受自然启发的优化算法及其在多阈值图像分割中的意义：全面综述
Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review.
作者信息
Rai Rebika, Das Arunita, Dhal Krishna Gopal
机构信息
Department of Computer Applications, Sikkim University, Sikkim, India.
Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India.
出版信息
Evol Syst (Berl). 2022;13(6):889-945. doi: 10.1007/s12530-022-09425-5. Epub 2022 Feb 21.
Abstract
Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.
摘要
多阈值分割（MLT）被认为是图像分割中一个重要且必要的研究领域，它能够有效解决在分析具有复杂非线性条件的多面图像的分割区域时所引发的难题。MLT作为一个简单的指数组合优化问题，通常通过一个复杂的目标函数要求来表述，而这只能通过非确定性方法来解决。因此，研究人员正在采用自然启发式优化算法（NIOA）作为一种替代方法，该方法可广泛用于解决与MLT相关的问题。本文对过去三年（2019 - 2021年）最近形成的新型NIOA进行了综述，重点介绍并探讨了基于NIOA的图像多阈值分割模型开发过程中遇到的主要挑战。
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