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Moments and Moment Invariants in Pattern Recognition / Edition 1

Moments and Moment Invariants in Pattern Recognition / Edition 1


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Product Details

ISBN-13: 9780470699874
Publisher: Wiley
Publication date: 12/30/2009
Pages: 312
Product dimensions: 6.80(w) x 9.90(h) x 0.90(d)

About the Author

Professor Jan Flusser, PhD, Dsc, is a director of theInstitute of Information Theory and Automation of the ASCR, Prague,Czech Republic, and a full professor of Computer Science at theCzech Technical University, Prague, and at the Charles University ,Prague. Jan Flusser’s research areas are moments and momentinvariants, image regristration, image fusion, multichannel blinddeconvolution  and super-resolution imaging. He has authoredand coauthored more than 150 research publications in these areas,including tutorials (ICIP’05, ICIP’07,EUSIPCO’07, CVPR’08, FUSION’08, SPPRA’09,SCIA’09) and invited/keynote talks (ICCS’06,COMPSTAT’06, WIO’06, DICTA’07, CGIM’08) atmajor international conferences. He gives undergraduate andgraduate courses on digital image processing, pattern recognition,and moment invariants and wavelets. Personal webpage

Tomáš Suk, PhD, is a research fellow of thesame Institute. His research interests include invariant features,moment and point-based invariants, color spaces and geometrictransformations. He has authored and coauthored more than 50research publications in these areas, some of which have elicited aconsiderable citation response. Tomás Suk coauthored tutorialson moment invariants held at international conference ICIP’07and SPPR’09. Personal webpage

Barbara Zitová, PhD, is Head of the Department ofImage Processing at the same Institute. Her research interest ismainly in image regi8stration, invariants, wavelets, and imageprocessing applications in cultural heritage. She has authored andcoauthored more that 30 research publications in these areas,including tutorials at international conferences (ICIP’05,ICIP’07, EUSIPCO’07, FUSION’08 andCVPR’08). Her paper “Image Registration Methods: ASurvey,” Image and Vision Computing, vol. 21, pp.977-1000, 2003, has become a major reference work in imageregistration . She teaches a specialized graduate course on momentinvariants and wavelets at the Czech Technical University. Personalwebpage

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Table of Contents

Authors’ biographies.



1 Introduction to moments.

1.1 Motivation.

1.2 What are invariants?

1.3 What are moments?

1.4 Outline of the book.


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.


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.



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.


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.



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.


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.


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.


9 Conclusion.


What People are Saying About This

From the Publisher

"This text is a little gem in the vast amount of literature on pattern recognition...In conclusion, this is an excellent text on pattern recognition that I highly recommend to practitioners and students in signal and image processing." (Computing Reviews, October 2010)

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