Geometrical Multiresolution Adaptive Transforms - Theory and Applications (Paperback, Softcover reprint of the original 1st ed. 2014)

Modern 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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 545
Release date:	September 2016
First published:	2014
Authors:	Agnieszka Lisowska
Dimensions:	235 x 155 x 6mm (L x W x T)
Format:	Paperback
Pages:	107
Edition:	Softcover reprint of the original 1st ed. 2014
ISBN-13:	978-3-319-37714-8
Categories:	Books > Computing & IT > General Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Promotions
LSN:	3-319-37714-0
Barcode:	9783319377148

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