Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
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
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
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
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
Real-Time Pattern Matching Using Projection Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior-based Segmentation and Shape Registration in the Presence of Perspective Distortion
International Journal of Computer Vision
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
IEEE Transactions on Image Processing
Active contours for tracking distributions
IEEE Transactions on Image Processing
Reduced set density estimator for object segmentation based on shape probabilistic representation
Journal of Visual Communication and Image Representation
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We present a new object segmentation method that is based on geodesic active contours with combined shape and appearance priors. It is known that using shape priors can significantly improve object segmentation in cluttered scenes and occlusions. Within this context, we add a new prior, based on the appearance of the object, (i.e., an image) to be segmented. This method enables the appearance pattern to be incorporated within the geodesic active contour framework with shape priors, seeking for the object whose boundaries lie on high image gradients and that best fits the shape and appearance of a reference model. The output contour results from minimizing an energy functional built of these three main terms. We show that appearance is a powerful term that distinguishes between objects with similar shapes and capable of successfully segment an object in a very cluttered environment where standard active contours (even those with shape priors) tend to fail.