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Ecology Objects Recognition by Optimized Inverse Filtration of Textured Background

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Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

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Abstract

Recognition and elimination of textured background is the first step in computer monitoring of environmental objects by intelligence systems. The approach to develop filters of textured images features is offered. The problem of texture recognition is formulated as inverse problem to modeling of texture dynamic. The modeling is based on the approximation of the texture surface by sum of principal components in the manner of eigen harmonics (EH) which frequencies are related with the texture structure. The approach has some advantages, since the EH have simple form and shift invariance is an important quality in the case of large landscapes modeling and filtering. The texture is considering as a response of a linear dynamic system to an excitation signal of a known shape. The dynamic system is characterized by a transient response. The inverse transient response allows to restore the excitation signal and, if it differs from the original within the specified limits, it’s a sign of texture recognition. The EH approximation is using to find transient response and its inverse form, to optimize it in accordance with criterion of minimal surface.

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Authors and Affiliations

  1. Vinnytsia National Technical, Vinnytsia, Ukraine

    Roman Kvyetnyy, Olga Sofina & Yuriy Bunyak

Authors
  1. Roman Kvyetnyy
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  2. Olga Sofina
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  3. Yuriy Bunyak
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Corresponding author

Correspondence to Olga Sofina .

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Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Kvyetnyy, R., Sofina, O., Bunyak, Y. (2023). Ecology Objects Recognition by Optimized Inverse Filtration of Textured Background. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_11

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