Combining gradient and albedo data for rotation invariant classification of 3D surface texture

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

We present a new texture classification scheme which is invariant to surface-rotation. Many texture classification approaches have been presented in the past that are image-rotation invariant, However, image rotation is not necessarily the same as surface rotation. We have therefore developed a classifier that uses invariants that are derived from surface properties rather than image properties. Previously we developed a scheme that used surface gradient (normal) fields estimated using photometric stereo. In this paper we augment these data with albedo information and an also employ an additional feature set: the radial spectrum. We used 30 real textures to test the new classifier. A classification accuracy of 91% was achieved when albedo and gradient ID polar and radial features were combined. The best performance was also achieved by using 2D albedo and gradient spectra. The classification accuracy is 99%.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
Pages848-855
Number of pages8
Volume2
Publication statusPublished - 2003
EventNINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION - Nice, France
Duration: 13 Oct 200316 Oct 2003

Conference

ConferenceNINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION
CountryFrance
CityNice
Period13/10/0316/10/03

Fingerprint Dive into the research topics of 'Combining gradient and albedo data for rotation invariant classification of 3D surface texture'. Together they form a unique fingerprint.

  • Cite this

    Wu, J., & Chantler, M. J. (2003). Combining gradient and albedo data for rotation invariant classification of 3D surface texture. In Proceedings of the IEEE International Conference on Computer Vision (Vol. 2, pp. 848-855)