Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
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
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
An edge-weighted centroidal Voronoi tessellation model for image segmentation
IEEE Transactions on Image Processing
Watershed Cuts: Thinnings, Shortest Path Forests, and Topological Watersheds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Grain segmentation on 3D superalloy images provides superalloy's micro-structures, based on which many physical and mechanical properties can be evaluated. This is a challenging problem in senses of (1) the number of grains in a superalloy sample could be thousands or even more; (2) the intensity within a grain may not be homogeneous; and (3) superalloy images usually contains carbides and noises. Recently, the Multichannel Edge-Weighted Centroid Voronoi Tessellation (MCEWCVT) algorithm [1] was developed for grain segmentation and showed better performance than many widely used image segmentation algorithms. However, as a general-purpose clustering algorithm, MCEWCVT does not consider possible prior knowledge from material scientists in the process of grain segmentation. In this paper, we address this issue by defining an energy minimization problem which subject to certain constraints. Then we develop a Constrained Multichannel Edge-Weighted Centroid Voronoi Tessellation (CMEWCVT) algorithm to effectively solve this constrained minimization problem. In particular, manually annotated segmentation on a very small set of 2D slices are taken as constraints and incorporated into the whole clustering process. Experimental results demonstrate that the proposed CMEWCVT algorithm significantly improve the previous grain-segmentation performance.