Cargando…
Computer Vision Metrics Survey, Taxonomy, and Analysis /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress : Imprint: Apress,
2014.
|
| Edición: | 1st ed. 2014. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-1-4302-5930-5 | ||
| 003 | DE-He213 | ||
| 005 | 20220124155840.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 140614s2014 xxu| s |||| 0|eng d | ||
| 020 | |a 9781430259305 |9 978-1-4302-5930-5 | ||
| 024 | 7 | |a 10.1007/978-1-4302-5930-5 |2 doi | |
| 050 | 4 | |a T385 | |
| 072 | 7 | |a UML |2 bicssc | |
| 072 | 7 | |a COM012000 |2 bisacsh | |
| 072 | 7 | |a UML |2 thema | |
| 082 | 0 | 4 | |a 006.6 |2 23 |
| 100 | 1 | |a Krig, Scott. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Computer Vision Metrics |h [electronic resource] : |b Survey, Taxonomy, and Analysis / |c by Scott Krig. |
| 250 | |a 1st ed. 2014. | ||
| 264 | 1 | |a Berkeley, CA : |b Apress : |b Imprint: Apress, |c 2014. | |
| 300 | |a XXXI, 508 p. 216 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 506 | 0 | |a Open Access | |
| 520 | |a 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. | ||
| 650 | 0 | |a Computer graphics. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 1 | 4 | |a Computer Graphics. |
| 650 | 2 | 4 | |a Computer Vision. |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781430259299 |
| 776 | 0 | 8 | |i Printed edition: |z 9781430259312 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-4302-5930-5 |z Texto Completo |
| 912 | |a ZDB-2-CWD | ||
| 912 | |a ZDB-2-SXPC | ||
| 912 | |a ZDB-2-SOB | ||
| 950 | |a Professional and Applied Computing (SpringerNature-12059) | ||
| 950 | |a Professional and Applied Computing (R0) (SpringerNature-43716) | ||