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Moments and moment invariants in pattern recognition /
Moments as projections of an image's intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a su...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex, U.K. ; Hoboken, N.J. :
J. Wiley,
2009.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover
- Contents
- Authors biographies
- Preface
- Acknowledgments
- 1 Introduction to moments
- 1.1 Motivation
- 1.2 What are invariants?
- 1.2.1 Categories of invariant
- 1.3 What are moments?
- 1.3.1 Geometric and complex moments
- 1.3.2 Orthogonal moments
- 1.4 Outline of the book
- References
- 2 Moment invariants to translation, rotation and scaling
- 2.1 Introduction
- 2.1.1 Invariants to translation
- 2.1.2 Invariants to uniform scaling
- 2.1.3 Traditional invariants to rotation
- 2.2 Rotation invariants from complex moments
- 2.2.1 Construction of rotation invariants
- 2.2.2 Construction of the basis
- 2.2.3 Basis of invariants of the second and third orders
- 2.2.4 Relationship to the Hu invariants
- 2.3 Pseudoinvariants
- 2.4 Combined invariants to TRS and contrast changes
- 2.5 Rotation invariants for recognition of symmetric objects
- 2.5.1 Logo recognition
- 2.5.2 Recognition of simple shapes
- 2.5.3 Experiment with a baby toy
- 2.6 Rotation invariants via image normalization
- 2.7 Invariants to nonuniform scaling
- 2.8 TRS invariants in 3D
- 2.9 Conclusion
- References
- 3 Affine moment invariants
- 3.1 Introduction
- 3.1.1 Projective imaging of a 3D world
- 3.1.2 Projective moment invariants
- 3.1.3 Affine transformation
- 3.1.4 AMIs
- 3.2 AMIs derived from the Fundamental theorem
- 3.3 AMIs generated by graphs
- 3.3.1 The basic concept
- 3.3.2 Representing the invariants by graphs
- 3.3.3 Independence of the AMIs
- 3.3.4 The AMIs and tensors
- 3.3.5 Robustness of the AMIs
- 3.4 AMIs via image normalization
- 3.4.1 Decomposition of the affine transform
- 3.4.2 Violation of stability
- 3.4.3 Relation between the normalized moments and the AMIs
- 3.4.4 Affine invariants via half normalization
- 3.4.5 Affine invariants from complex moments
- 3.5 Derivation of the AMIs from the CayleyAronhold equation
- 3.5.1 Manual solution
- 3.5.2 Automatic solution
- 3.6 Numerical experiments
- 3.6.1 Digit recognition
- 3.6.2 Recognition of symmetric patterns
- 3.6.3 The childrens mosaic
- 3.7 Affine invariants of color images
- 3.8 Generalization to three dimensions
- 3.8.1 Method of geometric primitives
- 3.8.2 Normalized moments in 3D
- 3.8.3 Half normalization in 3D
- 3.8.4 Direct solution of the CayleyAronhold equation
- 3.9 Conclusion
- Appendix
- References
- 4 Implicit invariants to elastic transformations
- 4.1 Introduction
- 4.2 General moments under a polynomial transform
- 4.3 Explicit and implicit invariants
- 4.4 Implicit invariants as a minimization task
- 4.5 Numerical experiments
- 4.5.1 Invariance and robustness test
- 4.5.2 ALOI classification experiment
- 4.5.3 Character recognition on a bottle
- 4.6 Conclusion
- References
- 5 Invariants to convolution
- 5.1 Introduction
- 5.2 Blur invariants for centrosymmetric PSFs
- 5.2.1 Template matching experiment
- 5.2.2 Invariants to linear motion blur
- 5.2.3 Extension to n dimensions
- 5.2.4 Possible applications and limitations
- 5.3 Blur invariants for N-fold symmetric PSFs
- 5.3.1 Blur invariants for circularly symmetric PSFs
- 5.3.2 Blur invariants for Gaussian PSFs
- 5.4 Combined invariants
- 5.4.1 Combined i.