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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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Recensione
“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)
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreSpringer-Nature New York Inc
- Data di pubblicazione2016
- ISBN 10 3319463632
- ISBN 13 9783319463636
- RilegaturaCopertina rigida
- LinguaInglese
- Numero edizione1
- Numero di pagine312
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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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Springer (edition 1st ed. 2016), 2016
ISBN 10: 3319463632
ISBN 13: 9783319463636
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Optimization Techniques in Computer Vision. Ill-Posed Problems and Regularization.
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xv, 293 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch. Codice articolo 11709DB
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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
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Springer International Publishing Dez 2016, 2016
ISBN 10: 3319463632
ISBN 13: 9783319463636
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Buch. Condizione: 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. Codice articolo 9783319463636
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Optimization Techniques in Computer Vision
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Springer International Publishing, 2016
ISBN 10: 3319463632
ISBN 13: 9783319463636
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Condizione: 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. Codice articolo 128452365
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Optimization Techniques in Computer Vision : Ill-Posed Problems and Regularization
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ISBN 10: 3319463632
ISBN 13: 9783319463636
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Buch. Condizione: 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. Codice articolo 9783319463636
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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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Hardcover. Condizione: Brand New. 312 pages. 9.50x6.50x1.00 inches. In Stock. Codice articolo x-3319463632
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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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Hardcover. Condizione: Like New. Like New. book. Codice articolo ERICA77333194636326
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