The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
This paper addresses the segmentation from an image of entities that have the form of a `network', i.e. the region in the image corresponding to the entity is composed of bran...
Ian H. Jermyn, Josiane Zerubia, Ting Peng, V&eacut...
We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects....
Occlusion and lack of visibility in dense crowded scenes make it very difficult to track individual people correctly and consistently. This problem is particularly hard to tackle i...
This paper presents an automatic algorithm which reconstructs building models from airborne LiDAR (light detection and ranging) data of urban areas. While our algorithm inherits t...