Computer Vision, Graphics, and Image Processing
On active contour models and balloons
CVGIP: Image Understanding
Feature extraction from faces using deformable templates
International Journal of Computer Vision
High-resolution conservative algorithms for advection in incompressible flow
SIAM Journal on Numerical Analysis
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
Minimal Surfaces Based Object Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Real-Time Interactive Path Extraction with on-the-Fly Adaptation of the External Forces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Lax-Friedrichs sweeping scheme for static Hamilton-Jacobi equations
Journal of Computational Physics
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Fast Constrained Surface Extraction by Minimal Paths
International Journal of Computer Vision
Faster segmentation algorithm for optical coherence tomography images with guaranteed smoothness
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
A survey of graph theoretical approaches to image segmentation
Pattern Recognition
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We introduce a novel implicit approach for single object segmentation in 3D images. The boundary surface of this object is assumed to contain two known curves (the constraining curves), given by an expert. The aim of our method is to find this surface by exploiting as much as possible the information given in the supplied curves. As for active surfaces, we use a cost potential which penalizes image regions of low interest (most likely areas of low gradient or away from the surface to be extracted). In order to avoid local minima, we introduce a new partial differential equation and use its solution for segmentation. We show that the zero level set of this solution contains the constraining curves as well as a set of paths joining them. These paths globally minimize an energy which is defined from the cost potential. Our approach is in fact an elegant, implicit extension to surfaces of the minimal path framework already known for 2D image segmentation. As for this previous approach, and unlike other variational methods, our method is not prone to local minima traps of the energy. We present a fast implementation which has been successfully applied to 3D medical and synthetic images.