Next:
Abstract
Contents
Index
Recognition of vehicle make from a frontal view.
Michal Conos
Abstract
Notation
Contents
List of Figures
List of Tables
Introduction
Problem statement
Structure of thesis
State of the art
Appearance Based Methods
Approach of T. Kato, Y. Ninonmiya and I. Masaki [17]
Approach of V.S. Petrovic, T.F. Cootes [19]
Model Based Methods
Summary
Proposed method
Structure of Proposed Method
Frontal Mask Localization and Geometric Image Normalization
Locating Car Frontal Mask
Geometric Image Normalization
Feature Extraction and Photometric Normalization
Classification
Detailed description of components
Distinctive Image Features from Scale-Invariant Keypoints -- SIFT
Features extraction
Overlapping tiles
Feature vector construction
Error function
Euclidean distance
Earth Mover's Distance -- EMD
Classifiers used
k-NN
-- The Nearest Neighbor
FLD - Fisher Linear Discriminant
Experiments
Database description
Car images
Truck images
Parameters
SIFT representation
Used classifiers
k-NN performed on car database
Optimal SIFT topology
SIFT representation
Euclidean versus EMD error function
Best k for k-NN
k-NN Performed on truck database
SIFT topology
SIFT representation comparison
EMD vs. Euclidean distance
Best k for k-NN
Fisher's linear discriminator (FLD) performed on car images
Optimal dimension
Fisher's linear discriminator (FLD) performed on truck images
Optimal dimension
Experimental validation
Sensitivity to learning set reduction
Sensitivity to image noise
Sensitivity to image blur
k-NN stability on truck images
Sensitivity to learning set reduction
Sensitivity to image blur
Sensitivity to image added noise
k-NN stability on car images
Sensitivity to learning set reduction
Sensitivity to image blur
Sensitivity to image added noise
Conclusion
Discussion and further research
Software design
Architecture overview
Program flow
Detailed modules design
Feature extraction module
Error module
Classification module
Index
Bibliography
About this document ...
Kocurek 2007-12-17
