Computer Vision, Graphics, and Image Processing
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
A fast level set method for propagating interfaces
Journal of Computational Physics
Tree methods for moving interfaces
Journal of Computational Physics
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
Normalized Gradient Vector Diffusion and Image Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Medical Image Segmentation Using Topologically Adaptable Snakes
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Medical image segmentation using topologically adaptable surfaces
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Multiscale Texture Enhancement
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Deformable Model with Non-euclidean Metrics
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Recursive Gaussian Derivative Filters
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Curvature of n-dimensional space curves in grey-value images
IEEE Transactions on Image Processing
A Combinatorial Method for Topology Adaptations in 3D Deformable Models
International Journal of Computer Vision
Fast segmentation of the mitral valve leaflet in echocardiography
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
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We present a parametric deformable model which recovers image components with a complexity independent from the resolution of input images. The proposed model also automatically changes its topology and remains fully compatible with the general framework of deformable models. More precisely, the image space is equipped with a metric that expands salient image details according to their strength and their curvature. During the whole evolution of the model, the sampling of the contour is kept regular with respect to this metric. By this way, the vertex density is reduced along most parts of the curve while a high quality of shape representation is preserved. The complexity of the deformable model is thus improved and is no longer influenced by feature-preserving changes in the resolution of input images. Building the metric requires a prior estimation of contour curvature. It is obtained using a robust estimator which investigates the local variations in the orientation of image gradient. Experimental results on both computer generated and biomedical images are presented to illustrate the advantages of our approach.