000 | 03379nam a22005415i 4500 | ||
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001 | 978-3-319-05011-9 | ||
003 | DE-He213 | ||
005 | 20200421112040.0 | ||
007 | cr nn 008mamaa | ||
008 | 140324s2014 gw | s |||| 0|eng d | ||
020 |
_a9783319050119 _9978-3-319-05011-9 |
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024 | 7 |
_a10.1007/978-3-319-05011-9 _2doi |
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050 | 4 | _aTA1637-1638 | |
050 | 4 | _aTA1634 | |
072 | 7 |
_aUYT _2bicssc |
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072 | 7 |
_aUYQV _2bicssc |
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072 | 7 |
_aCOM012000 _2bisacsh |
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072 | 7 |
_aCOM016000 _2bisacsh |
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082 | 0 | 4 |
_a006.6 _223 |
082 | 0 | 4 |
_a006.37 _223 |
100 | 1 |
_aLisowska, Agnieszka. _eauthor. |
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245 | 1 | 0 |
_aGeometrical Multiresolution Adaptive Transforms _h[electronic resource] : _bTheory and Applications / _cby Agnieszka Lisowska. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014. |
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300 |
_aXII, 107 p. 65 illus., 21 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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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v545 |
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505 | 0 | _aIntroduction -- Smoothlets -- Multismoothlets -- Moments-Based Multismoothlet Transform -- Image Compression -- Image Denoising -- Edge Detection -- Summary. | |
520 | _aModern image processing techniques are based on multiresolution geometrical methods of image representation. These methods are efficient in sparse approximation of digital images. There is a wide family of functions called simply 'X-lets', and these methods can be divided into two groups: the adaptive and the nonadaptive. This book is devoted to the adaptive methods of image approximation, especially to multismoothlets. Besides multismoothlets, several other new ideas are also covered. Current literature considers the black and white images with smooth horizon function as the model for sparse approximation but here, the class of blurred multihorizon is introduced, which is then used in the approximation of images with multiedges. Additionally, the semi-anisotropic model of multiedge representation, the introduction of the shift invariant multismoothlet transform and sliding multismoothlets are also covered. Geometrical Multiresolution Adaptive Transforms should be accessible to both mathematicians and computer scientists. It is suitable as a professional reference for students, researchers and engineers, containing many open problems and will be an excellent starting point for those who are beginning new research in the area or who want to use geometrical multiresolution adaptive methods in image processing, analysis or compression. | ||
650 | 0 | _aComputer science. | |
650 | 0 |
_aComputer science _xMathematics. |
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650 | 0 | _aImage processing. | |
650 | 0 | _aComputer mathematics. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aImage Processing and Computer Vision. |
650 | 2 | 4 | _aMath Applications in Computer Science. |
650 | 2 | 4 | _aMathematical Applications in Computer Science. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319050102 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v545 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-05011-9 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c56561 _d56561 |