THE WATERSHED TRANSFORMATION APPLIED TO IMAGE SEGMENTATION

By Serge Beucher. 2009

577 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Watershed Transform for Instance Segmentation (2016, background): “[3] proposed an algorithm that iteratively fills the watershed landscape from each local minimum, adding dams wherever two neighbouring bodies of water meet.”
Predicting Tree Attributes and Quality Characteristics of Scots Pine Using Airborne Laser Scanning Data (2009, background): “Finally, all the basins are bounded by dams, which thus constitute the boundaries of the segments (Beucher 1992, Gonzales and Woods 2002).”
Medical image registration: a review (2014, methodology): “Examples of region-based techniques are: thresholding methods (Wellner 1993; Otsu 1979), watershed (Beucher 1991; Grau et al. 2004) and region growing (Adams and Bischof 1994).”
Brain-Controlled Wheelchairs: A Robotic Architecture (2013, methodology): “For this, we use the watershed algorithm, since it is fast enough to work in real–time [15].”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, methodology): “The second step, i.e. separate leaf segmentation, is achieved using a watershed method [9] applied on the Euclidean distance map of the resulting plant mask image of the ﬁrst step of the method.”
Multiscale X-ray tomography of cementitious materials: A review (2020, background): “is first constructed – typically: gradient of the original image or distance transform of the binarized image [186].”
High-Content Screening for Quantitative Cell Biology. (2016, methodology): “This is most commonly done using a binarization or thresholding algorithm, such as Otsu's Method, combined with the watershed algorithm [28,29].”
Leaf segmentation in plant phenotyping: a collation study (2015, methodology): “separate leaf segmentation, is achieved using a watershed method [9] applied on the Euclidean distance map of the resulting plant mask image of the first step of the method.”
Biometric verification using thermal images of palm-dorsa vein patterns (2004, methodology): “The basic tool for watershed transformation is called the region maximum in [26], which can select pixels with a high gray value within a region as the candidates for watershed points.”