Mongi A. Abidi; Andrei V. Gribok; Joonki Paik Optimization Techniques in Computer Vision 9783319463636

Варианты приобретения

Цена: 18167.00р.
Кол-во:
Наличие: Поставка под заказ. Есть в наличии на складе поставщика.
Склад Америка: Есть
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября
При условии наличия книги у поставщика.

Добавить в корзину

в Мои желания

Автор: Mongi A. Abidi; Andrei V. Gribok; Joonki Paik
Название: Optimization Techniques in Computer Vision
ISBN: 9783319463636
Издательство: Springer
Классификация:

Прикладная математика

Математическое моделирование

Электроника

Системы и технологии визуализации

Алгоритмы и структуры данных

Машинное зрение

Обработка изображений

ISBN-10: 3319463632
Обложка/Формат: Hardback
Страницы: 293
Вес: 0.63 кг.
Дата издания: 2016
Серия: Advances in computer vision and pattern recognition
Язык: English
Издание: 1st ed. 2016
Иллюстрации: 2 tables, color; 23 illustrations, color; 104 illustrations, black and white; xv, 293 p. 127 illus., 23 illus. in color.
Размер: 234 x 156 x 19
Читательская аудитория: Professional & vocational
Подзаголовок: Ill-posed problems and regularization
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: 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.

Дополнительное описание: 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.-

Optimization Techniques in Computer Vision, Mongi A. Abidi; Andrei V. Gribok; Joonki Paik