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In this paper we present the processing chain for geometric and semantic mapping of a drone environment that we developed for search-and-rescue purposes. A precise 3D modelling of the environment is computed using video and (if available) Lidar data captured during the drone flight. Then semantic mapping is performed by interactive learning on the model, thus allowing generic object detection. Finally,...
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...
We describe an interactive framework for man-made structure classification. Our system is able to help an image analyst to define a query that is adapted to various image and geographic contexts. It offers a GIS-like interface for visually selecting the training region samples and a fast and efficient sample description by histogram of oriented gradients and local binary patterns. To learn a discrimination...
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