Research Article

Empirical Voronoi wavelets

Volume: 5 Number: 4 December 1, 2022
EN

Empirical Voronoi wavelets

Abstract

Recently, the construction of 2D empirical wavelets based on partitioning the Fourier domain with the watershed transform has been proposed. If such approach can build partitions of completely arbitrary shapes, for some applications, it is desirable to keep a certain level of regularity in the geometry of the obtained partitions. In this paper, we propose to build such partition using Voronoi diagrams. This solution allows us to keep a high level of adaptability while guaranteeing a minimum level of geometric regularity in the detected partition.

Keywords

Supporting Institution

Air Force Office of Scientific Research

Project Number

FA9550-21-1-0275

References

  1. B. Hurat, Z. Alvarado and J. Gilles: The Empirical Watershed Wavelet, Journal of Imaging, 6 (12) (2020), 140.
  2. J. Gilles: Continuous empirical wavelets systems, Advances in Data Science and Adaptive Analysis, 12 (03n04) (2020), 2050006.
  3. K. Bui, J. Fauman, D. Kes, L.Torres Mandiola, A. Ciomaga, R. Salazar, A.L. Bertozzi, J. Gilles, D. P. Goronzy, A. I. Guttentag and P. S. Weiss: Segmentation of Scanning Tunneling Microscopy Images Using Variational Methods and Empirical Wavelets, Pattern Analysis and Applications, 23 (2020), 625–651.
  4. Y. Huang, F. Zhou and J. Gilles: Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation, Neurocomputing, 349 (2019), 31–43.
  5. Y. Huang, V. De Bortoli, F. Zhou and J. Gilles: Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets, IET Image Processing Journal, 12 (9) (2018), 1626–1638.
  6. J. Gilles, K. Heal: A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation, International Journal of Wavelets, Multiresolution and Information Processing, 12 (6) (2014), 1450044-1–1450044-17.
  7. J. Gilles, G. Tran and S. Osher: 2D Empirical transforms. Wavelets, Ridgelets and Curvelets Revisited, SIAM Journal on Imaging Sciences, 7 (1) (2014), 157–186.
  8. J. Gilles: Empirical Wavelet Transform, IEEE Transactions on Signal Processing, 61 (16) (2013), 3999–4010.

Details

Primary Language

English

Subjects

Applied Mathematics

Journal Section

Research Article

Publication Date

December 1, 2022

Submission Date

September 28, 2022

Acceptance Date

October 26, 2022

Published in Issue

Year 2022 Volume: 5 Number: 4

APA
Gilles, J. (2022). Empirical Voronoi wavelets. Constructive Mathematical Analysis, 5(4), 183-189. https://doi.org/10.33205/cma.1181174
AMA
1.Gilles J. Empirical Voronoi wavelets. CMA. 2022;5(4):183-189. doi:10.33205/cma.1181174
Chicago
Gilles, Jerome. 2022. “Empirical Voronoi Wavelets”. Constructive Mathematical Analysis 5 (4): 183-89. https://doi.org/10.33205/cma.1181174.
EndNote
Gilles J (December 1, 2022) Empirical Voronoi wavelets. Constructive Mathematical Analysis 5 4 183–189.
IEEE
[1]J. Gilles, “Empirical Voronoi wavelets”, CMA, vol. 5, no. 4, pp. 183–189, Dec. 2022, doi: 10.33205/cma.1181174.
ISNAD
Gilles, Jerome. “Empirical Voronoi Wavelets”. Constructive Mathematical Analysis 5/4 (December 1, 2022): 183-189. https://doi.org/10.33205/cma.1181174.
JAMA
1.Gilles J. Empirical Voronoi wavelets. CMA. 2022;5:183–189.
MLA
Gilles, Jerome. “Empirical Voronoi Wavelets”. Constructive Mathematical Analysis, vol. 5, no. 4, Dec. 2022, pp. 183-9, doi:10.33205/cma.1181174.
Vancouver
1.Jerome Gilles. Empirical Voronoi wavelets. CMA. 2022 Dec. 1;5(4):183-9. doi:10.33205/cma.1181174