skip to main content
10.1145/3297067.3297082acmotherconferencesArticle/Chapter ViewAbstractPublication PagesspmlConference Proceedingsconference-collections
research-article

Implementation of Irregular Meshes for the Sparse Representation of Multidimensional Signals

Published:28 November 2018Publication 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.

References

  1. R.M. Mersereau "The processing of Hexagonally Sampled Two-Dimensional Signals", Proc IEEE, 930--949. (1979)Google ScholarGoogle Scholar
  2. I. Daubechies, I. Guskov, P. Schroder, W. Sweldens "Wavelets on irregular point sets", Phil. Trans. R. Soc. Lon. 1760, 2397--2413. (1999)Google ScholarGoogle ScholarCross RefCross Ref
  3. I. Guskov, P. Schroder, W. Sweldens "Multiresolution signal processing for meshes", SIGGRAPH '99, 325--334 (1999) Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. V. Monga, R. Bala, X. Mo, "Design and optimization of color look-up tables on a simplex topology", IEEE Trans. Image Process., vol. 21, no. 4, 1981--1996 (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. G.P. Fickel, C.R. Jung, T. Malzbender, R. Samadani, B. Culbertson, "Stereo matching and view interpolation based on image domain triangulation", Image Processing IEEE Transactions on, vol. 22, no. 9, 3353--3365 (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Sullivan G. J., Boyce J. M., Chen Y., Ohm J.-R., Segall C. A., Vetro A. Standardized Extensions of High Efficiency Video Coding // IEEE Journal on Selected Topics in Signal Processing. 2013. 7 (6). C. 1001--1016.Google ScholarGoogle ScholarCross RefCross Ref
  7. Glocker B., Heibel T.H., Navab N., Kohli P., Rother C. TriangleFlow: Optical Flow with Triangulation-Based Higher-Order Likelihoods // Computer Vision - ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, № 6313 Springer, Berlin, Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Vishnyakov S., Pekhterev V., Sokolova E. A Novel Method of the Image Processing on Irregular Triangular Meshes // Proc. SPIE 10615, ICGIP 2017.Google ScholarGoogle Scholar
  9. Jagalur-Mohan J., Feijoo G., Oberai A. A Galerkin least squares method for time harmonic Maxwell equations using Nedelec elements // Journal of Computational Physics. 2013. № 235, C. 67--81 Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Fettweis A. Multidimensional wave---digital principles: from filtering to numerical integration// IEEE transactions on circuits and systems. 1994. vol. 40. № 4. C. 174--182.Google ScholarGoogle Scholar

Index Terms

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

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      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

      Copyright © 2018 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 November 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader