Muutke küpsiste eelistusi

Moments and Moment Invariants in Pattern Recognition [Kõva köide]

5.00/5 (4 hinnangut Goodreads-ist)
(Inst of Information Theory & Automation), (Inst of Information Theory & Automation), (Inst of Information Theory & Automation)
  • Formaat: Hardback, 312 pages, kõrgus x laius x paksus: 252x173x22 mm, kaal: 658 g
  • Ilmumisaeg: 28-Oct-2009
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470699876
  • ISBN-13: 9780470699874
Teised raamatud teemal:
  • Kõva köide
  • Hind: 142,23 €*
  • * saadame teile pakkumise kasutatud raamatule, mille hind võib erineda kodulehel olevast hinnast
  • See raamat on trükist otsas, kuid me saadame teile pakkumise kasutatud raamatule.
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Lisa soovinimekirja
  • Raamatukogudele
Moments and Moment Invariants in Pattern RecognitionSuurem pilt
  • Formaat: Hardback, 312 pages, kõrgus x laius x paksus: 252x173x22 mm, kaal: 658 g
  • Ilmumisaeg: 28-Oct-2009
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470699876
  • ISBN-13: 9780470699874
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470699874.html
Märksõnad:
Moments as projections of an images 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 survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging.  

Key features:

 





Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments. Considers invariants to traditional transforms translation, rotation, scaling, and affine transform - from a new point of view, which offers new possibilities of designing optimal sets of invariants. Reviews and extends a recent field of invariants with respect to convolution/blurring. Introduces implicit moment invariants as a tool for recognizing elastically deformed objects. Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments. Offers comprehensive advice on the construction of various invariants illustrated with practical examples. Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course.

Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical imaging, remote sensing, robotics and computer vision. Post graduate students in image processing and pattern recognition will also find the book of interest.

Arvustused

"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)

