Ph.D. thesis:
Jiahua Wu
Rotation Invariant Classification of 3D Surface Texture Using Photometric Stereo
Heriot-Watt University,
2003.
Entire Thesis PDF
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%.
.
Table of Contents
Preliminary Pages (chapter0.pdf)
Chapter 1 Introduction (chapter1.pdf)
Chapter 2 Rotation Invariant Texture Classification (chapter2.pdf)
Chapter 3 From Surface to Image (chapter3.pdf)
Chapter 4 Photometric Stereo (chapter4.pdf)
Chapter 5 Gradient Space (chapter5.pdf)
Chapter 6 An Algorithm of Rotation Invariant Texture Classification (chapter6.pdf)
Chapter 7 Experiment and Results (chapter7.pdf)
Chapter 8 A New Classification Feature Space and A New Feature Generator (chapter8.pdf)
Chapter 9 Classification Scheme Using Modified Photometric Stereo and 2D Spectra Comparison (chapter9.pdf)
Chapter 10 Summary and Conclusion (chapter10.pdf)
References (References.pdf)
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