| Full text | |
| Author(s): |
Braz, Caio de Moraes
;
Miranda, Paulo A. V.
;
Ciesielski, Krzysztof Chris
;
Cappabianco, Fabio A. M.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | Journal of Mathematical Imaging and Vision; v. 62, n. 5, p. 14-pg., 2020-03-27. |
| Abstract | |
The goal of this work is to describe an efficient algorithm for finding a binary segmentation of an image such that the indicated object satisfies a novel high-level prior, called local band, LB, constraint; the returned segmentation is optimal, with respect to an appropriate graph-cut measure, among all segmentations satisfying the given LB constraint. The new algorithm has two stages: expanding the number of edges of a standard edge-weighted graph of an image; applying to this new weighted graph an algorithm known as an oriented image foresting transform, OIFT. In our theoretical investigation, we prove that OIFT algorithm belongs to a class of general fuzzy connectedness algorithms and so has several good theoretical properties, like robustness for seed placement. The extension of the graph constructed in the first stage ensures, as we prove, that the resulted object indeed satisfies the given LB constraint. We also notice that this graph construction is flexible enough to allow combining it with other high-level constraints. Finally, we experimentally demonstrate that the LB constraint gives competitive results as compared to geodesic star convexity, boundary band, and hedgehog shape prior, all implemented within OIFT framework and applied to various scenarios involving natural and medical images. (AU) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 16/21591-5 - Development of robust methods for edge delineation in images using graphs |
| Grantee: | Fábio Augusto Menocci Cappabianco |
| Support Opportunities: | Regular Research Grants |