03293nam 2200637 450 99646665120331620220913114752.03-540-46069-110.1007/BFb0085267(CKB)1000000000437443(SSID)ssj0000327430(PQKBManifestationID)12089434(PQKBTitleCode)TC0000327430(PQKBWorkID)10299451(PQKB)11174449(DE-He213)978-3-540-46069-5(MiAaPQ)EBC5592165(Au-PeEL)EBL5592165(OCoLC)1066195538(MiAaPQ)EBC6842679(Au-PeEL)EBL6842679(PPN)155183311(EXLCZ)99100000000043744320220913d1989 uy 0engurnn|008mamaatxtccrTsirelson's space /Peter G. Casazza and Thaddeus J. Shura1st ed. 1989.Berlin, Germany ;New York, New York :Springer-Verlag,[1989]©19891 online resource (X, 206 p.) Lecture Notes in Mathematics,0075-8434 ;1363Bibliographic Level Mode of Issuance: Monograph3-540-50678-0 Precursors of the Tsirelson construction -- The Figiel-Johnson construction of Tsirelson's space -- Block basic sequences in Tsirelson's space -- Bounded linear operators on T and the “blocking” principle -- Subsequences of the unit vector basis of Tsirelson's space -- Modified Tsirelson's Space: TM -- Embedding Theorems about T and T -- Isomorphisms between subspaces of Tsirelson's space which are spanned by subsequences of -- Permutations of the unit vector basis of Tsirelson's space -- Unconditional bases for complemented subspaces of Tsirelson's space -- Variations on a Theme -- Some final comments.This monograph provides a structure theory for the increasingly important Banach space discovered by B.S. Tsirelson. The basic construction should be accessible to graduate students of functional analysis with a knowledge of the theory of Schauder bases, while topics of a more advanced nature are presented for the specialist. Bounded linear operators are studied through the use of finite-dimensional decompositions, and complemented subspaces are studied at length. A myriad of variant constructions are presented and explored, while open questions are broached in almost every chapter. Two appendices are attached: one dealing with a computer program which computes norms of finitely-supported vectors, while the other surveys recent work on weak Hilbert spaces (where a Tsirelson-type space provides an example).Lecture Notes in Mathematics,0075-8434 ;1363MathematicsBanach spacesGlobal analysis (Mathematics)Mathematics.Banach spaces.Global analysis (Mathematics)515.732Casazza Peter G.1945-55468Shura Thaddeus J.1947-MiAaPQMiAaPQMiAaPQBOOK996466651203316Tsirelson's space262239UNISA05275nam 2200661 a 450 991101925490332120200520144314.0978661085590297812808559001280855908978047051049004705104989780470510483047051048X(CKB)1000000000356725(EBL)292581(OCoLC)476052571(SSID)ssj0000125019(PQKBManifestationID)11132730(PQKBTitleCode)TC0000125019(PQKBWorkID)10026368(PQKB)11391647(MiAaPQ)EBC292581(Perlego)2754993(EXLCZ)99100000000035672520070822d2007 uy 0engur|n|---|||||txtccrColor constancy /Marc EbnerChichester John Wileyc20071 online resource (409 p.)Wiley-IS&T series in imaging science and technologyDescription based upon print version of record.9780470058299 0470058293 Includes bibliographical references and index.Color Constancy; Contents; Series Preface; Preface; 1 Introduction; 1.1 What is Color Constancy?; 1.2 Classic Experiments; 1.3 Overview; 2 The Visual System; 2.1 Eye and Retina; 2.2 Visual Cortex; 2.3 On the Function of the Color Opponent Cells; 2.4 Lightness; 2.5 Color Perception Correlates with Integrated Reflectances; 2.6 Involvement of the Visual Cortex in Color Constancy; 3 Theory of Color Image Formation; 3.1 Analog Photography; 3.2 Digital Photography; 3.3 Theory of Radiometry; 3.4 Reflectance Models; 3.5 Illuminants; 3.6 Sensor Response; 3.7 Finite Set of Basis Functions4 Color Reproduction4.1 Additive and Subtractive Color Generation; 4.2 Color Gamut; 4.3 Computing Primary Intensities; 4.4 CIE XYZ Color Space; 4.5 Gamma Correction; 4.6 Von Kries Coefficients and Sensor Sharpening; 5 Color Spaces; 5.1 RGB Color Space; 5.2 sRGB; 5.3 CIE L*u*v*Color Space; 5.4 CIE L*a*b*Color Space; 5.5 CMY Color Space; 5.6 HSI Color Space; 5.7 HSV Color Space; 5.8 Analog and Digital Video Color Spaces; 6 Algorithms for Color Constancy under Uniform Illumination; 6.1 White Patch Retinex; 6.2 The Gray World Assumption; 6.3 Variant of Horn's Algorithm6.4 Gamut-constraint Methods6.5 Color in Perspective; 6.6 Color Cluster Rotation; 6.7 Comprehensive Color Normalization; 6.8 Color Constancy Using a Dichromatic Reflection Model; 7 Algorithms for Color Constancy under Nonuniform Illumination; 7.1 The Retinex Theory of Color Vision; 7.2 Computation of Lightness and Color; 7.3 Hardware Implementation of Land's Retinex Theory; 7.4 Color Correction on Multiple Scales; 7.5 Homomorphic Filtering; 7.6 Intrinsic Images; 7.7 Reflectance Images from Image Sequences; 7.8 Additional Algorithms; 8 Learning Color Constancy; 8.1 Learning a Linear Filter8.2 Learning Color Constancy Using Neural Networks8.3 Evolving Color Constancy; 8.4 Analysis of Chromatic Signals; 8.5 Neural Architecture based on Double Opponent Cells; 8.6 Neural Architecture Using Energy Minimization; 9 Shadow Removal and Brightening; 9.1 Shadow Removal Using Intrinsic Images; 9.2 Shadow Brightening; 10 Estimating the Illuminant Locally; 10.1 Local Space Average Color; 10.2 Computing Local Space Average Color on a Grid of Processing Elements; 10.3 Implementation Using a Resistive Grid; 10.4 Experimental Results; 11 Using Local Space Average Color for Color Constancy11.1 Scaling Input Values11.2 Color Shifts; 11.3 Normalized Color Shifts; 11.4 Adjusting Saturation; 11.5 Combining White Patch Retinex and the Gray World Assumption; 12 Computing Anisotropic Local Space Average Color; 12.1 Nonlinear Change of the Illuminant; 12.2 The Line of Constant Illumination; 12.3 Interpolation Methods; 12.4 Evaluation of Interpolation Methods; 12.5 Curved Line of Constant Illumination; 12.6 Experimental Results; 13 Evaluation of Algorithms; 13.1 Histogram-based Object Recognition; 13.2 Object Recognition under Changing Illumination13.3 Evaluation on Object Recognition TasksA human observer is able to recognize the color of objects irrespective of the light used to illuminate them. This is called color constancy. A digital camera uses a sensor to measure the reflected light, meaning that the measured color at each pixel varies according to the color of the illuminant. Therefore, the resulting colors may not be the same as the colors that were perceived by the observer. Obtaining color constant descriptors from image pixels is not only important for digital photography, but also valuable for computer vision, color-based automatic object recognition, and color imagWiley-IS&T series in imaging science and technology.Color visionColor vision.152.145Ebner Marc1753888MiAaPQMiAaPQMiAaPQBOOK9911019254903321Color constancy4421140UNINA