000771493 000__ 04862cam\a2200517Ii\4500 000771493 001__ 771493 000771493 005__ 20230306142610.0 000771493 006__ m\\\\\o\\d\\\\\\\\ 000771493 007__ cr\un\nnnunnun 000771493 008__ 141105t20142015sz\a\\\\ob\\\\000\0\eng\d 000771493 019__ $$a908089847 000771493 020__ $$a9783319106533$$q(electronic book) 000771493 020__ $$a3319106538$$q(electronic book) 000771493 020__ $$z9783319106526 000771493 035__ $$aSP(OCoLC)ocn894509934 000771493 035__ $$aSP(OCoLC)894509934$$z(OCoLC)908089847 000771493 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dGW5XE$$dYDXCP$$dN$T$$dIDEBK$$dEBLCP$$dOCLCQ$$dDEBBG$$dIAO$$dIAS$$dIAD$$dJBG 000771493 049__ $$aISEA 000771493 050_4 $$aTA1634 000771493 08204 $$a006.3/70151$$223 000771493 24500 $$aComputer vision in control systems- 1,$$pMathematical theory /$$cMargarita N. Favorskaya, Lakhmi C. Jain, editors. 000771493 24630 $$aMathematical theory 000771493 264_1 $$aCham :$$bSpringer,$$c[2014] 000771493 264_4 $$c©2015 000771493 300__ $$a1 online resource (xx, 371 pages) :$$billustrations. 000771493 336__ $$atext$$btxt$$2rdacontent 000771493 337__ $$acomputer$$bc$$2rdamedia 000771493 338__ $$aonline resource$$bcr$$2rdacarrier 000771493 4901_ $$aIntelligent Systems Reference Library,$$x1868-4394 ;$$vvolume 73 000771493 504__ $$aIncludes bibliographical references. 000771493 5050_ $$aForeword; Preface; Contents; About the Editors; 1 Development of Mathematical Theory in Computer Vision; Abstract; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; References; 2 Morphological Image Analysis for Computer Vision Applications; Abstract; 2.1 Introduction; 2.2 Basics of Mathematical Morphology; 2.2.1 Mathematical Morphology as a Set-Theoretic Scheme; 2.2.2 Binary Mathematical Morphology Based on Structuring Elements; 2.2.3 Grayscale Mathematical Morphology Based on Structuring Elements; 2.2.4 Mathematical Morphology as a Lattice-Theoretic Scheme. 000771493 5058_ $$a2.2.5 Morphologies Based on Connected Filters2.2.6 Morphological Skeleton; 2.3 Skeleton-Based Continuous Binary Morphology; 2.3.1 Skeleton of Binary Image Versus Binary Image of Skeleton; 2.3.2 Continuous Representation of Raster Image Boundary; 2.3.3 Polygonal Figure Skeleton; 2.3.4 Skeleton-Based Continuous Binary Morphologies; 2.4 Morphological Spectrum: Concept and Computation; 2.4.1 Pattern Spectrum and Morphological Spectra; 2.4.2 Thickness Map and Morphological Spectrum with Disk Structuring Elements. 000771493 5058_ $$a2.4.3 Calculation of Binary Morphological Spectra Based on Continuous Skeletal Representation2.4.4 Calculation of Grayscale Morphological Spectra; 2.5 Morphological Image Analysis (Pyt'ev Morphology); 2.5.1 Image Shape as an Invariant of Image Transforms; 2.5.2 Scene Recognition Based on Image Shape; 2.5.3 Scene Change Detection Based on Image Shape; 2.5.4 Scene Recognition Based on the Shape of Noisy Image; 2.5.5 Morphological Shape Matching; 2.6 Projective Morphologies, Morphological Segmentation and Complexity Analysis; 2.6.1 Projective Morphologies Based on Morphological Decompositions. 000771493 5058_ $$a2.6.2 Image Segmentation in the Framework of Projective Morphology2.6.3 Shape Regularization and Morphological Filters by Regularization; 2.6.4 Morphological Complexity, Filters, and Spectra by Complexity; 2.7 Conclusion; Acknowledgments; References; 3 Methods for Detecting of Structural Changes in Computer Vision Systems; Abstract; 3.1 Introduction; 3.2 Pixel Structural Similarity Criteria; 3.3 Spectral Criteria of Structural Image Similarity; 3.3.1 Polynomial Transforms; 3.3.2 Discrete Transforms; 3.4 Spectral Image Variation Detection; 3.4.1 Optimal Detection Algorithm. 000771493 5058_ $$a3.4.2 Quasi-optimal Algorithms3.5 Experimental Research of Structural Similarity Algorithms; 3.5.1 Practical Using of Pixel and Spectral Algorithms in Image Analysis; 3.5.2 Experimental Research of Spectral Statistics D0 and DE; 3.5.3 Experimental Research of MSSIM and MNSSIM1(2) Criteria; 3.6 Conclusion; References; 4 Hierarchical Adaptive KL-Based Transform: Algorithms and Applications; Abstract; 4.1 Introduction; 4.2 Analysis of the Image Transform Methods Based on the KLT; 4.2.1 Karhunen-Loeve Transform for Inter-frame (3D) Processing of a Group of Correlated Images. 000771493 506__ $$aAccess limited to authorized users. 000771493 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 1, 2014). 000771493 650_0 $$aComputer vision$$xMathematics. 000771493 7001_ $$aFavorskai͡a, M. N.$$q(Margarita Nikolaevna),$$eeditor. 000771493 7001_ $$aJain, L. C.,$$eeditor. 000771493 77608 $$iPrint version:$$aFavorskaya, Margarita N.$$tComputer Vision in Control Systems-1 : Mathematical Theory.$$dCham : Springer International Publishing, ©2014$$z9783319106526 000771493 830_0 $$aIntelligent systems reference library ;$$vvolume 73. 000771493 85280 $$bebk$$hSpringerLink 000771493 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-10653-3$$zOnline Access$$91397441.1 000771493 909CO $$ooai:library.usi.edu:771493$$pGLOBAL_SET 000771493 980__ $$aEBOOK 000771493 980__ $$aBIB 000771493 982__ $$aEbook 000771493 983__ $$aOnline 000771493 994__ $$a92$$bISE