Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
IFT-Watershed from Gray-Scale Marker
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Iterative relative fuzzy connectedness for multiple objects with multiple seeds
Computer Vision and Image Understanding
Weights and Topology: A Study of the Effects of Graph Construction on 3D Image Segmentation
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
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
Measurements of digitized objects with fuzzy borders in 2D and 3D
Image and Vision Computing
A graph-based framework for sub-pixel image segmentation
Theoretical Computer Science
Image foresting transform: on-the-fly computation of segmentation boundaries
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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The Image Foresting Transform (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned user-defined labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. In its original form, the IFT produces crisp segmentations, i.e., each image element is assigned the label of exactly one seed-point. Here, we propose a modified version of the IFT that computes region boundaries with sub-pixel precision by allowing mixed labels at region boundaries. We demonstrate that the proposed sub-pixel IFT allows properties of the segmented object to be measured with higher precision.