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
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tree methods for moving interfaces
Journal of Computational Physics
New Algorithms for Controlling Active Contours Shape and Topology
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Discrete Deformable Boundaries for the Segmentation of Multidimensional Images
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Medical Image Segmentation Using Topologically Adaptable Snakes
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Deformable model with a complexity independent from image resolution
Computer Vision and Image Understanding
Journal of Mathematical Imaging and Vision
Deformable model with a complexity independent from image resolution
Computer Vision and Image Understanding
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Deformable models like snakes are a classical tool for image segmentation. Highly deformable models extend them with the ability to handle dynamic topological changes, and therefore to extract arbitrary complex shapes. However, the resolution of these models largely depends on the resolution of the image. As a consequence, their time and memory complexity increases at least as fast as the size of input data. In this paper we extend an existing highly deformable model, so that it is able to locally adapt its resolution with respect to its position. With this property, a significant precision is achieved in the interesting parts of the image, while a coarse resolution is maintained elsewhere. The general idea is to replace the Euclidean metric of the image space by a deformed non-Euclidean metric, which geometrically expands areas of interest. With this approach, we obtain a new model that follows the robust framework of classical deformable models, while offering a significant independence from both the size of input data and the geometric complexity of image components.