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Journal ArticleDOI

A context sensitive Masi entropy for multilevel image segmentation using moth swarm algorithm

TLDR
Unlike other meta-heuristic algorithms used for thresholding operations, MSA provides a higher performance regarding threshold quality and low computational cost and experimental data boosts the use of MSA for energy curve based thresholding with Masi entropy.
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This article is published in Infrared Physics & Technology.The article was published on 2019-05-01. It has received 31 citations till now. The article focuses on the topics: Image segmentation & Thresholding.

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Citations
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Journal ArticleDOI

Multi-Strategy Emperor Penguin Optimizer for RGB Histogram-Based Color Satellite Image Segmentation Using Masi Entropy

TL;DR: The proposed multi-strategy emperor penguin optimizer (called MSEPO) is proposed to find the optimal threshold values for three channels of RGB images and is more suitable for high-dimensional segmentation of complex satellite images.
Journal ArticleDOI

Moth Swarm Algorithm for Image Contrast Enhancement

TL;DR: The proposed approach has the propriety of eliminating noisy and irrelevant information in order to improve the distribution capacity of significant pixel intensities in the enhanced image.
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Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study

TL;DR: It was shown that the QGA outpaces the GA, and the PSO outperforms significantly the both algorithms in the optimization task, and it was found that the Renyi entropy is more suitable for the purpose of medical image multilevel thresholding.
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A local contrast fusion based 3D Otsu algorithm for multilevel image segmentation

TL;DR: The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena, which can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions.
Journal ArticleDOI

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

TL;DR: In this article , a review of NIOA-based multi-level thresholding models is presented, highlighting and exploring the major challenges encountered during the development of image multi-thresholding models.
References
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Journal ArticleDOI

Survey over image thresholding techniques and quantitative performance evaluation

TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
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Divergence measures based on the Shannon entropy

TL;DR: A novel class of information-theoretic divergence measures based on the Shannon entropy is introduced, which do not require the condition of absolute continuity to be satisfied by the probability distributions involved and are established in terms of bounds.
Journal ArticleDOI

FSIM: A Feature Similarity Index for Image Quality Assessment

TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.
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A new method for gray-level picture thresholding using the entropy of the histogram

TL;DR: Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223–237;Comput.16, 1981, 210–239) have been carefully and critically examined and a new method with a sound theoretical foundation is proposed.
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