<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hong Peng</style></author><author><style face="normal" font="default" size="100%">Jun Wang</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%"> Optimal multi-level thresholding with membrane computing</style></title><secondary-title><style face="normal" font="default" size="100%">Digital Signal Processing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell-like P systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Histogram</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-level thresholding</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">53–64</style></pages><custom1><style face="normal" font="default" size="100%">1.495</style></custom1><custom2><style face="normal" font="default" size="100%">98/248 - Q2</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hong Peng</style></author><author><style face="normal" font="default" size="100%">Jun Wang</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author><author><style face="normal" font="default" size="100%">Peng Shi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel image thresholding method based on membrane computing and fuzzy entropy</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Intelligent and Fuzzy Systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">fuzzy entropy</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">thresholding method</style></keyword><keyword><style  face="normal" font="default" size="100%">Tissue P Systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://iospress.metapress.com/content/j542m5p0048588g2/?p=ea45f12cd9f04dd4815022f7d89cf1f8&pi=14</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">24</style></edition><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam, Netherlands</style></pub-location><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">229-237</style></pages><abstract><style face="normal" font="default" size="100%">Multi-level thresholding methods are a class of most popular image segmentation techniques, however, they are not computationally efficient since they exhaustively search the optimal thresholds to optimize the objective function. In order to eliminate the shortcoming, a novel multi-level thresholding method for image segmentation based on tissue P systems is proposed in this paper. The fuzzy entropy is used as the evaluation criterion to find optimal segmentation thresholds. The presented method can effectively search the optimal thresholds for multi-level thresholding based on fuzzy entropy due to parallel computing ability and particular mechanism of tissue P systems. Experimental results of both qualitative and quantitative comparisons for the proposed method and several existing methods illustrate its applicability and effectiveness.</style></abstract><custom1><style face="normal" font="default" size="100%">0.788</style></custom1><custom2><style face="normal" font="default" size="100%">81/114 - Q3</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hong Peng</style></author><author><style face="normal" font="default" size="100%">Jie Shao</style></author><author><style face="normal" font="default" size="100%">Bing Li</style></author><author><style face="normal" font="default" size="100%">Jun Wang</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author><author><style face="normal" font="default" size="100%">Yang Jiang</style></author><author><style face="normal" font="default" size="100%">Yufan Yang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Thresholding with Cell-like P Systems</style></title><secondary-title><style face="normal" font="default" size="100%">Tenth Brainstorming Week on Membrane Computing</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of the Tenth Brainstorming Week on Membrane Computing</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Celllike P systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Thresholding approach</style></keyword><keyword><style  face="normal" font="default" size="100%">Total fuzzy entropy</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.gcn.us.es/10BWMC/10BWMCvolII/papers/Hong%20Peng%20et%20al%20-%2010th%20BWMC.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Fénix Editora</style></publisher><pub-location><style face="normal" font="default" size="100%">Seville, Spain</style></pub-location><volume><style face="normal" font="default" size="100%">II</style></volume><pages><style face="normal" font="default" size="100%">75-88</style></pages><abstract><style face="normal" font="default" size="100%">P systems are a new class of distributed parallel computing models. In this
paper, a novel three-level thresholding approach for image segmentation based on celllike P systems is proposed in order to improve the computational eﬃciency of multilevel thresholding. A cell-like P system with a specially designed membrane structure is
developed and an improved evolution mechanism is integrated into the cell-like P system.
Due to parallel computing ability and particular mechanism of the cell-like P system, the
presented thresholding approach can eﬀectively search the optimal thresholds for threelevel thresholding based on total fuzzy entropy. Experimental results of both qualitative
and quantitative comparisons for the proposed approach and GA-based and PSO-based
approaches illustrate the applicability and eﬀectiveness.</style></abstract></record></records></xml>