Description: Optimization Techniques in Computer Vision Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Ill-Posed Problems and Regularization Author(s): Mongi A. Abidi, Andrei V. Gribok, Joonki Paik Format: Hardback Publisher: Springer International Publishing AG, Switzerland Imprint: Springer International Publishing AG ISBN-13: 9783319463636, 978-3319463636 Synopsis 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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Book Title: Optimization Techniques in Computer Vision
Number of Pages: 293 Pages
Language: English
Publication Name: Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization
Publisher: Springer International Publishing A&G
Publication Year: 2016
Subject: Engineering & Technology, Computer Science, Mathematics
Item Height: 235 mm
Item Weight: 5915 g
Type: Textbook
Author: Andrei V. Gribok, Joonki Paik, Mongi A. Abidi
Series: Advances in Computer Vision and Pattern Recognition
Item Width: 155 mm
Format: Hardcover
