Extraction of rotation invariant signature based on fractal geometry

Abstract

A new method of feature extraction with a rotation invariant property is presented. One of the main contributions of this study is that a rotation invariant signature of 2D contours is selected based on fractal theory. The rotation invariant signature is a measure of the fractal dimensions, which is rotation invariant based on a series of central projection transform (CPT) groups. As the CPT is applied to a 2D object, a unique contour is obtained. In the unfolding process, this contour is further spread into a central projection unfolded curve, which can be viewed as a periodic function due to the different orientations of the pattern. We consider the unfolded curves to be non-empty and bounded sets in IR/sup n/, and the central projection unfolded curves with respect to the box computing dimension are rotation invariant. Some experiments with positive results have been conducted. This approach is applicable to a wide range of areas such as image analysis, pattern recognition etc.