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Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization de Mongi A. Abidi

Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization: Advances in Computer Vision and Pattern Recognition

Autor Mongi A. Abidi, Andrei V. Gribok, Joonki Paik
en Limba Engleză Paperback – 4 iul 2018
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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Specificații

ISBN-13: 9783319835013
ISBN-10: 3319835017
Ilustrații: XV, 293 p. 127 illus., 23 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.44 kg
Ediția:Softcover reprint of the original 1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seria Advances in Computer Vision and Pattern Recognition

Locul publicării:Cham, Switzerland

Cuprins

Ill-Posed Problems in Imaging and Computer Vision.- Selection of the Regularization Parameter.- Introduction to Optimization.- Unconstrained Optimization.- Constrained Optimization.- Frequency-Domain Implementation of Regularization.- Iterative Methods.- Regularized Image Interpolation Based on Data Fusion.- Enhancement of Compressed Video.- Volumetric Description of Three-Dimensional Objects for Object Recognition.- Regularized 3D Image Smoothing.- Multi-Modal Scene Reconstruction Using Genetic Algorithm-Based Optimization.- Appendix A: Matrix-Vector Representation for Signal Transformation.- Appendix B: Discrete Fourier Transform.- Appendix C: 3D Data Acquisition and Geometric Surface Reconstruction.- Appendix D: Mathematical Appendix.- Index.

Recenzii

“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)

Textul de pe ultima copertă

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.

Caracteristici

Features a comprehensive description of regularization through optimization Contains a large selection of data fusion algorithms Includes chapters devoted to video compression and enhancement Includes supplementary material: sn.pub/extras