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...

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Authors: Peng, Hong, Wang, Jun, Pérez Jiménez, Mario de Jesús
Format: article
Status:Versión enviada para evaluación y publicación
Publication Date:2015
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/116326
Online Access:https://hdl.handle.net/11441/116326
https://doi.org/10.1016/j.dsp.2014.10.006
Access Level:Open access
Keyword:Membrane Computing
Cell-like P systems
Image segmentation
Multi-level thresholding
Histogram
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spelling Optimal multi-level thresholding with membrane computingPeng, HongWang, JunPérez Jiménez, Mario de JesúsMembrane ComputingCell-like P systemsImage segmentationMulti-level thresholdingHistogramThe 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.National Natural Science Foundation of China No. 61170030Chunhui Project Foundation of the Education Department of China No. Z2012025Chunhui Project Foundation of the Education Department of China No. Z2012031Research Fund of Sichuan Key Technology Research and Development Program No. 2013GZX0155Open Research Funds of Key Laboratory of High Performance Scientific Computing No. SZJJ2012-002ElsevierCiencias de la Computación e Inteligencia ArtificialTIC193: Computación NaturalNational Natural Science Foundation of ChinaChunhui Project Foundation of the Education Department of ChinaResearch Fund of Sichuan Key Technology Research and Development ProgramOpen Research Funds of Key Laboratory of High Performance Scientific Computing, China2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/116326https://doi.org/10.1016/j.dsp.2014.10.006reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésDigital Signal Processing, 37 (February 2015), 53-64.No. 61170030No. Z2012025No. Z2012031No. 2013GZX0155No. SZJJ2012-002https://www.sciencedirect.com/science/article/pii/S1051200414003157info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1163262026-06-17T12:51:07Z
dc.title.none.fl_str_mv Optimal multi-level thresholding with membrane computing
title Optimal multi-level thresholding with membrane computing
spellingShingle Optimal multi-level thresholding with membrane computing
Peng, Hong
Membrane Computing
Cell-like P systems
Image segmentation
Multi-level thresholding
Histogram
title_short Optimal multi-level thresholding with membrane computing
title_full Optimal multi-level thresholding with membrane computing
title_fullStr Optimal multi-level thresholding with membrane computing
title_full_unstemmed Optimal multi-level thresholding with membrane computing
title_sort Optimal multi-level thresholding with membrane computing
dc.creator.none.fl_str_mv Peng, Hong
Wang, Jun
Pérez Jiménez, Mario de Jesús
author Peng, Hong
author_facet Peng, Hong
Wang, Jun
Pérez Jiménez, Mario de Jesús
author_role author
author2 Wang, Jun
Pérez Jiménez, Mario de Jesús
author2_role author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
TIC193: Computación Natural
National Natural Science Foundation of China
Chunhui Project Foundation of the Education Department of China
Research Fund of Sichuan Key Technology Research and Development Program
Open Research Funds of Key Laboratory of High Performance Scientific Computing, China
dc.subject.none.fl_str_mv Membrane Computing
Cell-like P systems
Image segmentation
Multi-level thresholding
Histogram
topic Membrane Computing
Cell-like P systems
Image segmentation
Multi-level thresholding
Histogram
description 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.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/116326
https://doi.org/10.1016/j.dsp.2014.10.006
url https://hdl.handle.net/11441/116326
https://doi.org/10.1016/j.dsp.2014.10.006
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Digital Signal Processing, 37 (February 2015), 53-64.
No. 61170030
No. Z2012025
No. Z2012031
No. 2013GZX0155
No. SZJJ2012-002
https://www.sciencedirect.com/science/article/pii/S1051200414003157
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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