Graph Cuts and Efficient N-D Image Segmentation

By Yuri Boykov, G. Funka-Lea. International Journal of Computer Vision, 2006

2,230 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/af0a2f6300b1888058b45fc96fe5bc4f4ca4302a

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In Computer Vision and Pattern Recognition

Citations in the corpus

EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow (2021, methodology): “Optimization based methods: Most of them are traditional methods which are divided into four categories: 1) contour based methods [39, 40, 11, 3, 25], 2) graphcut based methods [9, 8, 31], 3) random walk based methods [15, 14, 5] and 4) region merging methods [2, 44, 43].”
Efficient hierarchical graph-based video segmentation (2010, background): “Interactive object segmentation has recently shown significant progress [1, 2, 9, 13, 16].”
Segmentation of Clustered Nuclei With Shape Markers and Marking Function (2009, methodology): “based methods optimize segmentation energies by combining a wide range of visual cues and constraints, and are related to active contour methods [12].”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “Ontheotherhand,graph cut algorithms facilitate a global optimal solution [86].”
Geodesic graph cut for interactive image segmentation (2010, background): “(See [4] for an excellent discussion of the variety of approaches for which graph-cut optimization has been used.)”
Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images (2010, methodology): “The globally optimal labeling is computed using the widely used graph-cuts algorithm [26]–[30], [40], [41].”
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation (2015, methodology): “This acts as the boundary term together with the regional term given by R 2 − ConvNet in order to perform a min-cut/max-ﬂow segmentation Boykov and Funka-Lea (2006).”
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets (2009, methodology): “Background seeds are set automatically, and a graph-cut algorithm [65] is employed to separate liver from background.”
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy (2019, methodology): “These include classical thresholding approaches with pixel-grouping via connected component trees, morphological methods based on the watershed transform [4, 16, 7], and optimization via graph-cuts [5].”
Constrained parametric min-cuts for automatic object segmentation (2010, methodology): “They are usually computed using max-flow algorithms that find exact solutions to certain energy minimization problems [6].”
Superpixels and Supervoxels in an Energy Optimization Framework (2010, methodology): “We apply the binary segmentation algorithm of [24] to separate an image into the salient object and background components.”
Analyzing spatially-varying blur (2010, methodology): “In each iteration, we estimate the optimal parameters θf and θb using expectation-maximization (EM), and then assign I[n] using graph cuts [3].”
Generative Image Segmentation Using Random Walks with Restart (2008, methodology): “One of the popular approaches is the Graph Cuts method (GC) [4] based on energy functionals which are minimized via discrete optimization techniques.”
Min-cut based segmentation of point clouds (2009, methodology): “The most relevant work to ours is [1], in which the authors take advantage of the min-cut algorithm to create a segmentation that satisfies constraints placed by the user or automatic cues.”
A survey of MRI-based medical image analysis for brain tumor studies (2013, methodology): “Over the last few years, algorithms based on some form of graph cuts for the segmentation (Boykov and Funka-Lea 2006) have become increasingly popular.”