Imposing Hard Constraints on Soft Snakes

An approach is presented for imposing generic hard constraints on deformable models at a low computational cost, while preserving the good convergence properties of snake-like models. We believe this capability to be essential not only for the accurate modeling of individual objects that obey known geometric and semantic constraints but also for the consistent modeling of sets of objects. Many of the approaches to this problem that have appeared in the vision literature rely on adding penalty terms to the objective functions. They rapidly become untractable when the number of constraints increases. Applied mathematicians have developed powerful constrained optimization algorithms that, in theory, can address this problem. However, these algorithms typically do not take advantage of the speci c properties of snakes. We have therefore designed a new algorithm that is closely related to Lagrangian methods but is tailored to accommodate the particular brand of deformable models used in th...

• Type: Conference
• Year: 1996
• Where: ECCV
• Authors: Pascal Fua, Christian Brechbühler
• Keywords: Computer Vision, Consistency Constraints, Constrained Optimization Algorithms, Constraints Increases, Deformable Models, ECCV 1996, Generic Hard Constraints