EFFICIENCY OF SIMPLE SHAPE DESCRIPTORS
Markus Peura and Jukka Iivarinen
In C. Arcelli, L. P. Cordella, and G. Sanniti di Baja (Eds.),
Advances in Visual Form Analysis, World Scientific, Singapore, pp. 443-451,
1997.
Abstract
Characterizing objects by their shape is a critical part in
many computer vision applications.
Several theoretically interesting approaches exist which, however,
remain computationally too expensive.
The goal of this paper is to emphasize the value of primitive
shape descriptors.
A set of simple descriptors is used as a feature vector
in order to group similar objects together.
Each descriptor is discussed separately and
discriminatory power of their combination is demonstrated on
irregular sample objects.
The advantages of using simple shape descriptors are obvious:
faster calculation and more general applicability.
Sometimes ``general applicability'' implies poor performance
in a specific application.
A neural network method (Self-Organizing Map) is used in
adapting a system to an application-specific shape space.
The results of the experiments are good. It is shown that
fairly simple shape descriptors can be flexibly used in
complex recognition tasks involving irregular objects.
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