Title :
HOSVD Based Method for Surface Data Approximation and Compression
Author :
Szeidl, László ; Rudas, Imre ; Rövid, András ; Várlaki, Péter
Author_Institution :
John von Neumann Fac. of Inf., Budapest Tech, Budapest
Abstract :
The main aim of this paper is to introduce a method for approximating surfaces given by a set of discrete points with possible additional parameters. The method uses the higher order singular value decomposition based on canonical form of two-variable TP (tensor product) functions. Except of approximation abilities of this principle, the paper focuses on the compression properties of the method, as well. The method is able to achieve high compression rate in the input data by keeping the error at remarkable lower level.
Keywords :
approximation theory; data compression; mathematics computing; singular value decomposition; tensors; discrete points; higher order singular value decomposition; surface data approximation; surface data compression; tensor product functions; Data compression; Image reconstruction; Robots; Shape; Singular value decomposition; Surface reconstruction; Temperature distribution; Temperature measurement; Tensile stress; Throughput;
Conference_Titel :
Intelligent Engineering Systems, 2008. INES 2008. International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-2082-7
Electronic_ISBN :
978-1-4244-2083-4
DOI :
10.1109/INES.2008.4481294
