caa a22 4500 467914176 CHVBK 20180406152857.0 cr unu---uuuuu 170328e20060801xx s 000 0 eng 10.1007/s11263-006-6849-5 doi (NATIONALLICENCE)springer-10.1007/s11263-006-6849-5 Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours [Elektronische Daten] [Olivier Juan, Renaud Keriven, Gheorghe Postelnicu] Based on recent work on Stochastic Partial Differential Equations (SPDEs), this paper presents a simple and well-founded method to implement the stochastic evolution of a curve. First, we explain why great care should be taken when considering such an evolution in a Level Set framework. To guarantee the well-posedness of the evolution and to make it independent of the implicit representation of the initial curve, a Stratonovich differential has to be introduced. To implement this differential, a standard Ito plus drift approximation is proposed to turn an implicit scheme into an explicit one. Subsequently, we consider shape optimization techniques, which are a common framework to address various applications in Computer Vision, like segmentation, tracking, stereo vision etc. The objective of our approach is to improve these methods through the introduction of stochastic motion principles. The extension we propose can deal with local minima and with complex cases where the gradient of the objective function with respect to the shape is impossible to derive exactly. Finally, as an application, we focus on image segmentation methods, leading to what we call Stochastic Active Contours. Springer Science + Business Media, LLC, 2006 Level Sets nationallicence Stochastic Partial Differential Equations nationallicence Shape optimization nationallicence Stochastic optimization nationallicence Segmentation nationallicence Active contours nationallicence Juan Olivier The Odyssee Lab (ENPC CERTIS/ENS Paris/INRIA Sophia, Paris aut Keriven Renaud The Odyssee Lab (ENPC CERTIS/ENS Paris/INRIA Sophia, Paris aut Postelnicu Gheorghe The Odyssee Lab (ENPC CERTIS/ENS Paris/INRIA Sophia, Paris aut International Journal of Computer Vision Kluwer Academic Publishers 69/1(2006-08-01), 7-25 0920-5691 69:1<7 2006 69 11263 https://doi.org/10.1007/s11263-006-6849-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-6849-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Juan Olivier The Odyssee Lab (ENPC CERTIS/ENS Paris/INRIA Sophia, Paris aut NATIONALLICENCE 700 1- Keriven Renaud The Odyssee Lab (ENPC CERTIS/ENS Paris/INRIA Sophia, Paris aut NATIONALLICENCE 700 1- Postelnicu Gheorghe The Odyssee Lab (ENPC CERTIS/ENS Paris/INRIA Sophia, Paris aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 69/1(2006-08-01), 7-25 0920-5691 69:1<7 2006 69 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer