Monogenic representation for self-similar random fields and color images | Vidéo | Carmin.tv

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Monogenic representation for self-similar random fields and color images

By Hermine Biermé

Appears in collection : Multifractal analysis and self-similarity / Analyse multifractale et auto-similarité

We consider the monogenic representation for self-similar random fields. This approach is based on the monogenic representation of a greyscale image, using Riesz transform, and is particularly well-adapted to detect directionality of self-similar Gaussian fields. In particular, we focus on distributions of monogenic parameters defined as amplitude, orientation and phase of the spherical coordinates of the wavelet monogenic representation. This allows us to define estimators for some anisotropic fractional fields. We then consider the elliptical monogenic model to define vector-valued random fields according to natural colors, using the RGB color model. Joint work with Philippe Carre (XLIM, Poitiers), Céline Lacaux (LMA, Avignon) and Claire Launay (IDP, Tours).

Information about the video

Date of recording 29/06/2023
Date of publication 12/07/2023
Institution CIRM
Licence CC BY NC ND
Audience Researchers, Graduate Students
Director(s) Jean Petit
Format MP4

Citation data

DOI 10.24350/CIRM.V.20063203
Cite this video Biermé, Hermine (29/06/2023). Monogenic representation for self-similar random fields and color images. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.20063203
URL https://dx.doi.org/10.24350/CIRM.V.20063203

Domain(s)

Probability

Bibliography

BIERMÉ, Hermine, CARRÉ, Philippe, LACAUX, Céline, et al. Modélisation de Textures: Champs Gaussiens Autosimilaires et Signal Monogène. 2023. - https://hal.science/hal-04067931/

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