2016, ISBN: 9783319463636

[ED: Hardcover], [PU: Springer / Springer International Publishing / Springer, Berlin], This book presents practical optimization techniques used in image processing and computer vision p… Meer...

[ED: Hardcover], [PU: Springer / Springer International Publishing / Springer, Berlin], This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc. Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision. 1st ed. 2016. 2016. xv, 293 S. XV, 293 p. 127 illus., 23 illus. in color. 235 mm Versandfertig in 6-10 Tagen, DE, [SC: 0.00], Neuware, gewerbliches Angebot, Offene Rechnung (Vorkasse vorbehalten)<

2016, ISBN: 9783319463636

[ED: Hardcover], [PU: Springer / Springer International Publishing / Springer, Berlin], This book presents practical optimization techniques used in image processing and computer vision p… Meer...

[ED: Hardcover], [PU: Springer / Springer International Publishing / Springer, Berlin], This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc.Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision. 1st ed. 2016. 2016. xv, 293 S. XV, 293 p. 127 illus., 23 illus. in color. 235 mm Versandfertig in 6-10 Tagen, DE, [SC: 0.00], Neuware, gewerbliches Angebot, Offene Rechnung (Vorkasse vorbehalten)<

2016, ISBN: 3319463632

1st ed. 2016 Gebundene Ausgabe Algorithmus, Bildbearbeitung, Bildverarbeitung, Grafik (EDV) / Bildverarbeitung, Mathematik, Elektronik, Algorithmen und Datenstrukturen, Theoretische Inf… Meer...

1st ed. 2016 Gebundene Ausgabe Algorithmus, Bildbearbeitung, Bildverarbeitung, Grafik (EDV) / Bildverarbeitung, Mathematik, Elektronik, Algorithmen und Datenstrukturen, Theoretische Informatik, Digitale Signalverarbeitung (DSP), regularizationparameterselection; shaperepresentationinimageprocessing; imageinterpolationalgorithms; regularizationmethodsforlinearinverseproblems; onedimensionaloptimization; unconstrainedoptimizationmethods; optimizationwithlinearconstraints; 3Dimagesmoothing; 3Dvolumetricdescription; algorithmanalysisandproblemcomplexity, mit Schutzumschlag 11, [PU:Springer International Publishing]<

2016, ISBN: 9783319463636

gebonden uitgave

[ED: Gebunden], [PU: Springer International Publishing], Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Features a comprehensive descript… Meer...

[ED: Gebunden], [PU: Springer International Publishing], Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Features a comprehensive description of regularization through optimizationContains a large selection of data fusion algorithmsIncludes chapters devoted to vide, DE, [SC: 0.00], Neuware, gewerbliches Angebot, Hardcover, 293, [GW: 5915g], 1. Auflage 2016, Banküberweisung, PayPal<

2016, ISBN: 9783319463636

gebonden uitgave

Ill-Posed Problems and Regularization, Buch, Hardcover, 1st ed. 2016, [PU: Springer International Publishing], Springer International Publishing, 2016