Generalized Multidimensional Orthogonal Polynomials with Applications to Shape Analysis

Authors:
J. Xu;Y.-H. Yang
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1990

Citing 5
Cited 0

An Autoregressive Model Approach to Two-Dimensional Shape Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Partial 2-D Shapes Using Fourier Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Image Analysis by the Methods of Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new technique for shape analysis using orthogonal polynomials

Pattern Recognition Letters
An experimental study of moment methods for automatic identification of three-dimensional objects from television images.

An experimental study of moment methods for automatic identification of three-dimensional objects from television images.

Quantified Score

Hi-index	0.15

Visualization

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.