 PhD
Thesis
Title - "Rotation Invariant Classification
of 3D Surface Texture Using Photometric Stereo"
Texture lab, Department of Computer Science, School of Mathematical
and Computer Sciences, Heriot-Watt University, Edinburgh, 2002.
Supervisor
- Porf. Mike J Chantler, Heriot-Watt University,
Edinburgh.
Examiners
Abstract
This thesis presented a new three-dimensional surface texture
classification scheme which was invariant to surface-rotation using
photometric stereo. Many texture classification approaches had
been presented in the past that were image-rotation invariant,
however, image rotation was
not necessarily the same as surface
rotation. A classifier therefore had been developed
that used invariants that were derived from surface properties
rather than image properties.
Firstly, various surface models were considered and a classification
scheme was developed that used magnitude spectra of the partial
derivatives of the surface obtained using photometric stereo. A
simple frequency domain method of removing the directional artefacts
of partial derivatives was presented, and a 1D feature set of polar
spectrum was also extracted from resulting spectrum. Classification
was performed by comparing training and classification polar spectra
over a range of rotations. Secondly, a new feature generator albedo
spectrum was introduced to provide more information on surface
texture properties, and an additional 1D feature set of the radial
spectrum was employed too. In addition, by examining the effect
of shadowing, a four-image photometric stereo method was developed
to provide more accurate three-dimensional surface properties.
Finally, a verification step was included in the classification
where the 2D spectrum features were compared instead of 1D spectrum
features.
The classification results using new-developed
photometric stereo real texture database shown that combining
2D gradient and albedo data improves the classification's performance
to provide a successful classification rate of 99%.
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