Morphological Segmentation

By Pierre Machart. 2009

Subfields: Computer Vision and Pattern Recognition, Computational Linguistics & Semantics

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Citations in the corpus

Nucleus segmentation across imaging experiments: The 2018 Data Science Bowl (2019, positive): “Following the strategy laid out here, models could also be constructed to segment cell structures in addition to the nucleus, such as cell borders and organelles.”
The watershed concept and its use in segmentation : a brief history (2012, background): “Instead of performing successive geodesic thickenings of all regional minima, one performs a thickening of a set of markers, some of them inside the objects to segment and at least one of them in the background [31],[4],[8].”
Power Watershed: A Unifying Graph-Based Optimization Framework (2011, background): “markers [59], [12], and is given as a function wR such that, for every edge e, we set wRðeÞ to be equal to the level of the highest component of w containing e and at least one”
A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing (2011, methodology): “To avoid the over-segmentation problem due to image noise, Meyer and Beucher (1990) introduced marker-controlled watershed segmentation.”
THE WATERSHED TRANSFORMATION APPLIED TO IMAGE SEGMENTATION (2009, unknown): “Beucher and Lantuejoul were the first to apply the concept of watershed and divide lines to segmentation problems [3].”
Segmentation of Clustered Nuclei With Shape Markers and Marking Function (2009, methodology): “A marker-controlled watershed algorithm replaces region minima or UEP with predefined markers, each representing an object [18].”
The Morphological Approach to Segmentation: The Watershed Transformation (2018, background): “Such a review may be found in [5] or in an introductory paper by the authors [6].”
SEMANTIC SEGMENTATION OF AERIAL IMAGES IN URBAN AREAS WITH CLASS-SPECIFIC HIGHER-ORDER CLIQUES (2015, methodology): “To separate erroneously merged buildings we apply the Watershed algorithm (Meyer and Beucher, 1990) per connected component.”
Segmentation and classification of hyperspectral images using watershed transformation (2010, methodology): “, area filtering) or to use markers [46].”