000 03250nam a22005055i 4500
001 978-3-319-33762-3
003 DE-He213
005 20200421111203.0
007 cr nn 008mamaa
008 160916s2016 gw | s |||| 0|eng d
020 _a9783319337623
_9978-3-319-33762-3
024 7 _a10.1007/978-3-319-33762-3
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aKrig, Scott.
_eauthor.
245 1 0 _aComputer Vision Metrics
_h[electronic resource] :
_bTextbook Edition /
_cby Scott Krig.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXVIII, 637 p. 331 illus., 139 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aImage Capture and Representation -- Image Re-processing -- Global and Regional Features -- Local Feature Design Concepts -- Taxonomy of Feature Description Attributes -- Interest Point Detector and Feature Descriptor Survey -- Ground Truth Data, Content, Metrics, and Analysis -- Vision Pipeline and Optimizations -- Feature Learning Architecture Taxonomy and Neuroscience Background -- Feature Learning and Deep Learning Architecture Survey.   .
520 _aBased on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods.  To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aComputational Intelligence.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319337616
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-33762-3
912 _aZDB-2-SCS
942 _cEBK
999 _c53992
_d53992