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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Layered Mean Shift Methods
verfasst von : Milan Šurkala, Karel Mozdřeň, Radovan Fusek, Eduard Sojka
Erschienen in: Scale Space and Variational Methods in Computer Vision
Verlag: Springer Berlin Heidelberg
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Segmentation is one of the most discussed problems in image processing. Many various methods for image segmentation exist. The mean-shift method is one of them and it was widely developed in recent years and it is still being developed. In this paper, we propose a new method called Layered Mean Shift that uses multiple mean-shift segmentations with different bandwidths stacked for elimination of the over-segmentation problem and finding the most appropriate segment boundaries. This method effectively reduces the need for the use of large kernels in the mean-shift method. Therefore, it also significantly reduces the computational complexity.