International Journal of Computer Vision
Level Set Gait Analysis for Synthesis and Reconstruction
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
3D active shape model segmentation with nonlinear shape priors
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
International Journal of Computer Applications in Technology
Weakly convex coupling continuous cuts and shape priors
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Reduced set density estimator for object segmentation based on shape probabilistic representation
Journal of Visual Communication and Image Representation
Synthesis of realistic subcortical anatomy with known surface deformations
MeshMed'12 Proceedings of the 2012 international conference on Mesh Processing in Medical Image Analysis
Simultaneous monocular 2d segmentation, 3d pose recovery and 3d reconstruction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Online web-data-driven segmentation of selected moving objects in videos
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Joint view-identity manifold for infrared target tracking and recognition
Computer Vision and Image Understanding
Regressing Local to Global Shape Properties for Online Segmentation and Tracking
International Journal of Computer Vision
Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior
Journal of Mathematical Imaging and Vision
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Segmentation involves separating an object from the background in a given image. The use of image information alone often leads to poor segmentation results due to the presence of noise, clutter or occlusion. The introduction of shape priors in the geometric active contour (GAC) framework has proved to be an effective way to ameliorate some of these problems. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, using level-sets. Following the work of Leventon et al., we propose to revisit the use of PCA to introduce prior knowledge about shapes in a more robust manner. We utilize kernel PCA (KPCA) and show that this method outperforms linear PCA by allowing only those shapes that are close enough to the training data. In our segmentation framework, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description permits to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, occlusions, or smearing.