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|Title:||On Morphological Hierarchical Representations for Image Processing and Spatial Data Clustering|
|Authors:||NAJMAN Laurent; SOILLE Pierre|
|Citation:||Proceedings of the Workshop on Applications of Discrete Geometry and Mathematical Morphology held in conjunction with the 20th International Conference on Pattern Recognition (ICPR 2010) p. 52-61|
|Publisher:||International Association for Pattern Recognition|
|Type:||Articles in periodicals and books|
|Abstract:||Hierarchical data representations in the context of classification and data clustering were put forward during the fifties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satisfied. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained.|
|JRC Institute:||Space, Security and Migration|
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