Based on the previous work, this paper presents a model- based approach to the representation and recognition of 2D closed, bounded, free-form curves and simply connected objects. The method involves a new expression of the objects based on complex variables and conformal mapping. In the model-based approaches, a object in a data set is recognized by comparing and matching its model with the known models of the objects that are stored in a database. To develop practical systems for object recognition, several problems must be solved: 1) how to get the model of a data set. We use a technique based on complex variables and conformal mapping to map an object to a circle. The coefficient vector of the polynomial mapping function is stored then in a database and represents the data set model. 2) when the object coordinates system changes, the coefficients vector of the polynomial mapping function also change. However, the coefficients change in a predictable and known way. The object recognition and matching is based on computing and comparing an object invariant entity. This invariant entity is a function of the coefficients and is independent of the coordinate systems. We show the effect of noise on object recognition and position/rotation/scaling estimation. Finally, some experimental results are presented to illustrate the power and robustness of the presented technique.

Date Published: 28 August 2001
PDF: 10 pages
Proc. SPIE 4388, Visual Information Processing X, (28 August 2001); doi: 10.1117/12.438273

Published in SPIE Proceedings Vol. 4388:
Visual Information Processing X
Stephen K. Park; Zia-ur Rahman; Robert A. Schowengerdt, Editor(s)