Using contextual information to set control parameters of a vision process

This paper presents a novel approach to supervised learning of image-understanding tactics based on the use of contextual information. We use a database to store past experiences. From this database and the context elements computed from the task specifications and the input data, we determine whether or not an algorithm is applicable, and which parameters are suitable for it. The database is continuously updated with information of success or failure of the system.