On active contour models and balloons
CVGIP: Image Understanding
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
SIAM Review
Multiple Contour Finding and Perceptual Grouping using Minimal Paths
Journal of Mathematical Imaging and Vision
A Level Set Model for Image Classification
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Global Minimum for Active Contour Models: A Minimal Path Approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Image segmentation by reaction-diffusion bubbles
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
IEEE Transactions on Image Processing
Journal of Mathematical Imaging and Vision
Video object contour tracking using improved dual-front active contour
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
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Most variational active contour models are designed to find the “desirable” local minima of data-dependent energy functionals with the hope of avoiding undesirable configurations due to noise or complex image structure. As such, there has been much research into the design of complex region-based energy functionals that are less likely to yield undesirable local minima. Unfortunately, most of these more “robust” region-based energy functionals are applicable to a much narrower class of imagery due to stronger assumptions about the underlying image data. Devising new implementation algorithms for active contours that attempt to capture more global minimizers of already proposed image-based energies would allow us to choose an energy that makes sense for a particular class of energy without concern over its sensitivity to local minima. However, sometimes the completely-global minimum is just as undesirable as a minimum that is too local. In this paper, we propose a novel, fast and flexible dual front implementation of active contours, motivated by minimal path techniques and utilizing fast sweeping algorithms, which is easily manipulated to yield minima with variable “degrees” of localness and globalness. The ability to gracefully move from capturing minima that are more local (according to the initial placement of the active contour/surface) to minima that are more global makes it much easier to obtain “desirable” minimizers (which often are neither the most local nor the most global). As the examples, we illustrate the 2D and 3D implementations of this dual-front active contour for image segmentation from MRI imagery.