Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Computing minimal surfaces via level set curvature flow
Journal of Computational Physics
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
International Journal of Computer Vision
Generalized gradient vector flow external forces for active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
A TASOM-based algorithm for active contour modeling
Pattern Recognition Letters
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image segmentation by reaction-diffusion bubbles
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Semi-supervised learning for structured output variables
ICML '06 Proceedings of the 23rd international conference on Machine learning
Local or Global Minima: Flexible Dual-Front Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semisupervised Learning of Hidden Markov Models via a Homotopy Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A class of constrained clustering algorithms for object boundary extraction
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Multiple contour extraction from graylevel images using an artificial neural network
IEEE Transactions on Image Processing
A faster converging snake algorithm to locate object boundaries
IEEE Transactions on Image Processing
Dynamic directional gradient vector flow for snakes
IEEE Transactions on Image Processing
Multigrid Geometric Active Contour Models
IEEE Transactions on Image Processing
Active Contour External Force Using Vector Field Convolution for Image Segmentation
IEEE Transactions on Image Processing
Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme
Information Sciences: an International Journal
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A coarse-to-fine boundary location with a self-organizing map (SOM)-like method is proposed in this paper. Inspired from the conventional SOM and universal gravitation, given a small quantity of supervision seeds from the desired boundaries, neurons are used to evolve to the desired boundaries in a coarse-to-fine framework. The major components of this framework are the designs of union action and evolving rate. In the course of neuron evolution, the union actions acting on these neurons will offer them the evolving directions. Also controlled by the corresponding referenced gradients, the neurons' evolving rates are adaptively adjusted at different positions. With the union actions and evolving rates, the neurons will evolve with appropriate manners to expand the set of feature points on the desired boundaries. The newly expanded feature points will cause the generation updates for feature points and neurons, and offer new information to guide the new generation of neurons to the boundaries. What is more, the proposed multiround evolution is as well a coarse-to-fine way for boundary location. Experiments and comparisons show that the proposed method performs well in complex long concavities, inhomogeneous and weak boundary location with good initialization flexibility.