Fractal Algorithm for Image Edge Enhancement and Detection

E. Soundararajan and R.A. Hovis (USA)


relative dispersion, fractal dimension, edges, thresholding.


Edge detection is a problem of fundamental importance in image analysis. Edges are places in the image with strong intensity contrast. In typical images, edges characterize object boundaries and are therefore useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm that performs well in many contexts and captures the requirements of subsequent processing stages. The classical edge detection approaches are based on first derivative, second derivative techniques, and surface fitting. In this paper, we present a fractal algorithm for image edge enhancement and detection. It has enhanced features that were not revealed by gradient operators. The results suggest that this method can provide useful information not available from conventional edge enhancement processes.

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