A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

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A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

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作者：Pablo Mesejo^a ; ^b ; ^c ; ^{pmesejo@ce.unipr.it" class="auth_mail" title="E-mail the corresponding author} ; Ó ; scar Ibá ; ñ ; ez^d ; ^{oscar.ibanez@decsai.ugr.es" class="auth_mail" title="E-mail the corresponding author} ; Ó ; scar Cordó ; n^d ; ^{ocordon@decsai.ugr.es" class="auth_mail" title="E-mail the corresponding author} ; Stefano Cagnoni^a ; ^{cagnoni@ce.unipr.it" class="auth_mail" title="E-mail the corresponding author}
关键词：Deformable models ; Metaheuristics ; Image segmentation ; Stochastic optimization ; Global continuous optimization
刊名：Applied Soft Computing
出版年：2016
出版时间：July 2016
年：2016
卷：44
期：Complete
页码：1-29
全文大小：3757 K

文摘

•: Metaheuristics (MHs) are general-purpose stochastic optimization methods.
•: A Deformable Model (DM) tries to maximize its overlap with the object to segment.
•: This survey is the first review about the hybridization of DMs and MHs.
•: We provide guidelines to choose/design your hybrid segmentation approach.
•: This review paper studies, analyzes and contextualizes more than 120 papers.
•: MHs help in parameters selection, initial boundary location and DM contour evolution.

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