Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
On active contour models and balloons
CVGIP: Image Understanding
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction from faces using deformable templates
International Journal of Computer Vision
Region-based tracking using affine motion models in long image sequences
CVGIP: Image Understanding
Catching moving objects with snakes for motion tracking
Pattern Recognition Letters
Active shape models—their training and application
Computer Vision and Image Understanding
Active contours approach to object tracking in image sequences with complex background
Pattern Recognition Letters
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
An unbiased active contour algorithm for object tracking
Pattern Recognition Letters
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Snake Implementation; A Dual Active Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Geodesic Active Regions for Supervised Texture Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A PDE-Based Level-Set Approach for Detection and Tracking of Moving Objects
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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In the last few years it has been made clear to the research community that further improvements in classic approaches for solving low-level computer vision and image/video understanding tasks are difficult to obtain. New approaches started evolving, employing knowledge-based processing, though transforming a priori knowledge to low-level models and rules are far from being straightforward. In this paper, we examine one of the most popular active contour models, snakes, and propose a snake model, modifying terms and introducing a model-based one that eliminates basic problems through the usage of prior shape knowledge in the model. A probabilistic rule-driven utilization of the proposed model follows, being able to handle (or cope with) objects of different shapes, contour complexities and motions; different environments, indoor and outdoor; cluttered sequences; and cases where background is complex (not smooth) and when moving objects get partially occluded. The proposed method has been tested in a variety of sequences and the experimental results verify its efficiency.