Graphical Models and Image Processing
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiseeded Segmentation Using Fuzzy Connectedness
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersnakes: Energy-Driven Watershed Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative Measures of Change based on Feature Organization: Eigenvalues and Eigenvectors
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Tree Pruning
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
An efficient method of license plate location
Pattern Recognition Letters
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
"Ratio Regions": A Technique for Image Segmentation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Iterative relative fuzzy connectedness for multiple objects with multiple seeds
Computer Vision and Image Understanding
Seed-Relative Segmentation Robustness of Watershed and Fuzzy Connectedness Approaches
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Automatic Image Segmentation by Tree Pruning
Journal of Mathematical Imaging and Vision
Object delineation by κ-connected components
EURASIP Journal on Advances in Signal Processing
A linear-time approach for image segmentation using graph-cut measures
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive Image Segmentation via Adaptive Weighted Distances
IEEE Transactions on Image Processing
A Bayesian approach to video object segmentation via merging 3-D watershed volumes
IEEE Transactions on Circuits and Systems for Video Technology
Synergistic arc-weight estimation for interactive image segmentation using graphs
Computer Vision and Image Understanding
User-friendly interactive image segmentation through unified combinatorial user inputs
IEEE Transactions on Image Processing
A graph-based framework for sub-pixel image segmentation
Theoretical Computer Science
Computer Vision and Image Understanding
Image segmentation by iterated region merging with localized graph cuts
Pattern Recognition
Generalized hard constraints for graph segmentation
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
User-steered image segmentation using live markers
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
IFTrace: Video segmentation of deformable objects using the Image Foresting Transform
Computer Vision and Image Understanding
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Image segmentation can be elegantly solved by optimum-path forest and minimum cut in graph. Given that both approaches exploit similar image graphs, some comparative analysis is expected between them. We clarify their differences and provide their comparative analysis from the theoretical point of view, for the case of binary segmentation (object/background) in which hard constraints (seeds) are provided interactively. Particularly, we formally prove that some optimum-path forest methods from two distinct region-based segmentation paradigms, with internal and external seeds and with only internal seeds, indeed minimize some graph-cut measures. This leads to a proof of the necessary conditions under which the optimum-path forest algorithm and the min-cut/max-flow algorithm produce exactly the same segmentation result, allowing a comparative analysis between them.