Active vision strategies for object recognition.

Summary This thesis, realized in cooperation with ONERA, concerns the active recognition of 3D objects by an autonomous agent equipped with an observation camera. While in passive recognition the acquisition modalities of the observations are imposed and sometimes generate ambiguities, active recognition exploits the possibility to control online these acquisition modalities during a sequential inference process in order to remove the ambiguity. The objective of the work is to establish planning strategies in the acquisition of information with the concern of a realistic implementation of active recognition. The framework of statistical learning is used for this purpose. The first part of the work is devoted to learning to plan. Two realistic constraints are taken into account: on the one hand, an imperfect modeling of the objects likely to generate additional ambiguities - on the other hand, the learning budget is expensive (in time, in energy), and therefore limited. The second part of the work focuses on how to best exploit the observations during the recognition process. The possibility of an active multi-scale recognition is studied to allow an interpretation as early as possible in the sequential process of information acquisition. Observations are also used to estimate the pose of the object in a robust way in order to ensure consistency between the planned modalities and those actually reached by the visual agent.