Two-dimensional multiscale entropy analysis: Applications to image texture evaluation

Silva, Luiz Eduardo Virgilio da; Duque, Juliano J.; Felipe, Joaquim Cezar; Murta, Leonardo; Humeau-Heurtier, Anne

doi:10.1016/j.sigpro.2018.02.004

Cited by 53 publications

(36 citation statements)

References 29 publications

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“…The bi-dimensional multiscale entropy (MSE 2D ) is an extension, over spatial scales τ , of SampEn 2D [252], [255]. For an image I with width W and height H , MSE 2D consists of two steps [255] 1) Construct the coarse-grained images {I (τ ) } as…”

Section: Two-dimensional Multiscale Entropy 1) Conceptmentioning

confidence: 99%

“…2) Compute SampEn 2D of each coarse-grained image. A variant of MSE 2D (ModMSE 2D ) has also been proposed [255]. ModMSE 2D is SampEn 2D (I (τ ) , m, r) where I (τ ) is, in this case, computed as…”

Section: Two-dimensional Multiscale Entropy 1) Conceptmentioning

confidence: 99%

“…2) ADVANTAGES AND LIMITATIONS MSE 2D has the advantage, over SampEn 2D and DistEn 2D , of giving information over spatial scales. Moreover, the computation time of MSE 2D is faster than the one of ModMSE 2D , while ModMSE 2D is more robust to small image sizes [255]: MSE 2D can generate undefined entropy values due to the absence of template matches. This is even more obvious with scales: the larger the scale value, the higher the probability of obtaining an undefined entropy value.…”

Section: Two-dimensional Multiscale Entropy 1) Conceptmentioning

confidence: 99%

“…3) EXAMPLES OF APPLICATIONS ModMSE 2D and MSE 2D have been applied to synthetic and real data and have proven to be suitable and powerful tools for image classification according to texture patterns [255]. MSE 2D has also been used in the biomedical field [252], [256].…”

Section: Two-dimensional Multiscale Entropy 1) Conceptmentioning

confidence: 99%

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Texture Feature Extraction Methods: A Survey

2019

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Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract-from raw images-meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropybased approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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Section: Two-dimensional Multiscale Entropy 1) Conceptmentioning

confidence: 99%

Section: Two-dimensional Multiscale Entropy 1) Conceptmentioning

confidence: 99%

Section: Two-dimensional Multiscale Entropy 1) Conceptmentioning

confidence: 99%

Section: Two-dimensional Multiscale Entropy 1) Conceptmentioning

confidence: 99%

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Texture Feature Extraction Methods: A Survey

2019

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“…These functions, which have been derived from the definition of entropy function [26], serve as a means to extract superior features from images so as to improve the accuracy of the proposed identification system. Next, the value of the central pixel is obtained as follows:…”

Section: Entropy-based Patternsmentioning

confidence: 99%

A new biometric identity recognition system based on a combination of superior features in finger knuckle print images

Heidari¹,

Chalechale²

2020

Turk J Elec Eng & Comp Sci

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Biometric methods are among the safest and most secure solutions for identity recognition and verification.One of the biometric features with sufficient uniqueness for identity recognition is the finger knuckle print (FKP). This paper presents a new method of identity recognition and verification based on FKP features, where feature extraction is combined with an entropy-based pattern histogram and a set of statistical texture features. The genetic algorithm (GA) is then used to find the superior features among those extracted. After extracting superior features, a support vector machine-based feedback scheme is used to improve the performance of the biometric system. Two datasets called Poly-U FKP and FKP are used for performance evaluation. The proposed method managed to achieve 94.91% and 98.5% recognition rates on the Poly-U FKP and FKP datasets and outperformed all of the existing methods in this respect. These results demonstrate the potential of this method as a simple yet effective solution for FKP-based identity recognition. Key words:Entropy-based pattern, texture feature, genetic algorithm, biometric, finger knuckle print based coding scheme was used for FKP recognition [12]. In [13], the local orientation information extracted by Gabor filters and the Fourier transform coefficients were considered, respectively, as local and global features of FKP images. Woodard et al. used a sensor to create a three-dimensional hand dataset and then utilized the features extracted from fingers for identification [14]. However, the cost, size, and weight of the sensor and the prolonged data retrieval and processing of this method limit its use as a practical biometric solution. In a study by Ferrer et al., they developed a biometric identification system that considers the FKP as a biometric feature and uses the hidden Markov model in the identification phase [15]. However, this method has an extensive processing phase and works well only when a limited amount of data is being processed. Rawikanth et al. used the FKP region to detect the orientation and scale of finger images. However, the subspace analysis method used in the feature extraction phase was not able to extract the features of the knuckles very effectively [16].

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Extended state observer‐based finite‐region control for 2‐D Markov jump systems

Peng

Dong

et al. 2022

Intl J Robust & Nonlinear

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In this article, an extended state observer‐based finite‐region control scheme is presented for two‐dimensional Markov jump systems with unknown mismatched disturbances. The mathematical model of the two‐dimensional Markov jump systems is built on the well‐known Roesser model. By establishing special recursive formulas and utilizing the 2‐D Lyapunov function theory, sufficient conditions are obtained, which prove that the resultant system is finite‐region bounded, if some linear matrix inequalities are achieved. Then, we provide an algorithm to solve the extended state observer‐based controller gains. With the proposed control scheme, the external disturbances can be actively rejected from the system outputs. To conclude, a numerical example based on the Darboux equation is provided to demonstrate the validity and effectiveness of the devised control scheme.

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Two-dimensional multiscale entropy analysis: Applications to image texture evaluation

Cited by 53 publications

References 29 publications

Texture Feature Extraction Methods: A Survey

Texture Feature Extraction Methods: A Survey

A new biometric identity recognition system based on a combination of superior features in finger knuckle print images

Extended state observer‐based finite‐region control for 2‐D Markov jump systems

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