Multispectral Image Segmentation for Fruit Quality Estimation
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
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In this article, we present an unsupervised segmentation algorithm through a multiresolution approach which uses both color and edge information with a quadtree structure, through as well as an iterative minimization process of an energy function. The algorithm has been applied to fruit images in order to distinguish the different areas of the fruit surface in fruit quality assessment applications. Due to the unsupervised nature of the method, it can adapt itself to the huge variability of colors and shapes of the regions in fruit inspection tasks.