Shape from shading with non-parallel light source.

by Siu-Yuk Yeung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 96-102).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.5Chapter 1.1 --- Shape recovery techniques --- p.5Chapter 1.2 --- Shape from Shading algorithms --- p.8Chapter 1.2.1 --- Some developments on surface reflection --- p.9Chapter 1.2.2 --- Some developments on computing methods --- p.11Chapter 1.2.3 --- Some developments on light source model --- p.12Chapter 1.3 --- Proposed algorithms in this thesis --- p.13Chapter 1.4 --- Thesis outline --- p.14Chapter 2 --- Camera and surface reflectance models for SFS --- p.15Chapter 2.1 --- Camera models for SFS --- p.16Chapter 2.1.1 --- Pinhole camera model and perspective projection --- p.17Chapter 2.1.2 --- Approximations of perspective projection --- p.20Chapter 2.2 --- Surface reflectance models for SFS --- p.22Chapter 2.2.1 --- Lambertian surface model --- p.23Chapter 2.2.2 --- Bidirectional Reflectance Distribuction Function --- p.23Chapter 2.3 --- Summary --- p.25Chapter 3 --- Review of some related SFS algorithms --- p.26Chapter 3.1 --- The SFS algorithm proposed by Bichsel and Pentland --- p.27Chapter 3.1.1 --- Determine surface height with a minimum downhill principle --- p.28Chapter 3.1.2 --- Implementation on a discrete grid --- p.30Chapter 3.2 --- The SFS algorithm proposed by Kimmel and Bruckstein --- p.31Chapter 3.2.1 --- Level set propagation --- p.32Chapter 3.2.2 --- Problem formulation --- p.33Chapter 3.2.3 --- Equal height contour propagation using level set method --- p.35Chapter 3.3 --- Summary --- p.36Chapter 4 --- Multiple extended light source models for SFS --- p.38Chapter 4.1 --- Three extended light source models for SFS --- p.40Chapter 4.1.1 --- Rectangular light source model --- p.40Chapter 4.1.2 --- Spherical light source model --- p.43Chapter 4.1.3 --- Cylindrical light source model --- p.48Chapter 4.2 --- SFS for an extended light source --- p.53Chapter 4.3 --- Multiple extended light source model --- p.53Chapter 4.4 --- Simulation and experiment result --- p.54Chapter 4.5 --- Error Analysis --- p.55Chapter 4.5.1 --- Descriptions of the error --- p.55Chapter 4.5.2 --- Errors for different light models --- p.55Chapter 4.6 --- Summary --- p.57Chapter 5 --- Global SFS for an endoscope image --- p.70Chapter 5.1 --- Introduction --- p.71Chapter 5.2 --- Local SFS algorithm for endoscope image --- p.73Chapter 5.2.1 --- Imaging system and brightness formulation --- p.74Chapter 5.2.2 --- Equal distance contour propagation and shape reconstruc- tion --- p.75Chapter 5.3 --- Global SFS algorithm for endoscope image --- p.76Chapter 5.3.1 --- A global shape from shading algorithm for a parallel light --- p.77Chapter 5.3.2 --- The relationship between depth map and distance map --- p.78Chapter 5.3.3 --- A global shape from shading algorithm for endoscope image --- p.78Chapter 5.4 --- Simulations and experiments results --- p.83Chapter 5.5 --- Summary --- p.86Chapter 6 --- Summary and conclusion --- p.87Chapter 6.1 --- Problems tackled in this thesis --- p.87Chapter 6.2 --- Discussion on future developments --- p.8

Recognition Using Motion And Shape

Our goal is to design a recognition system which can distinguish between two objects with the same shape, but different motion or between two objects with the same motion but a different shape. In this paper, we present a method for matching sets of trajectories which supplements motion information with knowledge about the spatial relationships between points on the moving object. First we present a simple algorithm which matches two single trajectories using only motion information. We convert the 2-D motion trajectories into two 1-D signals based on the speed and direction components. The signals are then represented by scale-space images both to simplify matching and because the scale-space representations are translation and rotation invariant. We extend the matching algorithm to include spatial information and propose a second algorithm which matches multiple trajectories by combining motion and spatial match scores. Both algorithms were tested with real and synthetic data