An entropy-based approach for shape description
In this paper an automatic method for the selection
of those Fourier descriptors which better correlate a 2D shape
contour is presented. To this aim, shape description has been
modeled as a non linear approximation problem and a strict
relationship between transform entropy and the sorted version
of the transformed analysed boundary is derived. As a result,
Fourier descriptors are selected in a hierarchical way and the
minimum number of coefficients able to give a nearly optimal
shape boundary representation is automatically derived. The
latter maximizes an entropic interpretation of a complexity-based
similarity measure, i.e. the normalized information distance.
Preliminary experimental results show that the proposed method
is able to provide a compact and computationally effective
description of shape boundary which guarantees a nearly optimal
matching with the original one.