Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations

By Luc M. Vincent, Pierre Soille. 1991

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation (2020, methodology): “BBFNet [7] obtains instance segmentation results by Watershed transform [79,4] and Hough-voting [5,47].”
InstanceCut: From Edges to Instances with MultiCut (2016, methodology): “For the superpixel extraction we utilize the well-known watershed technique [49], which is run directly on the edge scores.”
Multi-Scale Representation Learning on Proteins (2022, methodology): “Popular graph-based segmentation tools such as Felzenszwalb and Huttenlocher [2004, FH], mean shift [Comaniciu and Meer, 2002], and watershed [Vincent and Soille, 1991], however, produce non-compact superpixels of irregular sizes and shapes.”
SLIC Superpixels ? (2010, methodology): “Vincent and Soille [17] propose a fast version based on queuing of pixels.”
k-means Mask Transformer (2022, methodology): “…clustering-based methods usually operate on semantic segments [13,15,18] and group ‘thing’ pixels into instance segments with various representations, such as instance center regression [50,89,77,104,22,94,63], Watershed transform [91,5], Hough-voting [6,59,92], or pixel aﬃnity [51,69,85,36,47].”
Watershed-Based Segmentation and Region Merging (2000, methodology): “Several very efficient algorithms have been devised for the determination of watersheds [5, 10, 21]; some of them may be used on parallel architectures, implemented in hardware, or applied on general purpose computers in a few seconds for typical image sizes.”
Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, methodology): “…we will examine hysteresis segmentation [Vogel and Kretzschmar, 1996], indicator kriging [Oh and Lindquist, 1999], converging active contours [Sheppard et al., 2004], watershed segmentation [Vincent and Soille, 1991], and Bayesian Markov random field segmentation [Kulkarni et al., 2012].”
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration (2007, methodology): “A large number of methods approach segmentation using bottom-up merge strategies, beginning with the classic agglomerative clustering algorithm [4] to watershed [19, 12] and region growing (including methods that use probabilistic approaches [14, 13]) to more recent algebraic multigrid inspired aggr”
Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis (2010, methodology): “To further validate the segmentation algorithm, we compared the proposed segmentation method with the watershed algorithm [23] and the watershed-based hybrid merging algorithm [29].”
The image foresting transform: theory, algorithms, and applications (2004, methodology): “For many classical operators, the IFT-based implementation [6] is much closer to the theoretical definition than the published algorithms [20], [21].”
Superpixels and Polygons Using Simple Non-iterative Clustering (2017, background): “The watershed algorithm (WSHED) [33] accumulates similar pixels starting from local minima to find watersheds, i.”