Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
An improved seeded region growing algorithm
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Integrating Iterative Segmentation by Watershed with Tridimensional Visualization of MRIs
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-Precision Boundary Length Estimation by Utilizing Gray-Level Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pixel Coverage Segmentation for Improved Feature Estimation
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Sub-pixel Segmentation with the Image Foresting Transform
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Estimation of moments of digitized objects with fuzzy borders
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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The Image Foresting Transform (IFT) is a framework for seeded image segmentation, based on the computation of minimal cost paths in a discrete representation of an image. In two recent publications, we have shown that the segmentations obtained by the IFT may be improved by refining the segmentation locally around the boundaries between segmented regions. Since these methods operate on a small subset of the image elements only, they may be implemented efficiently if the set of boundary elements is known. Here, we show that this set may be obtained on-the-fly, at virtually no additional cost, as a by-product of the IFT algorithm.