Modeling and computer vision in image sequences [X-ray angiocardiography application]

In computational vision research, implicit or explicit knowledge is used for a number of tasks such as edge or line detection, stereo matching, motion tracking and three-dimensional representation. This knowledge deals with the image capture, the objects, the scenes under study as well as the set of processing algorithms and can 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-D reconstruction of the cardiac vascular network from an image pair from an angiographic image sequence. This highly underdetermined problem obliges one to incorporate and distribute a priori knowledge in all the steps involved in the reconstruction process and beyond. The authors 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 reconstruction (model based approach); 3-D sequence reconstruction (3-D motion estimation); morphological representation (image rendering); and functional interpretation (kinetic properties)