Modeling and Computer Vision in Image Sequences

In computational vision research, implicit or explicit knowledge is used for a number oftasks such as edge or linie detection, stereo matching, motion tracking and three-dimensioeal representation. This knowledge deals with the image capture, the objects, the scenes under study as well as the set of processing algorithms and CM be classified along several lines (intrinsic vs extrinsic features, time-space dimensions, low vs high level, generic vs specific, local vs global, etc.). These multiple points of view must be recognized as "experiencial" or "in depth" modeling aimed at ordering the procedures, coupling data and knowledge, controlling potential failures and deviations, or in other words, at solving a given problem. These issues are illustrated through the 3-01 reconstruction of the c.rdiac vascular network from a pair of mgiographic image sequence. This highly underdetermined problem obliges to incorporate and distribute a priori knowledge in alp the steps involved in the reconstruction process and beyond. They cover : the image acquisition (choice of incidences) ; the extraction of vessels (contour lines, centrelines) ; complex configuration analysis (crossings, superpositions) ; 2-D vessel motions (disambiguation from the tracking) ; 3-D static reconstruclion (model based approach) ; 3-D sequence reconstruction (3-D motion estimation) ; morphological representation (image rendering) ; functional interpretation (kinetic properties).