Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Filtering, Segmentation, and Depth
Filtering, Segmentation, and Depth
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Segmentation with Depth but Without Detecting Junctions
Journal of Mathematical Imaging and Vision
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Level Set Based Shape Prior Segmentation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Towards recognition-based variational segmentation using shape priors and dynamic labeling
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
IEEE Transactions on Image Processing
International Journal of Computer Vision
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In this work, we address the problem of segmenting multiple objects, with possible occlusions, in a variational setting. Most segmentation algorithms based on low-level features often fail under uncertainties such as occlusions and subtle boundaries. We introduce a segmentation algorithm incorporating high-level prior knowledge which is the shape of objects of interest. A novelty in our approach is that prior shape is introduced in a selective manner, only to occluded boundaries. Further, a direct application of our framework is that it solves the segmentation with depth problem that aims to recover the spatial order of overlapping objects for certain classes of images. We also present segmentation results on synthetic and real images.