Section: Overall Objectives
The overall objective of the PERCEPTION research team is to develop theories, models, methods, and systems in order to allow computers to see and to understand what they see. A major difference between classical computer systems and computer vision systems is that while the former are guided by sets of mathematical and logical rules, the latter are governed by the laws of nature. It turns out that formalizing interactions between an artificial system and the physical world is a tremendously difficult task.
A first objective is to be able to gather images and videos with one or several cameras, to calibrate them, and to extract 2D and 3D geometric information from these images and videos. This is an extremely difficult task because the cameras receive light stimuli and these stimuli are affected by the complexity of the objects (shape, surface, color, texture, material) composing the real world. The interpretation of light in terms of geometry is also affected by the fact that the three dimensional world projects onto two dimensional images and this projection alters the Euclidean nature of the observed scene.
A second objective is to analyse articulated and moving objects. The real world is composed of rigid, deformable, and articulated objects. Solutions for finding the motion fields associated with deformable and articulated objects (such as humans) remain to be found. It is necessary to introduce prior models that encapsulate physical and mechanical features as well as shape, aspect, and behaviour. The ambition is to describe complex motion as “events” at both the physical level and at the semantic level.
A third objective is to describe and interpret images and videos in terms of objects, object categories, and events. In the past it has been shown that it is possible to recognize a single occurrence of an object from a single image. A more ambitious goal is to recognize object classes such as people, cars, trees, chairs, etc., as well as events or objects evolving in time . In addition to the usual difficulties that affect images of a single object there is also the additional issue of the variability within a class. The notion of statistical shape must be introduced and hence statistical learning should be used. More generally, learning should play a crucial role and the system must be designed such that it is able to learn from a small training set of samples. Another goal is to investigate how an object recognition system can take advantage from the introduction of non-visual input such as semantic and verbal descriptions. The relationship between images and meaning is a great challenge.
A fourth objective is to build vision systems that encapsulate one or several objectives stated above. Vision systems are built within a specific application. The domains at which vision may contribute are numerous:
Multi-media technologies and in particular film and TV productions, database retrieval;
Visual surveillance and monitoring;
Augmented and mixed reality technologies and in particular entertainment, cultural heritage, telepresence and immersive systems, image-based rendering and image-based animation;
Embedded systems for television, portable devices, defense, space, etc.