Active shape models—their training and application
Computer Vision and Image Understanding
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geodesic Active Regions for Supervised Texture Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ACM SIGGRAPH 2004 Papers
A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Shape-Based Approach to Robust Image Segmentation using Kernel PCA
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A tutorial on spectral clustering
Statistics and Computing
Looking for Shapes in Two-Dimensional Cluttered Point Clouds
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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
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Shape models (SMs), capturing the common features of a set of training shapes, represent a new incoming object based on its projection onto the corresponding model. Given a set of learned SMs representing different objects classes, and an image with a new shape, this work introduces a joint classification-segmentation framework with a twofold goal. First, to automatically select the SM that best represents the object, and second, to accurately segment the image taking into account both the image information and the features and variations learned from the online selected model. A new energy functional is introduced that simultaneously accomplishes both goals. Model selection is performed based on a shape similarity measure, online determining which model to use at each iteration of the steepest descent minimization, allowing for model switching and adaptation to the data. High-order SMs are used in order to deal with very similar object classes and natural variability within them. Position and transformation invariance is included as part of the modeling as well. The presentation of the framework is complemented with examples for the difficult task of simultaneously classifying and segmenting closely related shapes, such as stages of human activities, in images with severe occlusions.