Paper Info
Title
Enhanced Parallel Generation Of Tree Structures For The Recognition Of 3d Images
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 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 m(1) x m(2) x m(3) voxels, its theoretical time complexity order is near O(log(m(1) + m(2) + m(3))), 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.
Year
DOI
Venue
2019
10.1007/978-3-030-21077-9_27
PATTERN RECOGNITION, MCPR 2019
Keywords
Field
DocType
3D digital images, Parallel computing, Abstract cell complex, Homological Spanning Forest, Crack transport
Abstract cell complex,Voxel,Discrete mathematics,Computer vision,Algebraic number,Computer science,Segmentation,Parallel generation,Tree structure,Artificial intelligence,Time complexity,3d image
Conference
Volume
ISSN
Citations 
11524
0302-9743
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Pedro Real126735.40
Helena Molina-Abril28210.87
Fernando Díaz del Río3165.95
S. Blanco-Trejo400.34
Darian M. Onchis55014.08