Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Seed-Relative Segmentation Robustness of Watershed and Fuzzy Connectedness Approaches
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Links Between Image Segmentation Based on Optimum-Path Forest and Minimum Cut in Graph
Journal of Mathematical Imaging and Vision
Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watershed Cuts: Thinnings, Shortest Path Forests, and Topological Watersheds
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph-based framework for sub-pixel image segmentation
Theoretical Computer Science
Power Watershed: A Unifying Graph-Based Optimization Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3d live-wire segmentation method for volume images using haptic interaction
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
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Graph-based methods have become well-established tools for image segmentation. Viewing the image as a weighted graph, these methods seek to extract a graph cut that best matches the image content. Many of these methods are interactive, in that they allow a human operator to guide the segmentation process by specifying a set of hard constraints that the cut must satisfy. Typically, these constraints are given in one of two forms: regional constraints (a set of vertices that must be separated by the cut) or boundary constraints (a set of edges that must be included in the cut). Here, we propose a new type of hard constraints, that includes both regional constraints and boundary constraints as special cases. We also present an efficient method for computing cuts that satisfy a set of generalized constraints, while globally minimizing a graph cut measure.