Active shape models—their training and application
Computer Vision and Image Understanding
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Nonparametric shape priors for active contour-based image segmentation
Signal Processing
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
Multi-Reference Shape Priors for Active Contours
International Journal of Computer Vision
Segmentation under Occlusions Using Selective Shape Prior
SIAM Journal on Imaging Sciences
Segmentation using the edge strength function as a shape prior within a local deformation model
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Efficient kernel density estimation of shape and intensity priors for level set segmentation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Nonlinear shape manifolds as shape priors in level set segmentation and tracking
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
Combining shape prior and statistical features for active contour segmentation
IEEE Transactions on Circuits and Systems for Video Technology
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This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data.