Optimal multi-level thresholding with membrane computing
The conventional methods are not effective and efficient for image multi-level thresholding due to time-consuming and expensive computation cost. The multi-level thresholding problem can be posed as anoptimization problem, optimizing some thresholding criterion. In this paper, membrane computing isi...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/116326 |
| Acceso en línea: | https://hdl.handle.net/11441/116326 https://doi.org/10.1016/j.dsp.2014.10.006 |
| Access Level: | acceso abierto |
| Palabra clave: | Membrane Computing Cell-like P systems Image segmentation Multi-level thresholding Histogram |
| Sumario: | The conventional methods are not effective and efficient for image multi-level thresholding due to time-consuming and expensive computation cost. The multi-level thresholding problem can be posed as anoptimization problem, optimizing some thresholding criterion. In this paper, membrane computing isintroduced to propose an efficient and robust multi-level thresholding method, where a cell-like P systemwith the nested structure of three layers is designed as its computing framework. Moreover, an improvedvelocity-position model is developed to evolve the objects in membranes based on the special membranestructure and communication mechanism of objects. Under the control of evolution-communicationmechanism of objects, the cell-like P system can efficiently exploit the best multi-level thresholds for animage. Simulation experiments on nine standard images compare the proposed multi-level thresholdingmethod with several state-of-the-art multi-level thresholding methods and demonstrate its superiority. |
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