A Novel Approach to Estimate Fractal Dimension from Closed Curves

Author(s)

Backes, Andre R. ; Florindo, Joao B. ; Bruno, Odemir M. ; Jiang, X ; Petkov, N

• Document type: Journal article
• Source: Lecture Notes in Computer Science; v. 5702, p. 2-pg., 2009-01-01.

Abstract

An important point in pattern recognition and image analysis is the study of properties of the shapes used to represent all object in an image. Particularly, all interesting measure of a shape is its level of complexity, a value that call be obtained frond its fractal dimension. Many methods were developed for estimating the fractal dimensions of shapes but none of these are, efficient for every situation. This work proposes a. novel approach to estimate the fractal dimension from shape contour by using Curvature Scale Space (CSS). Efficiency of the technique in comparison to the well-known method of Bouligand-Minkowski. Results show that the use of CSS yields fractal dimension values robust to several shape transformations (such as rotation, scale and presence of noise), so providing interesting results for a process of classification of shapes based oil this measure. (AU)