Local multifractal detrended fluctuation analysis for non-stationary image's texture segmentation

Wang, Fang; Zong-shou, LI; Li, Jinwei

doi:10.1016/j.apsusc.2014.10.065

Cited by 22 publications

(12 citation statements)

References 27 publications

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“…Moreover, we propose to study the following natural estimator for the multifractal correlation ⇢ mf , cf. (7), defined as⇢…”

Section: Multifractal Estimation Performancementioning

confidence: 99%

“…The scale-free concept postulates that a large continuum of scales, rather than a small set of specific scales, all contribute to temporal dynamics. It has permitted significant contributions and successful analyses of numerous real-world applications very different in nature (cf., e.g., [4][5][6][7][8][9][10][11]). However and surprisingly, despite the fact that in many recent applications several time series are collected that need to be analyzed jointly, multifractal analysis remained univariate in essence, that is, even when several time series are jointly available, their analysis is conducted independently on each single one (see a contrario a few notable exceptions in, e.g., [12,13]).…”

Section: Introductionmentioning

confidence: 99%

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Assessing Cross-Dependencies Using Bivariate Multifractal Analysis

Wendt

Abry

Roux

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Multifractal analysis, notably with its recent wavelet-leader based formulation, has nowadays become a reference tool to characterize scale-free temporal dynamics in time series. It proved successful in numerous applications very diverse in nature. However, such successes remained restricted to univariate analysis while many recent applications call for the joint analysis of several components. Surprisingly, multivariate multifractal analysis remained mostly overlooked. The present contribution aims at defining a wavelet-leader based framework for multivariate multifractal analysis and at studying its properties and estimation performance. To better understand what properties of multivariate data are actually captured in multivariate multifractal analysis, a multivariate multifractal model is used as representative paradigm and permits to show that multivariate multifractal analysis puts in evidence transient and local dependencies that are not well quantified or even evidenced by the classical Pearson correlation coefficient.Index Terms-multivariate multifractal analysis, wavelet leaders, transient higher order dependencies

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“…Moreover, we propose to study the following natural estimator for the multifractal correlation ⇢ mf , cf. (7), defined as⇢…”

Section: Multifractal Estimation Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Cross-Dependencies Using Bivariate Multifractal Analysis

Wendt

Abry

Roux

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…A c c e p t e d M a n u s c r i p t 6 fitting effect of lnF q (s) vs. lns by using the distance error [11] which can evaluate the quality of the MF-DFA estimations. We plot F q (s) and s in a double log plot.…”

Section: Page 6 Of 13mentioning

confidence: 99%

“…[10] proved that the 2D MF-DFA was superior to traditional image processing methods and classical MFA by analyzing lots of natural textures, where the generalized Hurst exponent calculated by the modified 2D MF-DFA possess the best performance of noise immunity, resistance to compression and anti-blur. Some voluntary studies have been implemented for some specific image analysis by using the 2D MF-DFA [5,7,11,23,28] since then. For example, Yadav et al [23] used the technology to analyze LiF thin film surface.…”

Section: Introductionmentioning

confidence: 99%

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Magnetic resonance image segmentation using multifractal techniques

Wang

Liu

2015

Applied Surface Science

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The prediction model of nitrogen nutrition in cotton canopy leaves based on hyperspectral visible‐near infrared band feature fusion

Liu

et al. 2023

Biotechnology Journal

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Hyperspectral remote sensing technology is becoming increasingly popular in various fields due to its ability to provide detailed information about crop growth and nutritional status. The use of hyperspectral technology to predict SPAD (Soil and Plant Analyzer Development) values during cotton growth and adopt precise fertilization management measures is crucial for achieving high yield and fertilizer efficiency. To detect the nitrogen nutrition in cotton canopy leaves quickly, a non‐destructive nitrogen nutrition retrieval model was proposed based on the spectral fusion features of the cotton canopy. The hyperspectral vegetation index and multifractal features were fused to predict the SPAD value and identify the amount of fertilizer applied at different levels. The random decision forest algorithm was used as the model predictor and classifier. A method was introduced which was widely used in the fields of finance and stocks (MF‐DFA) into the field of agriculture to extract fractal features of cotton spectral reflectance. Comparing the fusion feature with multi‐fractal feature and vegetation index, the results showed that the fusion feature parameters had higher accuracy and better stability than using a single feature or feature combination. The R2 was as high as 0.8363, and the RMSE was 1.8767%. Our intelligent model provides a new idea for detecting nitrogen nutrition in cotton canopy leaves rapidly.

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Local multifractal detrended fluctuation analysis for non-stationary image's texture segmentation

Cited by 22 publications

References 27 publications

Assessing Cross-Dependencies Using Bivariate Multifractal Analysis

Assessing Cross-Dependencies Using Bivariate Multifractal Analysis

Magnetic resonance image segmentation using multifractal techniques

The prediction model of nitrogen nutrition in cotton canopy leaves based on hyperspectral visible‐near infrared band feature fusion

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