This volume introduces the fundamental concepts and tools
involved in the design and implementation of
object
recognition systems. Divided into three parts, it first
introduces the topic and covers the acquisition of images,
then details 3-D object reconstruction, modelling and
matching, and finally describes typical recognition systems
using
case studies. Key features include: Extensive
literature surveys of state-of-the-art systems An
FTP site
from which readers can obtain the MATLAB codes used to
generate some of the results found in the text:
ftp://ftp.springer.de/pub/cs/object_recognition/ Object
Recognition will be essential reading for research
scientists, advanced undergraduate and postgraduate
students in computer vision, image processing and pattern
classification. It will also be of interest to
practitioners working in the field of computer vision.
Contents
Part A - Introduction and
Acquisition Systems:
1. Introduction.
2. Stereo Matching and Reconstruction of a Depth Map.
Part A - Summary.
Part B - Database Creation and
Modeling for 3D Object Recognition:
3. 3-D Object Creation for Recognition.
4. Object Representation and Feature Matching.
Part B - Summary.
Part C - Vision Systems - Case
Studies:
5. Optical Character Recognition.
6. Recognition by Parts and Part Segmentation
Techniques.
7. 3D Object Recognition Systems.
Part C - Summary:
Appendices
A. Vector and Matrix Analysis.
B. Principal Component Analysis.
C. Optimisation Fundamentals.
D. Differential Geometry - Basic Principles.
E. Spline Theory.
F. Detailed Derivation of Registration Equations.
References.
Index.