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Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization (Advances in Computer Vision and Pattern Recognition) - Tapa blanda
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“The presentation of the problems is accompanied by illustrating examples. The book contains both a great theoretical background and practical applications and is thus self-contained. It is useful for master and doctoral students, as well as for researchers and practitioners dealing with computer vision and image processing, but also working in mathematical optimization.” (Ruxandra Stoean, zbMATH 1362.68003, 2017)
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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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- EditorialSpringer
- Año de publicación2018
- ISBN 10 3319835017
- ISBN 13 9783319835013
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas312
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Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization (Advances in Computer Vision and Pattern Recognition)
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Optimization Techniques in Computer Vision : Ill-Posed Problems and Regularization
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Optimization Techniques in Computer Vision
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Springer International Publishing Jul 2018, 2018
ISBN 10: 3319835017
ISBN 13: 9783319835013
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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 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. 312 pp. Englisch. Nº de ref. del artículo: 9783319835013
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Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization (Advances in Computer Vision and Pattern Recognition)
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Optimization Techniques in Computer Vision : Ill-Posed Problems and Regularization
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Springer International Publishing, Springer International Publishing, 2018
ISBN 10: 3319835017
ISBN 13: 9783319835013
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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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. Nº de ref. del artículo: 9783319835013
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Optimization Techniques in Computer Vision
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ISBN 10: 3319835017
ISBN 13: 9783319835013
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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. Nº de ref. del artículo: 448757754
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Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization (Advances in Computer Vision and Pattern Recognition)
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Paperback. Condición: New. New. book. Nº de ref. del artículo: ERICA80033198350176
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