A Quadtree-Based Unsupervised Segmentation Algorithm for Fruit Visual Inspection

Abstract

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 the preliminary results of an unsupervised segmentation algorithm through a multiresolution method using color information for fruit inspection tasks. The use of a Quadtree 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 in fruit images in order to segment the different zones of the fruit surface. Due to the unsupervised nature of the procedure, it can adapt to the huge variability of color and shape of regions in fruit inspection applications.