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.
This work has been partly supported by grants CPI2001-2956-C02-02 from Spanish CICYT and IST-2001-37306 from the European Union.
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Usó, A.M. (2003). A Quadtree-Based Unsupervised Segmentation Algorithm for Fruit Visual Inspection. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_60
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DOI: https://doi.org/10.1007/978-3-540-44871-6_60
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