Authors' biographies xi
Preface xiii
Acknowledgments xv
Introduction to moments
1(12)
Motivation
1(2)
What are invariants?
3(3)
Categories of invariant
4(2)
What are moments?
6(2)
Geometric and complex moments
6(1)
Orthogonal moments
7(1)
Outline of the book
8(5)
References
9(4)
Moment invariants to translation, rotation and scaling
13(36)
Introduction
13(4)
Invariants to translation
13(1)
Invariants to uniform scaling
14(1)
Traditional invariants to rotation
15(2)
Rotation invariants from complex moments
17(9)
Construction of rotation invariants
17(2)
Construction of the basis
19(3)
Basis of invariants of the second and third orders
22(1)
Relationship to the Hu invariants
22(4)
Pseudoinvariants
26(1)
Combined invariants to TRS and contrast changes
27(2)
Rotation invariants for recognition of symmetric objects
29(9)
Logo recognition
32(1)
Recognition of simple shapes
33(1)
Experiment with a baby toy
34(4)
Rotation invariants via image normalization
38(4)
Invariants to nonuniform scaling
42(1)
TRS invariants in 3D
43(2)
Conclusion
45(4)
References
45(4)
Affine moment invariants
49(64)
Introduction
49(5)
Projective imaging of a 3D world
49(1)
Projective moment invariants
50(2)
Affine transformation
52(1)
AMIs
53(1)
AMIs derived from the Fundamental theorem
54(1)
AMIs generated by graphs
55(12)
The basic concept
55(2)
Representing the invariants by graphs
57(1)
Independence of the AMIs
58(6)
The AMIs and tensors
64(2)
Robustness of the AMIs
66(1)
AMIs via image normalization
67(12)
Decomposition of the affine transform
70(4)
Violation of stability
74(1)
Relation between the normalized moments and the AMIs
74(2)
Affine invariants via half normalization
76(1)
Affine invariants from complex moments
76(3)
Derivation of the AMIs from the Cayley-Aronhold equation
79(5)
Manual solution
79(2)
Automatic solution
81(3)
Numerical experiments
84(8)
Digit recognition
84(3)
Recognition of symmetric patterns
87(1)
The children's mosaic
87(5)
Affine invariants of color images
92(3)
Generalization to three dimensions
95(9)
Method of geometric primitives
96(2)
Normalized moments in 3D
98(4)
Half normalization in 3D
102(2)
Direct solution of the Cayley-Aronhold equation
104(1)
Conclusion
104(9)
Appendix
105(4)
References
109(4)
Implicit invariants to elastic transformations
113(16)
Introduction
113(3)
General moments under a polynomial transform
116(1)
Explicit and implicit invariants
117(2)
Implicit invariants as a minimization task
119(1)
Numerical experiments
120(5)
Invariance and robustness test
121(1)
ALOI classification experiment
122(1)
Character recognition on a bottle
122(3)
Conclusion
125(4)
References
126(3)
Invariants to convolution
129(36)
Introduction
129(4)
Blur invariants for centrosymmetric PSFs
133(12)
Template matching experiment
138(1)
Invariants to linear motion blur
139(4)
Extension to n dimensions
143(1)
Possible applications and limitations
144(1)
Blur invariants for N-fold symmetric PSFs
145(3)
Blur invariants for circularly symmetric PSFs
146(1)
Blur invariants for Gaussian PSFs
147(1)
Combined invariants
148(3)
Combined invariants to convolution and rotation
149(1)
Combined invariants to convolution and affine transform
150(1)
Conclusion
151(14)
Appendix
151(11)
References
162(3)
Orthogonal moments
165(48)
Introduction
165(1)
Moments orthogonal on a rectangle
166(20)
Hypergeometric functions
167(1)
Legendre moments
168(3)
Chebyshev moments
171(2)
Other moments orthogonal on a rectangle
173(5)
OG moments of a discrete variable
178(8)
Moments orthogonal on a disk
186(10)
Zernike and Pseudo-Zernike moments
186(6)
Orthogonal Fourier-Mellin moments
192(2)
Other moments orthogonal on a disk
194(2)
Object recognition by ZMs
196(1)
Image reconstruction from moments
197(9)
Reconstruction by the direct calculation
199(1)
Reconstruction in the Fourier domain
200(1)
Reconstruction from OG moments
201(3)
Reconstruction from noisy data
204(1)
Numerical experiments with image reconstruction from OG moments
204(2)
Three-dimensional OG moments
206(3)
Conclusion
209(4)
References
209(4)
Algorithms for moment computation
213(22)
Introduction
213(1)
Moments in a discrete domain
213(2)
Geometric moments of binary images
215(7)
Decomposition methods for binary images
216(3)
Boundary-based methods for binary images
219(2)
Other methods for binary images
221(1)
Geometric moments of graylevel images
222(3)
Intensity slicing
222(1)
Approximation methods
223(2)
Efficient methods for calculating OG moments
225(5)
Methods using recurrent relations
225(3)
Decomposition methods
228(2)
Boundary-based methods
230(1)
Generalization to n dimensions
230(1)
Conclusion
231(4)
References
232(3)
Applications
235(54)
Introduction
235(1)
Object representation and recognition
235(5)
Image registration
240(10)
Registration of satellite images
241(5)
Image registration for image fusion
246(4)
Robot navigation
250(7)
Indoor robot navigation based on circular landmarks
251(2)
Recognition of landmarks using fish-eye lens camera
253(4)
Image retrieval
257(2)
Watermarking
259(4)
Watermarking based on the geometric moments
260(3)
Medical imaging
263(4)
Landmark recognition in the scoliosis study
264(3)
Forensic applications
267(4)
Detection of near-duplicated image regions
267(4)
Miscellaneous applications
271(5)
Noise-resistant optical flow estimation
272(1)
Focus measure
272(3)
Edge detection
275(1)
Gas-liquid flow categorization
276(1)
3D objects visualization
276(1)
Conclusion
276(13)
References
277(12)
Conclusion
289(2)
Index 291
Professor Jan Flusser, PhD, Dsc, is a director of the Institute of Information Theory and Automation of the ASCR, Prague, Czech Republic, and a full professor of Computer Science at the Czech Technical University, Prague, and at the Charles University , Prague. Jan Flussers research areas are moments and moment invariants, image regristration, image fusion, multichannel blind deconvolution  and super-resolution imaging. He has authored and coauthored more than 150 research publications in these areas, including tutorials (ICIP05, ICIP07, EUSIPCO07, CVPR08, FUSION08, SPPRA09, SCIA09) and invited/keynote talks (ICCS06, COMPSTAT06, WIO06, DICTA07, CGIM08) at major international conferences. He gives undergraduate and graduate courses on digital image processing, pattern recognition, and moment invariants and wavelets. Personal webpage http://www.utia.cas.cz/people/flusser. Tomá Suk, PhD, is a research fellow of the same Institute. His research interests include invariant features, moment and point-based invariants, color spaces and geometric transformations. He has authored and coauthored more than 50 research publications in these areas, some of which have elicited a considerable citation response. Tomás Suk coauthored tutorials on moment invariants held at international conference ICIP07 and SPPR09. Personal webpage http://zoi.utia.cas.cz/suk.

Barbara Zitová, PhD, is Head of the Department of Image Processing at the same Institute. Her research interest is mainly in image regi8stration, invariants, wavelets, and image processing applications in cultural heritage. She has authored and coauthored more that 30 research publications in these areas, including tutorials at international conferences (ICIP05, ICIP07, EUSIPCO07, FUSION08 and CVPR08). Her paper Image Registration Methods: A Survey, Image and Vision Computing, vol. 21, pp. 977-1000, 2003, has become a major reference work in image registration . She teaches a specialized graduate course on moment invariants and wavelets at the Czech Technical University. Personal webpage http://zoi.utia.cas.cz/zitova.