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[BF15] Local integrity constraints for structure detection and segmentation in high resolution earth observation images
Conférence Internationale avec comité de lecture : IEEE International Conference on Image Processing (ICIP 2015), September 2015, pp.-, Canada,Mots clés: Object retrieval and segmentation, interactive learning, pairwise learning
Résumé:
Considering the idea that objects in images have a higher local
structural integrity than the background they lie into, we propose a
method that learns a supervised distance characterizing the mem-
bership of a pair of elements to the target structure. We test our
ideas by applying them to the task of extracting semantic structures
in high resolution Earth Observation images. The results show that
the method works well in many situations when there is no training
dataset. The limits of the method are also discussed.
Equipe:
vertigo
BibTeX
| @inproceedings { | |||
| BF15, | |||
| title | = | "{Local integrity constraints for structure detection and segmentation in high resolution earth observation images}", | |
| author | = | " P. Blanchart and M. Ferecatu ", | |
| booktitle | = | "{IEEE International Conference on Image Processing (ICIP 2015)}", | |
| year | = | 2015, | |
| month | = | "September", | |
| pages | = | "-", | |
| address | = | " Canada", | |
| } | |||
