DocumentCode
1518340
Title
Generalized multidimensional orthogonal polynomials with applications to shape analysis
Author
Xu, Jian ; Yang, Yee-Hong
Author_Institution
Dept. of Comput. Sci., Saskatchwan Univ., Saskatoon, Sask., Canada
Volume
12
Issue
9
fYear
1990
fDate
9/1/1990 12:00:00 AM
Firstpage
906
Lastpage
913
Abstract
A technique using the generalized multidimensional orthogonal polynomials (GMDOP) for 2-D shape analysis is proposed. In shape analysis, spatial invariances (i.e. translational invariance, scaling invariance, rotational invariance, etc.) are important requirements for a shape analysis algorithm. The described technique provides not only the three invariant properties but also mirror-image rotational invariance and permutational invariance. Experimental results supporting the theory are presented
Keywords
invariance; pattern recognition; picture processing; polynomials; 2D images; multidimensional orthogonal polynomials; pattern recognition; permutational invariance; picture processing; rotational invariance; scaling invariance; shape analysis; spatial invariances; translational invariance; Algorithm design and analysis; Cognition; Computer vision; Data mining; Humans; Multidimensional systems; Pattern analysis; Pattern recognition; Polynomials; Shape;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.57684
Filename
57684
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