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.
|Conference||Sixth International Conference on Scale Space and Variational Methods in Computer Vision|
|Period||04/06/2017 → 08/06/2017|
|Series||Scale Space and Variational Methods in Computer Vision. 6th International Conference, Ssvm 2017. Proceedings: Lncs 10302|
|Series||Lecture Notes in Computer Science|