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Implementation of Irregular Meshes for the Sparse Representation of Multidimensional Signals

Published: 28 November 2018 Publication History

Abstract

The paper is dedicated to development of effective tools of multidimensional digital signal processing on irregular meshes. ANN-based method of irregular mesh generation for intra-frame video coding is developed. The method described is based on artificial neural network implementation. Different architectures and types of artificial neural networks are compared. The training and testing sequences generation problem is discussed. The aim of the irregular mesh coverage of the two-dimensional signal (frame) is to decrease computational cost for the further motion detection between frames. The benefit of the artificial neural network usage is the relatively low computational cost of the mesh generation in comparison with analogous. The implementation of the irregular meshes for the correlation analysis between signals is discussed. Examples of the utilization of the irregular mesh-based FIR filtering for the open-boundary problem numerical solution are presented. Generalized results obtained may be used for pattern recognition, data compression, multidimensional look-up table interpolation.

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  • (2020)Irregular Meshes for Color ManagementProceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning10.1145/3432291.3432298(79-83)Online publication date: 22-Oct-2020

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  1. Implementation of Irregular Meshes for the Sparse Representation of Multidimensional Signals

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    cover image ACM Other conferences
    SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning
    November 2018
    177 pages
    ISBN:9781450366052
    DOI:10.1145/3297067
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    Published: 28 November 2018

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    Author Tags

    1. finite element method
    2. interpolation
    3. irregular mesh
    4. open-boundary problem
    5. scene analysis

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    • (2020)Irregular Meshes for Color ManagementProceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning10.1145/3432291.3432298(79-83)Online publication date: 22-Oct-2020

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