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Ph.D. thesis: G. McGunnigle
The Classification of Textured Surfaces Under Varying Illuminant Direction
Abstract
This thesis sets texture analysis in a physical context. Models of the
system components are obtained from the literature and integrated into
a description of the process linking the rough surface to the feature set
on which classification is based. The first component is the rough surface,
models of the surface topography are selected from the fields of tribology
and scattering. Various reflectance models are considered and a spectral
model of the surface/image relationship from the literature, is evaluated
and discussed. The relationship between the incident image and the captured
data set is investigated and described. This model is integrated with the
spectral description of the feature measures to form a model of the transition
from surface to feature set.
It is clear from this model that the direction of illumination can affect the directionality of an image obtained from a given surface. Changes in the illuminant direction will result in changes in the feature outputs. If the illuminant direction is altered between training and classification, the classification rule may be inappropriate and classification poor. Several schemes are considered to combat this problem. A technique which uses a representation of the physical surface as the basis for the generation of appropriate training data is selected for further evaluation. The surface derivative fields of the training surface are estimated using photometric techniques. A rendering algorithm uses these estimates to simulate the appearance of the training surface when it is illuminated from an arbitrary direction. It is shown that where illuminant direction is varied this system is able to perform significantly better than a naive classifier, and in some cases approaches the level of accuracy obtained from training the classifier under the conditions at which classification is performed. Table of ContentsPreliminary Pages (chapter0.pdf)Chapter 1 Introduction (chapter1.pdf) Chapter 2 Surfaces (chapter2.pdf) Chapter 3 Image Formation (chapter3.pdf) Chapter 4 Image Capture (chapter4.pdf) Chapter 5 A Classification System (chapter5.pdf) Chapter 6 Modelling the Classifier Tilt Response. (chapter6.pdf) Chapter 7 Addressing the problem. (chapter7.pdf) Chapter 8 A Simulation-based Approach to Tilt Effects (chapter8.pdf) Chapter 9 Summary and Conclusions (chapter9.pdf) References (references.pdf) Appendix (Appendix.pdf)
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