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001 978-3-319-05011-9
003 DE-He213
005 20170628034120.0
007 cr nn 008mamaa
008 140324s2014 gw | s |||| 0|eng d
020 _a9783319050119
_9978-3-319-05011-9
024 7 _a10.1007/978-3-319-05011-9
_2doi
050 4 _aTA1637-1638
050 4 _aTA1637-1638
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
100 1 _aLisowska, Agnieszka.
_eauthor.
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.
300 _aXII, 107 p. 65 illus., 21 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v545
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 vision.
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
999 _c18368
_d18368