Segmentation of fractal objects: Application to the measure of algae deposit density in the ‘green tide’ phenomenon

In this communication, we present an original unsupervised image segmentation procedure which assumes the 2-D objects to be fractal. This technique is applied to the evaluation of the covering rate of algae deposit in the `green tide' phenomenon which occurs on the coasts of Brittany. After a discussion relative to the fractal nature of the objects under study, we introduce a fractal growth model called DLA which, in conjunction with the image data, allows the obtention of a binarized image. For this, a Bayesian formulation is adopted. Some experimental results are presented, which show the potentiality of this approach.