LEADER 05571nam 2200697Ia 450 001 9910816990103321 005 20200520144314.0 010 $a1-282-38033-8 010 $a9786612380334 010 $a0-470-68475-5 010 $a0-470-68476-3 035 $a(CKB)1000000000822207 035 $a(EBL)470179 035 $a(OCoLC)476315050 035 $a(SSID)ssj0000340532 035 $a(PQKBManifestationID)11233232 035 $a(PQKBTitleCode)TC0000340532 035 $a(PQKBWorkID)10388323 035 $a(PQKB)10432746 035 $a(MiAaPQ)EBC470179 035 $a(Au-PeEL)EBL470179 035 $a(CaPaEBR)ebr10351114 035 $a(CaONFJC)MIL238033 035 $a(EXLCZ)991000000000822207 100 $a20090818d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMoments and moment invariants in pattern recognition /$fJan Flusser, Tomas Suk, Barbara Zitov 205 $a1st ed. 210 $aChichester, West Sussex, U.K. ;$aHoboken, N.J. $cJ. Wiley$d2009 215 $a1 online resource (314 p.) 300 $aDescription based upon print version of record. 311 $a0-470-69987-6 320 $aIncludes bibliographical references and index. 327 $aContents; 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 327 $a2.2.3 Basis of invariants of the second and third orders2.2.4 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 327 $a3.1.3 Affine transformation3.1.4 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 Cayley-Aronhold equation 327 $a3.5.1 Manual solution3.5.2 Automatic solution; 3.6 Numerical experiments; 3.6.1 Digit recognition; 3.6.2 Recognition of symmetric patterns; 3.6.3 The children's 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 Cayley-Aronhold 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 327 $a4.4 Implicit invariants as a minimization task4.5 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 327 $a5.3.2 Blur invariants for Gaussian PSFs 330 $aMoments 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 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, 606 $aOptical pattern recognition$xMathematics 606 $aMoment problems (Mathematics) 606 $aInvariants 615 0$aOptical pattern recognition$xMathematics. 615 0$aMoment problems (Mathematics) 615 0$aInvariants. 676 $a515/.42 700 $aFlusser$b Jan$0926090 701 $aSuk$b Tomas$01720799 701 $aZitova$b Barbara$01720800 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910816990103321 996 $aMoments and moment invariants in pattern recognition$94119800 997 $aUNINA