The Watershed Transform: Definitions, Algorithms and Parallelization Strategies

Fundamenta Informaticae, 2000

1,135 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f389e9f8d328443664f4792eb942e190a4d06f4d

Citations in the corpus

3D numerical reconstruction of well-connected porous structure of rock using fractal algorithms (2014, positive): “In order to efficiently implement the algorithm, we first employed the watershed algorithm [50,51] to separate the pore space into isolated”
Medical image processing on the GPU - Past, present and future (2013, neutral): “, 1988), region growing algorithms (Adams and Bischof, 1994), the watershed transform (Digabel and Lantuejoul, 1977; Roerdink and Meijster, 2000), classification-based algorithms,”
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards (2016, positive): “For this, we implement two different detection techniques, the watershed segmentation (WS) algorithm (Roerdink & Meijster, 2000) and the circular Hough transform (CHT) algorithm (Atherton & Kerbyson, 1999).”
Digital Topology and Geometry in Medical Imaging: A Survey (2015, neutral): “A review of several definitions of the watershed transform and the associated algorithms was presented by Roerdink and Meijster [181].”
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries (2021, positive): “For highlight detection, we directly use these scores as prediction; for moment retrieval, we use TAG [49] to progressively groups top-scored clips with the classical watershed algorithm [34].”
The image foresting transform: theory, algorithms, and applications (2004, neutral): “There is yet no consensual definition for the WT [41].”
The watershed concept and its use in segmentation : a brief history (2012, neutral): “Roerdink published a remarkable review on the various methods for constructing the watershed [39].”
Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm (2006, neutral): “The algorithm originated from mathematical morphology that deals with the topographic representation of an image [3], [4].”
Comparison of parameter‐adapted segmentation methods for fluorescence micrographs (2011, positive): “The third algorithm in the present study uses the watershed transform (38), which is more widely established in the literature than the MIL described above for segmentation of cells.”
Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential (2014, neutral): “Details about watershed can be found in [42].”
A guided tour into subcellular colocalization analysis in light microscopy (2006, neutral): “© 2006 The Royal Microscopical Society No claim to original US government works Received 13 April 2006; accepted 28 June 2006 Blackwell Publishing LtdT U TO R I A L R E V I E W”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, neutral): “The boundaries of the dams are called watershed lines, which split the landscape into regions [202].”
Improved watershed transform for medical image segmentation using prior information (2004, positive): “In this paper, we present a novel modification of the watershed transform that significantly improves the watershed transform performance by allowing the introduction of functions based on prior information.”
Power Watershed: A Unifying Graph-Based Optimization Framework (2011, positive): “The algorithms that are reviewed in this section view the image as a graph with each pixel corresponding to a node and edges weighted to reﬂect changes in image intensity, color or other features.”