A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval

Ves, Esther de; Acevedo, Daniel; Ruedin, Ana M. C.; Benavent, Xaro

doi:10.1016/j.patcog.2014.03.004

Cited by 30 publications

(3 citation statements)

References 30 publications

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“…Gaussian distribution is one of the most commonly used models for texture analysis [24,28,29]. Rayleigh, Weibull [30,31], and Wishart [25] distributions have been also used for texture feature extraction. However, due to its shape and properties, each of these distributions is suitable for a specific type of data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many researchers have used texture modeling and feature extraction in multiresolution domains like wavelet transform [26,27]. Most of these methods use transform coefficients' energy [28], fractal dimension [20], or statistical modeling parameters [29][30][31] as texture features. Different multiresolution transforms have been introduced in recent years.…”

Section: Literature Reviewmentioning

confidence: 99%

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Skewed alpha-stable distribution for natural texture modeling and segmentation in contourlet domain

Sarafrazi

Yazdi

2019

J Image Video Proc.

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Texture modeling is a very useful tool in image analysis. This model can be used in texture segmentation, denoising or texture synthesis. In this work, alpha-stable distribution has been proposed to model and segment textured images in contourlet domain. Contourlet transform's ability to extract texture features in different scales and directions combined with alpha-stable distribution's modeling capabilities prove to be an effective method for texture feature extraction. Kolmogorov-Smirnov distance has been used to evaluate how well the proposed distribution fits to the image in the contourlet domain. The performance of the proposed features on image segmentation has been also compared with that of features extracted using different texture analysis methods in the presence of noise. Experimental results have demonstrated the superior performance of the proposed features and their robust performance in the presence of noise.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Skewed alpha-stable distribution for natural texture modeling and segmentation in contourlet domain

Sarafrazi

Yazdi

2019

J Image Video Proc.

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“…There are many classical texture characterization methods in the literature that can be divided into four different categories: statistical-based (e.g., gray-level co-occurrence matrices (GLCM) [22] and local binary patterns (LBP) [32]), spectral methods (e.g. Gabor filters [24] and wavelet transform [41]), structural methods (e.g. morphological decomposition [27]) and model-based methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Fractal-Based Approach to Network Characterization Applied to Texture Analysis

Ribas

Manzanera

Bruno

2019

Computer Analysis of Images and Patterns

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This work proposes a new method for texture analysis that combines fractal descriptors and complex network modeling. At first, the texture image is modeled as a network. Then, the network is converted into a surface where the Cartesian coordinates and the vertex degree is mapped into a 3D point in the surface. Then, we calculate a description vector of this surface using a method inspired by the Bouligand-Minkowski technique for estimating the fractal dimension of a surface. Specifically, the descriptor corresponds to the evolution of the volume occupied by the dilated surface, when the radius of the spherical structuring element increases. The feature vector is given by the concatenation of the volumes of the dilated surface for different radius values. Our proposal is an enhancement of the classic complex networks descriptors, where only the statistical information was considered. Our method was validated on four texture datasets and the results reveal that our method leads to highly discriminative textural features.

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Video Watermarking for Persistent and Robust Tracking of Entertainment Content (PARTEC)

Bhowmik

Abhayaratne

Green

2020

Advances in Intelligent Systems and Computing

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The exploitation of film and video content on physical media, broadcast and Internet involves working with many large media files. The move to filebased workflows necessitates the copying and transfer of digital assets amongst many parties, but the detachment of assets and their metadata leads to issues of reliability, quality and security. This paper proposes a novel watermarking based approach to deliver a unique solution to enable digital media assets to be maintained with their metadata persistently and robustly. Watermarking based solution for entertainment content manifests new challenges, including maintaining high quality of the media content, robustness to compression and file format changes and synchronisation against scene editing. The proposed work addresses these challenges and demonstrates interoperability with existing industrial software framework for media asset management (MAM) systems.

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A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval

Cited by 30 publications

References 30 publications

Skewed alpha-stable distribution for natural texture modeling and segmentation in contourlet domain

Skewed alpha-stable distribution for natural texture modeling and segmentation in contourlet domain

A Fractal-Based Approach to Network Characterization Applied to Texture Analysis

Video Watermarking for Persistent and Robust Tracking of Entertainment Content (PARTEC)

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