Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Constructing stochastic pyramids by MIDES: maximal independent directed edge set
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Hierarchical watersheds within the combinatorial pyramid framework
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
Hierarchical Matching Using Combinatorial Pyramid Framework
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Fully deformable 3D digital partition model with topological control
Pattern Recognition Letters
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Segmentation algorithms based on an energy minimisation framework often depend on a scale parameter which balances a fit to data and a regularising term. Irregular pyramids are defined as a stack of graphs successively reduced. Within this framework, the scale is often defined implicitly as the height in the pyramid. However, each level of an irregular pyramid can not usually be readily associated to the global optimum of an energy or a global criterion on the base level graph. This last drawback is addressed by the scale set framework designed by Guigues. The methods designed by this author allow to build a hierarchy and to design cuts within this hierarchy which globally minimise an energy. This paper studies the influence of the construction scheme of the initial hierarchy on the resulting optimal cuts. We propose one sequential and one parallel method with two variations within both. Our sequential methods provide partitions near an energy lower bound defined in this paper. Parallel methods require less execution times than the sequential method of Guigues even on sequential machines.