Whittle maximum likelihood estimator for isotropic fractional Brownian images

Rachid Harba; G. Jacquet; C. Wilches; M. Zequera; Luis Vilcahuamán

Whittle maximum likelihood estimator for isotropic fractional Brownian images

Rachid Harba, G. Jacquet, C. Wilches, M. Zequera, Luis Vilcahuamán

Sección de Electricidad y Electrónica

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Fractional Brownian motion (fBm) of H parameter is a stochastic fractal process that can be used to create virtual landscapes or to model 2D physical phenomenon. In this communication, we explore the Whittle maximum likelihood estimator (WMLE) to assess the H parameter of isotropic fBm images. We have compared a 1D estimator assessed from the lines increments of the image, to a 2D estimator of the 2D increments of the image. These 2 estimators were tested on 2D fBm generated using the exact Stein method. Results are of high quality. The mean H values are very close to the true ones for both estimators. The standard deviations of the 2D estimates are 2 times smaller than in 1D and should be preferred for practical application as for example texture analysis.

Original language	Spanish
Title of host publication	2014 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2014
State	Published - 19 Oct 2015

Cite this

@inproceedings{5ee8d8b173e54d4dbdfc93e5874461e4,

title = "Whittle maximum likelihood estimator for isotropic fractional Brownian images",

abstract = "Fractional Brownian motion (fBm) of H parameter is a stochastic fractal process that can be used to create virtual landscapes or to model 2D physical phenomenon. In this communication, we explore the Whittle maximum likelihood estimator (WMLE) to assess the H parameter of isotropic fBm images. We have compared a 1D estimator assessed from the lines increments of the image, to a 2D estimator of the 2D increments of the image. These 2 estimators were tested on 2D fBm generated using the exact Stein method. Results are of high quality. The mean H values are very close to the true ones for both estimators. The standard deviations of the 2D estimates are 2 times smaller than in 1D and should be preferred for practical application as for example texture analysis.",

author = "Rachid Harba and G. Jacquet and C. Wilches and M. Zequera and Luis Vilcahuam{\'a}n",

year = "2015",

month = oct,

day = "19",

language = "Espa{\~n}ol",

booktitle = "2014 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2014",

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TY - GEN

T1 - Whittle maximum likelihood estimator for isotropic fractional Brownian images

AU - Harba, Rachid

AU - Jacquet, G.

AU - Wilches, C.

AU - Zequera, M.

AU - Vilcahuamán, Luis

PY - 2015/10/19

Y1 - 2015/10/19

N2 - Fractional Brownian motion (fBm) of H parameter is a stochastic fractal process that can be used to create virtual landscapes or to model 2D physical phenomenon. In this communication, we explore the Whittle maximum likelihood estimator (WMLE) to assess the H parameter of isotropic fBm images. We have compared a 1D estimator assessed from the lines increments of the image, to a 2D estimator of the 2D increments of the image. These 2 estimators were tested on 2D fBm generated using the exact Stein method. Results are of high quality. The mean H values are very close to the true ones for both estimators. The standard deviations of the 2D estimates are 2 times smaller than in 1D and should be preferred for practical application as for example texture analysis.

AB - Fractional Brownian motion (fBm) of H parameter is a stochastic fractal process that can be used to create virtual landscapes or to model 2D physical phenomenon. In this communication, we explore the Whittle maximum likelihood estimator (WMLE) to assess the H parameter of isotropic fBm images. We have compared a 1D estimator assessed from the lines increments of the image, to a 2D estimator of the 2D increments of the image. These 2 estimators were tested on 2D fBm generated using the exact Stein method. Results are of high quality. The mean H values are very close to the true ones for both estimators. The standard deviations of the 2D estimates are 2 times smaller than in 1D and should be preferred for practical application as for example texture analysis.

M3 - Contribución a la conferencia

BT - 2014 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2014

ER -