Active contour evolved by joint probability classification on Riemannian manifold
2016 (English)In: Signal, Image and Video Processing, ISSN 1863-1703, E-ISSN 1863-1711, 1-8 p.Article in journal (Refereed) Epub ahead of printText
In this paper, we present an active contour model for image segmentation based on a nonparametric distribution metric without any intensity a priori of the image. A novel nonparametric distance metric, which is called joint probability classification, is established to drive the active contour avoiding the instability induced by multimodal intensity distribution. Considering an image as a Riemannian manifold with spatial and intensity information, the contour evolution is performed on the image manifold by embedding geometric image feature into the active contour model. The experimental results on medical and texture images demonstrate the advantages of the proposed method.
Place, publisher, year, edition, pages
Springer London, 2016. 1-8 p.
Active contour, Image segmentation, Joint probability classification, Nonparametric distribution, Riemannian manifold
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-187086DOI: 10.1007/s11760-016-0891-8ScopusID: 2-s2.0-84964010146OAI: oai:DiVA.org:kth-187086DiVA: diva2:928962
QP 2016052016-05-172016-05-172016-05-17Bibliographically approved