Combining Gradient and Albedo Data for Rotation Invariant Classification of 3D Surface Texture

Authors:
Jiahua Wu;Mike J. Chantler
Affiliations:
-;-
Venue:
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Year:
2003

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Abstract

We present a new texture classification scheme whichis invariant to surface-rotation. Many textureclassification approaches have been presented in the pastthat are image-rotation invariant, However, imagerotation is not necessarily the same as surface rotation.We have therefore developed a classifier that usesinvariants that are derived from surface properties ratherthan image properties. Previously we developed ascheme that used surface gradient (normal) fieldsestimated using photometric stereo. In this paper weaugment these data with albedo information and an alsoemploy an additional feature set: the radial spectrum.We used 30 real textures to test the new classifier. Aclassification accuracy of 91% was achieved whenalbedo and gradient 1D polar and radial features werecombined. The best performance was also achieved byusing 2D albedo and gradient spectra. The classificationaccuracy is 99%.