000 03598nam a22005295i 4500
001 978-3-031-01822-0
003 DE-He213
005 20240730163435.0
007 cr nn 008mamaa
008 220601s2018 sz | s |||| 0|eng d
020 _a9783031018220
_9978-3-031-01822-0
024 7 _a10.1007/978-3-031-01822-0
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aFelsberg, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978537
245 1 0 _aProbabilistic and Biologically Inspired Feature Representations
_h[electronic resource] /
_cby Michael Felsberg.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIII, 89 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Computer Vision,
_x2153-1064
505 0 _aPreface -- Acknowledgments -- Introduction -- Basics of Feature Design -- Channel Coding of Features -- Channel-Coded Feature Maps -- CCFM Decoding and Visualization -- Probabilistic Interpretation of Channel Representations -- Conclusions -- Bibliography -- Author's Biography -- Index.
520 _aUnder the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife-they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_978538
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_978539
650 2 4 _aAutomated Pattern Recognition.
_931568
710 2 _aSpringerLink (Online service)
_978540
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000799
776 0 8 _iPrinted edition:
_z9783031006944
776 0 8 _iPrinted edition:
_z9783031029509
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_978541
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01822-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c84606
_d84606