SIMULATION OF IMAGE TIME SERIES FROM DYNAMICAL FRACTIONAL BROWNIAN FIELDS

被引：0

作者：

Atto, Abdourrahmane M. ^{[1
]}

Fillatre, Lionel ^{[2
]}

Antonini, Marc ^{[2
]}

Nikiforov, Igor ^{[3
]}

机构：

[1] Univ Savoy, Polytech Annecy Chambery, LISTIC, EA 3703, Savoy, France

[2] Univ Nice Sophia Antipolis, CNRS, UMR 7271, I3S, Nice, France

[3] Univ Technol Troyes, CNRS, UMR 6279, STMR,LM2S, Troyes, France

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2014年

关键词：

Image Time Series; Stochastic fields; Dynamic Fractional Brownian Field; Dynamic textures; Spatio-Temporal Field Simulation; SPECTRUM; TEXTURE;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The paper addresses random field time series analysis and simulation. The analysis constrains a spatial isotropic fractional Brownian field to a dynamic temporal behavior from separable time varying Hurst parameters. The constrained dynamic applies by embedding the wavelet packet spectrum of the input random field into different spectra associated with the same random family (exponential spectrum decay). The paper highlights the relevance of the approach for representing and simulating isotropic light source and cloud dynamics.

引用

页码：6086 / 6090

页数：5

共 50 条

[21] Fractional Brownian Fields for Response Surface Metamodeling
Zhang, Ning
Apley, Daniel W.
JOURNAL OF QUALITY TECHNOLOGY, 2014, 46 (04) : 285 - 301
[22] Velocity Landscapes Resolve Multiple Dynamical Populations from Fluorescence Image Time Series
Pandzic, Elvis
Abu-Arish, Asmahan
Whan, Renee M.
Hanrahan, John
Wiseman, Paul W.
BIOPHYSICAL JOURNAL, 2017, 112 (03) : 296A - 296A
[23] A New Approach for Time Series Forecasting: Bayesian Enhanced by Fractional Brownian Motion with Application to Rainfall Series
Rodriguez Rivero, Cristian
Patino, Daniel
Pucheta, Julian
Sauchelli, Victor
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2016, 7 (03) : 237 - 244
[24] Optimal series representation of fractional Brownian sheets
Kühn, T
Linde, W
BERNOULLI, 2002, 8 (05) : 669 - 696
[25] Stochastic Interpolation of Sparsely Sampled Time Series via Multipoint Fractional Brownian Bridges
Friedrich, J.
Gallon, S.
Pumir, A.
Grauer, R.
PHYSICAL REVIEW LETTERS, 2020, 125 (17)
[26] Investigating long and short memory in cryptocurrency time series by stochastic fractional Brownian models
Ballestra, Luca Vincenzo
Molent, Andrea
Pacelli, Graziella
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2024, 53 (09) : 4596 - 4606
[27] Fuzzy clustering to classify several time series models with fractional Brownian motion errors
Mahmoudi, Mohammad Reza
Baleanu, Dumitru
Qasem, Sultan Noman
Mosavi, Amirhosein
Band, Shahab S.
ALEXANDRIA ENGINEERING JOURNAL, 2021, 60 (01) : 1137 - 1145
[28] Automated Continuous Fields Prediction From Landsat Time Series: Application to Fractional Impervious Cover
Hakkenberg, Christopher R.
Dannenberg, Matthew P.
Song, Conghe
Vinci, Giuseppe
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2020, 17 (01) : 132 - 136
[29] Dynamical simulation analysis based on time fractional tranmission line model
Zhang Yanzhu
Xue Dingyu
2006 7TH INTERNATIONAL SYMPOSIUM ON ANTENNAS, PROPAGATION AND EM THEORY, VOLS 1 AND 2, PROCEEDINGS, 2006, : 601 - 604
[30] Image encryption technique based on fractional chaotic time series
Wu, Guo-Cheng
Baleanu, Dumitru
Lin, Zhen-Xiang
JOURNAL OF VIBRATION AND CONTROL, 2016, 22 (08) : 2092 - 2099

← 1 2 3 4 5 →