Series in Machine Perception and Artificial Intelligence - Vol. 42
INVARIANTS FOR PATTERN RECOGNITION AND CLASSIFICATION
edited by Marcos A Rodrigues (Sheffield Hallam University, UK)
This book was conceived from the realization that there was a need to update recent work on invariants in a single volume providing a useful set of references and pointers to related work. Since the publication in 1992 of J L Mundy and A Zisserman's Geometric Invariance in Computer Vision, the subject has been evolving rapidly. New approaches to invariants have been proposed and novel ways of defining and applying invariants to practical problem solving are testimony to the fundamental importance of the study of invariants to machine vision. This book represents a snapshot of current research around the world.
A version of this collection of papers has appeared in the International Journal of Pattern Recognition and Artificial Intelligence (December 1999). The papers in this book are extended versions of the original material published in the journal. They are organized into two categories: foundations and applications. Foundation papers present new ways of defining or analyzing invariants, and application papers present novel ways in which known invariant theory is extended and effectively applied to real-world problems in interesting and difficult contexts. Each category contains roughly half of the papers, but there is considerable overlap. All papers carry an element of novelty and generalization that will be useful to theoreticians and practitioners alike. It is hoped that this volume will be not only useful but also inspirational to researchers in image processing, pattern recognition and computer vision at large.
Contents:
- Analysis and Computation of Projective Invariants from Multiple
Views in the Geometric Algebra Framework
- Invariants to Convolution and Rotation
- A New Representation for Quartic Curves and Complete Sets of Geometric Invariants
- A Robust Affine Invariant Metric on Boundary Patterns
- Invariant Geometric Properties of Image Correspondence Vectors as Rigid Constraints to Motion Estimation
- Features of Derivative Continuity in Shape
- Fourier–Mellin Based Invariants for the Recognition of Multi-Oriented and Multi-Scaled Shapes — Application to Engineering Drawing Analysis
- High-Order Statistical Pattern Spectrum: An Invariant and Noise-Robust Shape Descriptor
- Improved Moment Invariants for Invariant Image Representation
- An Approach Using Elastic Graph Dynamic Link Model for Automating the Satellite Interpretation of Tropical Cyclone Patterns
- Colour Normalization for Colour Object Recognition and Image Retrieval
Readership: Researchers in pattern recognition and computer vision.
| 248pp |
Pub. date: Jan 2001 |
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