000 | 03598nam a22005295i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/978-3-031-01822-0 _2doi |
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050 | 4 | _aTA1501-1820 | |
050 | 4 | _aTA1634 | |
072 | 7 |
_aUYT _2bicssc |
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_aCOM016000 _2bisacsh |
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072 | 7 |
_aUYT _2thema |
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082 | 0 | 4 |
_a006 _223 |
100 | 1 |
_aFelsberg, Michael. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978537 |
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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. |
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300 |
_aXIII, 89 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aSynthesis Lectures on Computer Vision, _x2153-1064 |
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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 |
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650 | 0 |
_aComputer vision. _978538 |
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650 | 0 |
_aPattern recognition systems. _93953 |
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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 |
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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 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01822-0 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
999 |
_c84606 _d84606 |