Fractal Image Models And Object Detection

Abstract

This paper discusses a new technique for object detection that uses fractals to model the natural background in a visible image. Our technique is based on the fact that fractal-based models have been found to be good models for natural objects as well as images of natural objects. On the other hand, man-made objects are decidedly not self-similar and therefore fractal-based models are not good models for man-made objects and their images. The technique adaptively fits a fractal-based model and a 2-D autoregressive model over the image and the fractal dimension and model-fit errors are used to identify regions of anomalous dimension and high error. Thus the technique uses a dual approach to object detection by modeling and deemphasizing the natural background instead of explicitly modeling and identifying the man-made object. Results are shown for a real image.