Flusser / Zitova / Suk Moments and Moment Invariants in Pattern Recognition
1. Auflage 2009
ISBN: 978-0-470-68476-4
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
E-Book, Englisch, 312 Seiten, E-Book
ISBN: 978-0-470-68476-4
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
Moments as projections of an image's intensity onto a properpolynomial basis can be applied to many different aspects of imageprocessing. These include invariant pattern recognition, imagenormalization, image registration, focus/ defocus measurement, andwatermarking. This book presents a survey of both recent andtraditional image analysis and pattern recognition methods, basedon image moments, and offers new concepts of invariants to linearfiltering and implicit invariants. In addition to the theory,attention is paid to efficient algorithms for moment computation ina discrete domain, and to computational aspects of orthogonalmoments. The authors also illustrate the theory through practicalexamples, demonstrating moment invariants in real applicationsacross computer vision, remote sensing and medical imaging.
Key features:
* Presents a systematic review of the basic definitions andproperties of moments covering geometric moments and complexmoments.
* Considers invariants to traditional transforms -translation, rotation, scaling, and affine transform - from a newpoint of view, which offers new possibilities of designing optimalsets of invariants.
* Reviews and extends a recent field of invariants with respectto convolution/blurring.
* Introduces implicit moment invariants as a tool for recognizingelastically deformed objects.
* Compares various classes of orthogonal moments (Legendre,Zernike, Fourier-Mellin, Chebyshev, among others) and demonstratestheir application to image reconstruction from moments.
* Offers comprehensive advice on the construction of variousinvariants illustrated with practical examples.
* Includes an accompanying website providing efficient numericalalgorithms for moment computation and for constructing invariantsof various kinds, with about 250 slides suitable for a graduateuniversity course.
Moments and Moment Invariants in Pattern Recognition isideal for researchers and engineers involved in pattern recognitionin medical imaging, remote sensing, robotics and computer vision.Post graduate students in image processing and pattern recognitionwill also find the book of interest.
Autoren/Hrsg.
Weitere Infos & Material
Authors' biographies.
Preface.
Acknowledgments.
1 Introduction to moments.
1.1 Motivation.
1.2 What are invariants?
1.3 What are moments?
1.4 Outline of the book.
References.
2 Moment invariants to translation, rotation andscaling.
2.1 Introduction.
2.2 Rotation invariants from complex moments.
2.3 Pseudoinvariants.
2.4 Combined invariants to TRS and contrast changes.
2.5 Rotation invariants for recognition of symmetricobjects.
2.6 Rotation invariants via image normalization.
2.7 Invariants to nonuniform scaling.
2.8 TRS invariants in3D.
2.9 Conclusion.
References.
3 Affine moment invariants.
3.1 Introduction.
3.2 AMIs derived from the Fundamental theorem.
3.3 AMIs generated by graphs.
3.4 AMIs via image normalization.
3.5 Derivation of the AMIs from the Cayley-Aronholdequation.
3.6 Numerical experiments.
3.7 Affine invariants of color images.
3.8 Generalization to three dimensions.
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.
4.6 Conclusion.
References.
5 Invariants to convolution.
5.1 Introduction.
5.2 Blur invariants for centrosymmetric PSFs.
5.3 Blur invariants for N-fold symmetric PSFs.
5.4 Combined invariants.
5.5 Conclusion.
Appendix.
References.
6 Orthogonal moments.
6.1 Introduction.
6.2 Moments orthogonal on a rectangle.
6.3 Moments orthogonal on a disk.
6.4 Object recognition by ZMs.
6.5 Image reconstruction from moments.
6.6 Three-dimensional OG moments.
6.7 Conclusion.
References.
7 Algorithms for moment computation.
7.1 Introduction.
7.2 Moments in a discrete domain.
7.3 Geometric moments of binary images.
7.4 Geometric moments of graylevel images.
7.5 Efficient methods for calculating OG moments.
7.6 Generalization to n dimensions.
7.7 Conclusion.
References.
8 Applications.
8.1 Introduction.
8.2 Object representation and recognition.
8.3 Image registration.
8.4 Robot navigation.
8.5 Image retrieval.
8.6 Watermarking.
8.7 Medical imaging.
8.8 Forensic applications.
8.9 Miscellaneous applications.
8.10 Conclusion.
References.
9 Conclusion.
Index.