y., Bogachev, M.I., Kayumov, A.R. (2020). Object Selection in Computer Vision: From Multi-thresholding to Percolation Based Scene Representation. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Advanced Control Systems-5. Intelligent Systems Reference Library, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-030-33795-7_6

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