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2006 International Conference on Image Processing

DOI: 10.1109/icip.2006.312769

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Acoustic Range Image Segmentation by Effective Mean Shift

Proceedings article published in 2006 by Umberto Castellani, Marco Cristani, Vittorio Murino ORCID
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Image perception in underwater environment is a difficult task for a human operator, and data segmentation becomes a crucial step toward an higher level interpretation and recognition of the observing scenarios. This paper contributes to the related state of the art, by fitting the mean shift clustering paradigm to the segmentation of acoustical range images, providing a segmentation approach in which whatever parameter tuning is absent. Moreover, the method exploits actively the connectivity information provided by the range map, by using reverse projection as acceleration technique. Therefore, the method is able to produce, starting from raw range data, meaningful segmented clouds of points in a fully automatic and efficient fashion.