3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms.

Details

Serval ID
serval:BIB_8DD9483F11D4
Type
Article: article from journal or magazin.
Collection
Publications
Title
3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms.
Journal
IEEE transactions on image processing
Author(s)
Dicente Cid Y., Muller H., Platon A., Poletti P., Depeursinge A.
ISSN
1941-0042 (Electronic)
ISSN-L
1057-7149
Publication state
Published
Issued date
04/2017
Peer-reviewed
Oui
Volume
26
Number
4
Pages
1899-1910
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3-D solid images. Computational analysis of these images is complex but necessary since it is difficult for humans to visualize and quantify their detailed 3-D content. One of the most common methods to analyze 3-D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3-D. Current state-of-the- art techniques face many challenges when working with 3-D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3-D Riesz- wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher-order 3-D Riesz-wavelet transforms. The estimations of local texture orientations are based on higher- order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3-D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which is better than all other techniques that are published and tested on the same database.
Keywords
Computer Graphics and Computer-Aided Design, Software
Pubmed
Web of science
Funding(s)
Swiss National Science Foundation / 320030-146804
Swiss National Science Foundation / PZ00P2-154891
Create date
29/08/2023 8:44
Last modification date
09/10/2023 15:30
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