<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive semi-definite matrices is used to estimate the structural information represented by the local orientation and the degree of anisotropy in structured and sinusoid-like textured images. In order to ensure faithful local structure estimation, an adaptive algorithm for the regularization of the extent of gradient fields smoothing is proposed. Results obtained on different texture samples show the strength of the proposed method in accurately representing the local variation of orientations in the underlying textured images, which paves the way towards an accurate analysis of the texture structures.</p></abstract>

This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive semi-definite matrices is used to estimate the structural information represented by the local orientation and the degree of anisotropy in structured and sinusoid-like textured images. In order to ensure faithful local structure estimation, an adaptive algorithm for the regularization of the extent of gradient fields smoothing is proposed. Results obtained on different texture samples show the strength of the proposed method in accurately representing the local variation of orientations in the underlying textured images, which paves the way towards an accurate analysis of the texture structures.

Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images

Department of Telecommunications, Faculty of Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive...

Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images

Online veröffentlicht: 30. März 2018

Seitenbereich: 51 - 68

Eingereicht: 14. März 2017

Akzeptiert: 20. Dez. 2017

DOI: https://doi.org/10.2478/cait-2018-0005

Schlüsselwörter
Binary Image, Coherence Factor, Gaussian Kernel, Gradient Field, Image Structure, Orientation, Symmetric Positive Semi-Definite Matrix, Textured Images

© 2018 Adib Akl, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images

Online veröffentlicht: 30. März 2018

Seitenbereich: 51 - 68

Eingereicht: 14. März 2017

Akzeptiert: 20. Dez. 2017

DOI: https://doi.org/10.2478/cait-2018-0005

SchlüsselwörterBinary Image, Coherence Factor, Gaussian Kernel, Gradient Field, Image Structure, Orientation, Symmetric Positive Semi-Definite Matrix, Textured Images

© 2018 Adib Akl, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Binary Image, Coherence Factor, Gaussian Kernel, Gradient Field, Image Structure, Orientation, Symmetric Positive Semi-Definite Matrix, Textured Images