Introduction to mathematical morphology
Computer Vision, Graphics, and Image Processing
Morphological structuring element decomposition
Computer Vision, Graphics, and Image Processing
Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposition of Convex Polygonal Morphological Structuring Elements into Neighborhood Subsets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Optimization Algorithms with Pascal Programs
Discrete Optimization Algorithms with Pascal Programs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Binary Morphological Algorithms on a Massively Parallel Processor
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
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Morphological operations with 3D images require a huge amount of computation. The decomposition of structuring elements used in the morphological operations such as dilation and erosion greatly reduces the amount of computation. This paper presents a new method for the decomposition of a 3D convex structuring element into a set of basis convex structuring elements. Furthermore, the decomposition method is applied to the neighborhood decomposition, in which each basis is one of the combinations of the origin voxel and its 26 neighborhood voxels. First, we derived the set of decomposition conditions on the lengths of the original and the basis convex structuring elements, and then the decomposition problem is converted into a linear integer optimization problem. The objective of the optimization is to minimize a cost function representing the optimal criterion of the parallel processing computer architecture on which the operation is performed. Thus, our method can be used to obtain the different optimal decompositions minimizing the amount of computation for different parallel processing computer architectures.