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
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
A Level Set Model for Image Classification
International Journal of Computer Vision
Multiple Contour Finding and Perceptual Grouping using Minimal Paths
Journal of Mathematical Imaging and Vision
A Robust Snake Implementation; A Dual Active Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
International Journal of Computer Vision
Surface Extraction from Volumetric Images Using Deformable Meshes: A Comparative Study
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Image segmentation by reaction-diffusion bubbles
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Video object tracking with feedback of performance measures
IEEE Transactions on Circuits and Systems for Video Technology
Cytoplasm contour approximation based on color fuzzy sets and color gradient
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Segmentation of medical images of different modalities using distance weighted C-V model
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Archaeological trace extraction by a local directional active contour approach
Pattern Recognition
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This paper presents a novel fast model for active contours to detect objects in an image, based on techniques of curve evolution. The proposed model can detect objects whose boundaries are not necessarily defined by gradient, based on the minimization of a fuzzy energy, which can be seen as a particular case of a minimal partition problem. This fuzzy energy is used as the model motivation power evolving the active contour, which will stop on the desired object boundary. However, the stopping term does not depend on the gradient of the image, as most of the classical active contours, but instead is related to the image color and spatial segments. The fuzziness of the energy provides a balanced technique with a strong ability to reject "weak" local minima. Moreover, this approach converges to the desired object boundary very fast, since it does not solve the Euler-Lagrange equations of the underlying problem, but, instead, calculates the fuzzy energy alterations directly. The theoretical properties and various experiments presented demonstrate that the proposed fuzzy energy-based active contour is better and more robust than classical snake methods based on the gradient or other kind of energies.