An Unsupervised Segmentation Algorithm Based on a Quadtree Structure*

ABTRACT Many segmentation techniques are available in the literature and some of them have been widely used in different application problems. Most of these segmentation techniques were motivated by specific application purposes. In this article we present an unsupervised segmentation algorithm through a multiresolution approach which uses both color and edge information. The use of a Quadtree (QT) structure simplifies the combination of a multiresolution approach with the chosen strategy for the segmentation process and speeds up the whole procedure. The algorithm has been tested for segmenting classical images in order to compare our results with the ones provided by other color segmentation methods. It has also been applied to fruit images in order to segment the different zones of the fruit surface. Due to the unsupervised nature of the method, it can adapt to the huge variability of color and shape of the regions in fruit inspection applications.