000			02039naaaa2200265uu 4500
001			https://directory.doabooks.org/handle/20.500.12854/43720
005			20220220062807.0
020			_a/doi.org/10.1007/978-1-4302-5930-5
020			_a9781430259299
020			_a9781430259305
024	7		_ahttps://doi.org/10.1007/978-1-4302-5930-5 _cdoi
041	0		_aEnglish
042			_adc
100	1		_aScott Krig _4auth
245	1	0	_aComputer Vision Metrics: Survey, Taxonomy, and Analysis
260			_bApress _c2014
300			_a1 electronic resource (508 p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aComputer 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.
536			_aIntel
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by-nc-nd/4.0/ _2cc _4https://creativecommons.org/licenses/by-nc-nd/4.0/
546			_aEnglish
856	4	0	_awww.oapen.org _uhttps://link.springer.com/book/10.1007/978-1-4302-5930-5 _70 _zDOAB: download the publication
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/43720 _70 _zDOAB: description of the publication
999			_c70591 _d70591