Abstract
The problem of parallel, bottom-up construction of hierarchical structures adapted to the content of an input image is addressed. This principle is essential in image analysis and understanding. For each level of the structure, which is related to a resolution level, the region adjacency graph of a segmentation of the input image is defined. All segmentation and resolution reduction operations are local, and therefore a complete input-dependent hierarchy can be built in O[log(image-size)]. The region adjacency graph contraction is achieved by extracting a maximal independent set (MIS) of the graph. A new parallel probabilistic method for MIS computation is proposed and discussed. The representation uncertainty introduced by the probabilistic component of the hierarchy construction is reduced through consensus among an ensemble of outputs obtained from the same input image.
Peter Meer would like to acknowledge the support of the National Science Foundation under Grant IRI-9210861, and the support of the Rutgers Research Council.
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© 1994 Springer-Verlag Berlin Heidelberg
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Montanvert, A., Meer, P., Bertolino, P. (1994). Hierarchical Shape Analysis in Grey-level Images. In: O, YL., Toet, A., Foster, D., Heijmans, H.J.A.M., Meer, P. (eds) Shape in Picture. NATO ASI Series, vol 126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03039-4_37
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DOI: https://doi.org/10.1007/978-3-662-03039-4_37
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