9783319835013 - optimization techniques in computer vision: ill-posed problems and regularization (advances in computer vision and pattern recognition) de abidi, mongi a.; gribok, andrei v.; paik, joonki (13 resultados)
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Taschenbuch. Condición: Neu. Optimization Techniques in Computer Vision | Ill-Posed Problems and Regularization | Mongi A. Abidi (u. a.) | Taschenbuch | Advances in Computer Vision and Pattern Recognition | xv | Englisch | 2018 | Springer | EAN 9783319835013 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenst…r. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Editorial: Springer International Publishing, Springer International Publishing, 2018
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Serie: Libro 58 de 86 - Advances in Computer Vision and Pattern Recognition
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Editorial: Springer International Publishing Jul 2018, 2018
Serie: Libro 58 de 86 - Advances in Computer Vision and Pattern Recognition
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 processi…ng 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. 312 pp. Englisch.
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Serie: Libro 58 de 86 - Advances in Computer Vision and Pattern Recognition
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Condición: New. Print on Demand pp. 308.
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Editorial: Springer International Publishing, 2018
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Condición: New. 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 video compression and enhancementThis book pr.
Idioma: Inglés
Editorial: Springer, 2018
Serie: Libro 58 de 86 - Advances in Computer Vision and Pattern Recognition
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Condición: New. PRINT ON DEMAND pp. 308.
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Editorial: Springer, Palgrave Macmillan Jul 2018, 2018
Serie: Libro 58 de 86 - Advances in Computer Vision and Pattern Recognition
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 a…nd 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 312 pp. Englisch.
