Optimization Techniques Computer Vision by Abidi Mongi (27 results)

Language: English
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Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
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Taschenbuch. Condition: 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.

Language: English
Published by Springer International Publishing, Springer International Publishing 2018
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
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Taschenbuch. Condition: 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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Published by Springer 2018
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Language: English
Published by Springer International Publishing, Springer International Publishing 2016
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
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Buch. Condition: 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.

Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization
Abidi, Mongi A. (Author)/ Gribok, Andrei V. (Author)/ Paik, Joonki (Author)
Language: English
Published by Springer 2016
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
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Language: English
Published by Springer International Publishing Jul 2018 2018
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
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Taschenbuch. Condition: 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.

Language: English
Published by Springer 2018
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
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Published by Springer International Publishing 2018
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
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Condition: 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.

Language: English
Published by Springer 2018
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Published by Springer, Palgrave Macmillan Jul 2018 2018
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
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Taschenbuch. Condition: 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.

Language: English
Published by Springer International Publishing Dez 2016 2016
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
- Hardcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Buch. Condition: 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.

Language: English
Published by Springer International Publishing 2016
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
- Hardcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
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Gebunden. Condition: 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.

Language: English
Published by Springer, Palgrave Macmillan Dez 2016 2016
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
- Hardcover
- Print on Demand
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Buch. Condition: 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 and comp…uter 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.

Language: English
Published by Springer 2016
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
- Hardcover
- Print on Demand
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Language: English
Published by Springer 2016
Series: Advances in Computer Vision and Pattern Recognition, Book 58 of 86. Book 58 of 86 - Advances in Computer Vision and Pattern Recognition
- Hardcover
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