caa a22 4500 467914656 CHVBK 20180406152858.0 cr unu---uuuuu 170328e20061201xx s 000 0 eng 10.1007/s11263-006-7462-3 doi (NATIONALLICENCE)springer-10.1007/s11263-006-7462-3 Multiscale Active Contours [Elektronische Daten] [Xavier Bresson, Pierre Vandergheynst, Jean-Philippe Thiran] We propose a new multiscale image segmentation model, based on the active contour/snake model and the Polyakov action. The concept of scale, general issue in physics and signal processing, is introduced in the active contour model, which is a well-known image segmentation model that consists of evolving a contour in images toward the boundaries of objects. The Polyakov action, introduced in image processing by Sochen-Kimmel-Malladi in Sochen et al. (1998), provides an efficient mathematical framework to define a multiscale segmentation model because it generalizes the concept of harmonic maps embedded in higher-dimensional Riemannian manifolds such as multiscale images. Our multiscale segmentation model, unlike classical multiscale segmentations which work scale by scale to speed up the segmentation process, uses all scales simultaneously, i.e. the whole scale space, to introduce the geometry of multiscale images in the segmentation process. The extracted multiscale structures will be useful to efficiently improve the robustness and the performance of standard shape analysis techniques such as shape recognition and shape registration. Another advantage of our method is to use not only the Gaussian scale space but also many other multiscale spaces such as the Perona-Malik scale space, the curvature scale space or the Beltrami scale space. Finally, this multiscale segmentation technique is coupled with a multiscale edge detecting function based on the gradient vector flow model, which is able to extract convex and concave object boundaries independent of the initial condition. We apply our multiscale segmentation model on a synthetic image and a medical image. Springer Science + Business Media, LLC, 2006 active contour nationallicence scale space nationallicence multiscale segmentation nationallicence PDE nationallicence Polyakov action nationallicence Riemannian manifolds nationallicence gradient vector flow nationallicence Bresson Xavier Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, CH-1015, Lausanne, Switzerland aut Vandergheynst Pierre Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, CH-1015, Lausanne, Switzerland aut Thiran Jean-Philippe Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, CH-1015, Lausanne, Switzerland aut International Journal of Computer Vision Kluwer Academic Publishers 70/3(2006-12-01), 197-211 0920-5691 70:3<197 2006 70 11263 https://doi.org/10.1007/s11263-006-7462-3 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-7462-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bresson Xavier Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, CH-1015, Lausanne, Switzerland aut NATIONALLICENCE 700 1- Vandergheynst Pierre Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, CH-1015, Lausanne, Switzerland aut NATIONALLICENCE 700 1- Thiran Jean-Philippe Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, CH-1015, Lausanne, Switzerland aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 70/3(2006-12-01), 197-211 0920-5691 70:3<197 2006 70 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer