Title

Rotation Invariant Roughness Features For Texture Classification

Abstract

In this paper, we introduce a rotational invariant feature set for texture classification, based on an extension of fractal dimension (FD) features. The FD extracts roughness information from images considering all available scales at once. In this work, a single scale is considered at a time so that textures with scale-dependent properties are satisfactorily characterized. Single scale features are combined with multiple scale features for a more complete textural representation. Directional wavelets are employed for the computation of roughness features, because of their ability to extract information at different resolutions and directions. The final feature vector is rotational invariant and retains the texture directional information. The roughness feature set results in higher classification rate than other feature vectors presented in this work, while preserving the important properties of FD, namely insensitivity to absolute illumination and contrast.

Publication Date

1-1-2002

Publication Title

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volume

4

Number of Pages

3672-3675

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICASSP.2002.5745452

Socpus ID

0036288467 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0036288467

This document is currently not available here.

Share

COinS