Conference paper ยท Book chapter
A Probabilistic Framework for Curve Evolution
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
In this work, we propose a nonparametric probabilistic framework for image segmentation using deformable models. We estimate an underlying probability distributions of image features from regions defined by a deformable curve. We then evolve the curve such that the distance between the distributions is increasing.
The resulting active contour resembles a well studied piecewise constant Mumford-Shah model, but in a probabilistic setting. An important property of our framework is that it does not require a particular type of distributions in different image regions. Additional advantages of our approach include ability to handle textured images, simple generalization to multiple regions, and efficiency in computation.
We test our probabilistic framework in combination with parametric (snakes) and geometric (level-sets) curves. The experimental results on composed and natural images demonstrate excellent properties of our framework.
Language: | English |
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Publisher: | Springer |
Year: | 2017 |
Pages: | 421-32 |
Proceedings: | 6<sup>th</sup> International Conference on Scale Space and Variational Methods in Computer Vision |
Series: | Lecture Notes in Computer Science |
Journal subtitle: | 6th International Conference, Ssvm 2017, Kolding, Denmark, June 4-8, 2017, Proceedings |
ISBN: | 3319587706 , 3319587714 , 9783319587707 and 9783319587714 |
ISSN: | 03029743 |
Types: | Conference paper and Book chapter |
DOI: | 10.1007/978-3-319-58771-4_34 |
ORCIDs: | Dahl, Vedrana Andersen , Dahl, Anders Bjorholm and 0000-0003-2503-6475 |