Enhanced Parallel Generation of Tree Structures for the Recognition of 3D Images
|Author/s||Real Jurado, Pedro
Molina Abril, Helena
Díaz del Río, Fernando
Blanco Trejo, Sergio
Onchis, Darian M.
|Department||Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII)
Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos
|Abstract||Segmentations of a digital object based on a connectivity
criterion at n-xel or sub-n-xel level are useful tools in image topological
analysis and recognition. Working with cell complex analogous of digital
objects, an ...
Segmentations of a digital object based on a connectivity criterion at n-xel or sub-n-xel level are useful tools in image topological analysis and recognition. Working with cell complex analogous of digital objects, an example of this kind of segmentation is that obtained from the combinatorial representation so called Homological Spanning Forest (HSF, for short) which, informally, classifies the cells of the complex as belonging to regions containing the maximal number of cells sharing the same homological (algebraic homology with coefficient in a field) information. We design here a parallel method for computing a HSF (using homology with coefficients in Z/2Z) of a 3D digital object. If this object is included in a 3D image of m1 × m2 × m3 voxels, its theoretical time complexity order is near O(log(m1 + m2 + m3)), under the assumption that a processing element is available for each voxel. A prototype implementation validating our results has been written and several synthetic, random and medical tridimensional images have been used for testing. The experiments allow us to assert that the number of iterations in which the homological information is found varies only to a small extent from the theoretical computational time.
|Citation||Real Jurado, P., Molina Abril, H., Díaz del Río, F., Blanco Trejo, S. y Onchis, D. (2019). Enhanced Parallel Generation of Tree Structures for the Recognition of 3D Images. En MCPR 2019: 11th Mexican Conference on Pattern Recognition (292-301), Querétaro, Mexico: Springer.|
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