Name
Abstract | ||
|---|---|---|
Multi-region segmentation algorithms often have the onus of incorporating complex anatomical knowledge representing spatial or geometric relationships between objects, and general-purpose methods of addressing this knowledge in an optimization-based manner have thus been lacking. This paper presents Generalized Hierarchical Max-Flow (GHMF) segmentation, which captures simple anatomical part-whole relationships in the form of an unconstrained hierarchy. Regularization can then be applied to both parts and wholes independently, allowing for spatial grouping and clustering of labels in a globally optimal convex optimization framework. For the purposes of ready integration into a variety of segmentation tasks, the hierarchies can be presented in run-time, allowing for the segmentation problem to be readily specified and alternatives explored without undue programming effort or recompilation. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Pattern recognition,Computer science,Segmentation,Regularization (mathematics),Maximum flow problem,Artificial intelligence,Hierarchy,Cluster analysis,Convex optimization,Machine learning |
DocType | Volume | Citations |
Journal | abs/1404.0336 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John S. H. Baxter | 1 | 74 | 14.67 |
| Martin Rajchl | 2 | 421 | 34.67 |
| Jing Yuan | 3 | 372 | 23.02 |
| Terry M. Peters | 4 | 1335 | 181.71 |