NDLI: Sampling multidimensional signals by a new class of quasi-random sequences

Content Provider	Springer Nature Link
Author	Skublska Rafajłowicz, Ewa Rafajłowicz, Ewaryst
Copyright Year	2010
Abstract	A new method of equidistributed (EQD) sampling of nD signals is proposed and investigated. Equidistributed or quasi-random (QR) sequences are deterministic sequences, which cover nD space more evenly than uniformly distributed random sequences. We propose to generate quasi-random nD sample sites using space-filling curves, which propagate their measure and neighborhood preserving properties to sample points. It is proved that these points are not only equidistributed but also well distributed, which results in a good volume approximation from samples of 2D and 3D images. A general way of reconstructing output of a linear system from QR samples of its input nD signals is also proposed and proved to be convergent. As an example of applications of the theory, a double radial basis neural network is proposed, which allows for the reconstruction of 2D cross-sections from samples of 3D image or from a video sequence of 2D images.
Starting Page	237
Ending Page	253
Page Count	17
File Format	PDF
ISSN	09236082
Journal	Multidimensional Systems and Signal Processing
Volume Number	23
Issue Number	1-2
e-ISSN	15730824
Language	English
Publisher	Springer US
Publisher Date	2010-06-12
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sampling Image processing Space-filling curves Equdistributed sequences Quasi-random sequences Well-distributed sequences Electrical Engineering Artificial Intelligence (incl. Robotics) Circuits and Systems Signal, Image and Speech Processing
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Signal Processing Information Systems Computer Science Applications Software Hardware and Architecture

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