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[RLF13] Urban structure detection with deformable part-based models
Conférence Internationale avec comité de lecture : IEEE International Geoscience and Remote Sensing Symposium, July 2013, pp.200-203,Mots clés: deformable part-based models, image analysis, object detection, object recognition, remote sensing, very high resolution
Résumé:
In this paper we apply the deformable part model by Felzenszwalb et al., which is at this moment the state of the art in many computer vision related tasks, to detect different types of man made structures in very high resolution aerial images - a reputedly difficult problem in our field. We test the framework on a database of crops of aerial images at a definition of 10 cm/pixel and investigate how the model performs on several classes of objects. The results show that the model can achieve reasonable performance in this context. However, depending on the type of object, there are specific issues which will have to be taken into account to build an effective semi-supervised annotation tool based on this model.
Equipe:
vertigo
Collaboration:
ONERA - DTIM
BibTeX
| @inproceedings { | |||
| RLF13, | |||
| title | = | "{Urban structure detection with deformable part-based models}", | |
| author | = | " H. Randrianarivo and B. Le Saux and M. Ferecatu ", | |
| booktitle | = | "{IEEE International Geoscience and Remote Sensing Symposium}", | |
| year | = | 2013, | |
| month | = | "July", | |
| pages | = | "200-203", | |
| } | |||
