000 | 03413nam a22005415i 4500 | ||
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001 | 978-981-32-9945-0 | ||
003 | DE-He213 | ||
005 | 20240423125022.0 | ||
007 | cr nn 008mamaa | ||
008 | 191011s2019 si | s |||| 0|eng d | ||
020 |
_a9789813299450 _9978-981-32-9945-0 |
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024 | 7 |
_a10.1007/978-981-32-9945-0 _2doi |
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050 | 4 | _aTA1501-1820 | |
050 | 4 | _aTA1634 | |
072 | 7 |
_aUYT _2bicssc |
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072 | 7 |
_aCOM016000 _2bisacsh |
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072 | 7 |
_aUYT _2thema |
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082 | 0 | 4 |
_a006 _223 |
100 | 1 |
_aRahman, S. M. Mahbubur. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aOrthogonal Image Moments for Human-Centric Visual Pattern Recognition _h[electronic resource] / _cby S. M. Mahbubur Rahman, Tamanna Howlader, Dimitrios Hatzinakos. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2019. |
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300 |
_aXII, 149 p. 58 illus., 42 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aCognitive Intelligence and Robotics, _x2520-1964 |
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505 | 0 | _a1 Introduction -- 2 Image Moments -- 3 Face Recognition -- 4 Expression Recognition -- 5 Fingerprint Classification -- 6 Iris Recognition -- 7 Hand Gesture Recognition -- 8 Conclusion. | |
520 | _aInstead of focusing on the mathematical properties of moments, this book is a compendium of research that demonstrates the effectiveness of orthogonal moment-based features in face recognition, expression recognition, fingerprint recognition and iris recognition. The usefulness of moments and their invariants in pattern recognition is well known. What is less well known is how orthogonal moments may be applied to specific problems in human-centric visual pattern recognition. Unlike previous books, this work highlights the fundamental issues involved in moment-based pattern recognition, from the selection of discriminative features in a high-dimensional setting, to addressing the question of how to classify a large number of patterns based on small training samples. In addition to offering new concepts that illustrate the use of statistical methods in addressing some of these issues, the book presents recent results and provides guidance on implementing the methods. Accordingly, it will be of interest to researchers and graduate students working in the broad areas of computer vision and visual pattern recognition. | ||
650 | 0 |
_aImage processing _xDigital techniques. |
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650 | 0 | _aComputer vision. | |
650 | 1 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
700 | 1 |
_aHowlader, Tamanna. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aHatzinakos, Dimitrios. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789813299443 |
776 | 0 | 8 |
_iPrinted edition: _z9789813299467 |
776 | 0 | 8 |
_iPrinted edition: _z9789813299474 |
830 | 0 |
_aCognitive Intelligence and Robotics, _x2520-1964 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-32-9945-0 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c173305 _d173305 |