Computer Vision Metrics [electronic resource] : Survey, Taxonomy, and Analysis / by Scott Krig.
By: Krig, Scott [author.]
Contributor(s): SpringerLink (Online service)
Material type:
TextPublisher: Berkeley, CA : Apress : Imprint: Apress, 2014Edition: 1st ed. 2014Description: XXXI, 508 p. 216 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781430259305Subject(s): Computer graphics | Optical data processing | Natural language processing (Computer science) | Computer Graphics | Image Processing and Computer Vision | Natural Language Processing (NLP)Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.6 LOC classification: T385Online resources: Click here to access online
In:
Springer Nature Open Access eBookSummary: 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.
Open Access
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.
There are no comments on this title.