Ph.D. thesis: Christina Gullón

Height Recovery of Rough Surfaces from Intensity Images
Heriot-Watt University, February 2003.
Entire Thesis PDF

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Abstract

This thesis is concerned with the 3D estimation of rough surfaces from their intensity images. A technique which combines Photometric Stereo and frequency integration is proposed. The combination of these two standard methods for reconstructing rough surfaces is novel. We refer to this technique as the Benchmark technique. Two novel recovery algorithms which rely on assumptions about the linearity of the surface reflectance are also presented. We refer to them as the Optimal Linear Filter and the Linear Photometric Stereo. The proposed methods differ in the information that they require as well as in the assumptions that they make about the surface.

The ability of the proposed techniques to estimate rough surfaces is assessed using simulation and real data. The assessment considers a diverse set of textures including those that are challenging for the algorithms, such as very rough or specular surfaces.

The most robust estimation is given by the Optimal Linear Filter. However this technique requires information about the surface topography, which is usually not available. Between the alternatives, the Benchmark technique gives more accurate reconstructions.

A post-processing step which can be used to improve the surface estimate is presented. This minimises the brightness error using an iterative approach. When the Linear Photometric Stereo method is combined with the post-processing step, its performance is similar to that of the Benchmark technique, despite requiring one less image. However the Linear Photometric Stereo algorithm is restricted to constant albedo surfaces. The choice of the most appropriate method is determined by the application requirements.

 

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Table of contents

Preliminary Pages

Chapter 1  Introduction

Chapter 2 Topographics Texture

Chapter 3  Surface-to-Image Models

Chapter 4 Shape Recovery: A Review

Chapter 5 Shape Recovery: Two Novel Methods

Chapter 6 Optimal Lighting Conditions

Chapter 7 Assessment of Surface Estimation: Simulation

Chapter 8 Assesment of Surface Estimation: Experiment

Chapter 9 Improving the Surface Reconstruction

Chapter 10 Conclusions

Appendices

References

 

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