Computing geodesics and minimal surfaces via graph cuts

By Yuri Boykov, Vladimir Kolmogorov. Proceedings Ninth IEEE International Conference on Computer Vision, 2003

685 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4f32d8245ba8ec0e6dd3d14830a26a66c0c9d1d9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Reconstructing building mass models from UAV images (2016, positive): “In our 252 implementation, we use graph cut [30, 31] to find the optimal 253 labeling assignment.”
Robust and Efficient Surface Reconstruction From Range Data (2009, methodology): “Methods based on the minimal surfaces framework with graph cuts of [BK03], such as [HK06,LB07], also belong to this category: values of the indicator function are assigned to whole elementary volumes over a regular grid so as to globally minimise an energy with a minimum s - t cut optimisation.”
What energy functions can be minimized via graph cuts? (2002, methodology): “These methods have been successfully used for a wide variety of vision problems, including image restoration [9], [10], [18], [21], stereo and motion [4], [9], [10], [20], [24], [27], [32], [35], [36], image synthesis [29], image segmentation [8], voxel occupancy [39], multicamera scene reconstructi”
Global Optimization for Shape Fitting (2007, methodology): “Section 3.2 combines it with surface regularization (generic shape priors).”
A survey of graph theoretical approaches to image segmentation (2013, neutral): “[71] investigated geometric properties of segments.”
Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model (2013, background): “It was shown [49] that the denser the neighborhood system is when constructing the graph , the smoother are the obtained boundaries.”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “[69] is a combination of the intrinsic local probability map and the learned global property map, and the smoothness term is represented by n-links [281], which encourages continuous local”
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach (2006, neutral): “Boykov and Kolmogorov's algorithm [47] is flexible and shares some elegance with the level set methods.”
Large-Scale Data for Multiple-View Stereopsis (2016, methodology): “Some of the best performing ways are iso-surface extraction methods (Kazhdan et al. 2006; Fuhrmann and Goesele 2014; Curless and Levoy 1996; Shalom et al. 2010) and graph-cut-based methods (Boykov and Kolmogorov 2003; Mücke et al. 2011; Labatut et al. 2009; Hiep et al. 2009).”
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency (2007, methodology): “Then the weight of the edge joining the two co rresponding nodes on the graph will be [2] wij = 4πh(2) 3 ρ (”
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score (2007, methodology): “In the last few years, this method has been successfully applied to several problems in computer vision, including stereovision [16] and image segmentation [2].”