Computer Vision Metrics Survey, Taxonomy, and Analysis /

• Главный автор: Krig, Scott (Автор)
• Соавтор: SpringerLink (Online service)
• Формат: Электронный ресурс eКнига
• Язык: английский
• Опубликовано: Berkeley, CA : Apress : Imprint: Apress, 2014.
• Редактирование: 1st ed. 2014.
• Предметы: Computer graphics.; Computer vision.; Natural language processing (Computer science).; Computer Graphics.; Computer Vision.; Natural Language Processing (NLP).
• Online-ссылка: Link to Metadata

Итог

Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing 'how-to' source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.

Объем

XXXI, 508 p. 216 illus. online resource.

ISBN

9781430259305

DOI

10.1007/978-1-4302-5930-5

Доступ

Open Access