Active shape models—their training and application
Computer Vision and Image Understanding
A variational level set approach to multiphase motion
Journal of Computational Physics
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
DEFORMOTION: Deforming Motion, Shape Average and the Joint Registration and Segmentation of Images
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Facial feature localization based on an improved active shape model
Information Sciences: an International Journal
Joint Parametric and Non-parametric Curve Evolution for Medical Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Converting level set gradients to shape gradients
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Facial expression feature selection based on rough set
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Journal of Mathematical Imaging and Vision
Computer Vision and Image Understanding
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When segmenting images of low quality or with missing data, statistical prior information about the shapes of the objects to be segmented can significantly aid the segmentation process. However, defining probability densities in the space of shapes is an open and challenging problem. In this paper, we propose a nonparametric shape prior model for image segmentation problems. In particular, given example training shapes, we estimate the underlying shape distribution by extending a Parzen density estimator to the space of shapes. Such density estimates are expressed in terms of distances between shapes, and we consider the L2 distance between signed distance functions for shape density estimation, in addition to a distance measure based on the template metric. In particular, we consider the case in which the space of shapes is interpreted as a manifold embedded in a Hilbert space. We then incorporate the learned shape prior distribution into a maximum a posteriori (MAP) estimation framework for segmentation. This results in an optimization problem, which we solve using active contours. We demonstrate the effectiveness of the resulting algorithm in segmenting images that involve low-quality data and occlusions. The proposed framework is especially powerful in handling ''multimodal'' shape densities.