Geometrical multiresolution adaptive transforms [electronic resource] : theory and applications / Agnieszka Lisowska.

Geometrical multiresolution adaptive transforms [electronic resource] : theory and applications / Agnieszka Lisowska.

9783319050119 electronic book
3319050117 electronic book
9783319050102

Cham : Springer, 2014.

English

1 online resource (xii, 107 pages) : illustrations (some color).

10.1007/978-3-319-05011-9 doi

TA1637.5

006.6

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.

Includes bibliographical references.

Access limited to authorized users.

Description based on online resource; title from PDF title page (SpringerLink, viewed March 31, 2014).

Studies in computational intelligence ; v.545. 1860-949X

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