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
fDate :
9/1/1990 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
