Recursive region splitting at hierarchical scope views
Computer Vision, Graphics, and Image Processing
Subdivisions of n-dimensional spaces and n-dimensional generalized maps
SCG '89 Proceedings of the fifth annual symposium on Computational geometry
Topological models for boundary representation: a comparison with n-dimensional generalized maps
Computer-Aided Design - Beyond solid modelling
Two linear time Union-Find strategies for image processing
Theoretical Computer Science
Topological Encoding of 3D Segmented Images
DGCI '00 Proceedings of the 9th International Conference on Discrete Geometry for Computer Imagery
Computer Vision and Image Understanding
Topological model for 3D image representation: Definition and incremental extraction algorithm
Computer Vision and Image Understanding
3D image topological structuring with an oriented boundary graph for split and merge segmentation
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Polynomial algorithms for subisomorphism of nD open combinatorial maps
Computer Vision and Image Understanding
Computing homology for surfaces with generalized maps: application to 3d images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
A distance measure between labeled combinatorial maps
Computer Vision and Image Understanding
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One of the most commonly used approach to segment a 2D image is the split and merge approach. In this paper, we are defining these two operations in 3D within the topological maps framework. This mathematic model of regions segmented image representation allows us to define these algorithms in a local and generic way. Moreover, we are defining a new operation, the corefining, which allows to treat big images. They are cut into small units, treated separately, then the result of each of them are combined to reconstruct the final representation. These three operations let us view efficient 3D segmentation algorithms, which is a difficult problem due to the size of data to treat.