Bio-inspired parallel computing of representative geometrical objects of holes of binary 2D-images
In this paper, we present a bio-inspired parallel implementation of a solution of the problem of looking for the representative geometrical objects of the homology groups in a binary 2D image (extended-HGB2I problem), which is an extended version of a well-known problem in homology theory. In partic...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/156938 |
| Acceso en línea: | https://hdl.handle.net/11441/156938 https://doi.org/10.1504/IJBIC.2017.10004005 |
| Access Level: | acceso abierto |
| Palabra clave: | Computational algebraic topology Compute unified device architecture CUDA Homology groups Membrane computing Tissue-like P systems |
| Sumario: | In this paper, we present a bio-inspired parallel implementation of a solution of the problem of looking for the representative geometrical objects of the homology groups in a binary 2D image (extended-HGB2I problem), which is an extended version of a well-known problem in homology theory. In particular, given a binary 2D image, all black connected components and the representative curves of the holes of these components are obtained and labelled. To this aim, a new technique for labelling the connected components of a binary image is presented. In order to compute the solution, the formal framework uses techniques from membrane computing and the implementation has been done in a hardware architecture called compute unified device architecture (CUDA). The computational complexity of the proposed solution is O(m) with respect to the input (image) size m ∼ n2. Finally, some examples and applications are also presented. |
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