| Description |
1 online resource (xiii, 233 pages) : illustrations |
| Series |
Series in machine perception and artificial intelligence ; vol. 42 |
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Series in machine perception and artificial intelligence ; vol. 42.
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| Contents |
Ch. 1. Analysis and computation of projective invariants from multiple views in the geometric algebra framework. 1.1. Introduction. 1.2. Geometric algebra: an outline. 1.3. Projective geometry and the projective split. 1.4. 1D and 2D projective invariants from a single view. 1.5. 3D projective invariants from multiple views. 1.6. Experimental results. 1.7. Conclusions -- ch. 2. Invariants to convolution and rotation. 2.1. Introduction. 2.2. Mathematical background. 2.3. Invariants to convolution composed of the complex moments. 2.4. Combined invariants. 2.5. Additional invariance. 2.6. Testing the numerical properties. 2.7. Application to satellite image registration. 2.8. Conclusion -- ch. 3. A new representation for quartic curves and complete sets of geometric invariants. 3.1. Introduction. 3.2. Elliptical-circular (E2C) representation of quartic curves. 3.3. A complete set of geometric invariants. 3.4. Alignment. 3.5. Affine equivalent quartics. 3.6. Experiments. 3.7. Concluding remarks -- ch. 4. A robust affine invariant metric on boundary patterns. 4.1. Introduction. 4.2. Invariant metrics on patterns. 4.3. Robustness axioms. 4.4. Constructing invariant pattern metrics. 4.5. The reflection metric. 4.6. Experimental results. 4.7. Conclusion -- ch. 5. Invariant geometric properties of image correspondence vectors as rigid constraints to motion estimation. 5.1. Introduction. 5.2. The method: a geometric constraints framework for motion analysis. 5.3. Description of the algorithms. 5.4. Experimental results. 5.5. Conclusions -- ch. 6. Features of derivative continuity in shape. 6.1. Introduction. 6.2. Means and methods. 6.3. Demonstrations. 6.4. Discussion |
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Ch. 7. Fourier-Mellin based invariants for the recognition of multi-oriented and multi-scaled shapes -- application to engineering drawings analysis. 7.1. Introduction. 7.2. General interpretation structure. 7.3. Review of existing invariant pattern recognition techniques. 7.4. Invariant pattern recognition for multi-oriented and multi-scaled characters and symbols. 7.5. Experimental results. 7.6. Conclusion and perspectives -- ch. 8. High-order statistical pattern spectrum: an invariant and noise-robust shape descriptor. 8.1. Introduction. 8.2. Morphological shape descriptors. 8.3. High-order statistical pattern spectrum. 8.4. Results. 8.5. Conclusions -- ch. 9. Improved moment invariants for invariant image representation. 9.1. Introduction. 9.2. Basic theory of regular moments and symmetrical problem. 9.3. New moments. 9.4. New moments for rotated images. 9.5. New moments for noisy images. 9.6. Experimental study. 9.7. Conclusion -- ch. 10. An approach using elastic graph dynamic link model for automating the satellite interpretation of tropical cyclone patterns. 10.1. Introduction. 10.2. Satellite image interpretation. 10.3. Automatic pattern recognition techniques. 10.4. The dynamic link architecture. 10.5. The Active Contour Model (ACM). 10.6. The Elastic Graph Dynamic Link Model (EGDLM). 10.7. Implementation. 10.8. Conclusion and further work -- ch. 11. Colour normalization for colour object recognition and image retrieval. 11.1. Introduction. 11.2. Colour image formation. 11.3. Removing illumination dependency. 11.4. Experiments of colour based object recognition. 11.5. Conclusion |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Optical pattern recognition.
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Invariants.
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COMPUTERS -- Computer Vision & Pattern Recognition.
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COMPUTERS -- Optical Data Processing.
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Invariants
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Optical pattern recognition
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Invariante
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Klassifikation
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Mustererkennung
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| Form |
Electronic book
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| Author |
Rodrigues, Marcos A. (Marcos Aurelio)
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| ISBN |
9789812791894 |
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9812791892 |
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128193402X |
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9781281934024 |
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