Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy

Bhandari, Ashish Kumar; Singh, Vineet; Kumar, Anil; Singh, Girish Kumar

doi:10.1016/j.eswa.2013.10.059

Cited by 417 publications

(103 citation statements)

References 49 publications

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“…Several other approaches have proposed automatic detection of brain tumor based on using segmentation with other filtering processes [13][14][15][16][17][18][19], which are summarized in Table 2.…”

Section: Related Workmentioning

confidence: 99%

Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding

Alyahya¹,

Abu-Shareha²

2018

IJITCS

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Abstract-In this paper, the accuracy of the entropybased thresholding approaches in brain tumor detection framework is investigated. Entropies are information gain methods that have been used for image thresholding with various application and different image modalities. The accuracy of the existing entropies for image thresholding has been studied in general domain (e.g.: natural images) and were not compared thoroughly. Thus, a framework for brain tumor segmentation is proposed with the core process of the image thresholding, in order to evaluate the accuracy of the entropies. Five entropies, namely, Renyi, Maximum, Minimum, Tsallis and Kapur are evaluated. Moreover, the aggregation of entropies was implemented and evaluated. The results show that the maximum entropy is the best for brain tumor detection. Moreover, it was shown that aggregation of entropies output does not enhance the result, however, it works as automatic selection of the best result and produces the results with the highest accuracy.

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Section: Related Workmentioning

confidence: 99%

Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding

Alyahya¹,

Abu-Shareha²

2018

IJITCS

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“…Applications of cuckoo search includes for selecting optimal matching parameters in milling operations (92), cuckoo search involves in feed forward neural network training (93), for structural design optimization of vehicle components (94), for solving travelling sales man problem (95), the basic cuckoo search algorithm has been modified and utilized for unconstrained optimization problems (96), for satellite image segmentation in multi-level thresholding (97), the cuckoo search algorithm have been improved for global optimization (98), the hybridized cuckoo with fuzzy for solving multi-objective scheduling problem (99), parameter estimation for chaotic systems using cuckoo search algorithm (100).…”

Section: B Applications Areas Of Cuckoo Seach Optimizationmentioning

confidence: 99%

A Review of Bio Inspired Computing and its Applications

kumar¹,

Bhargava²

2018

J.JBTW

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Bio Inspired computation is the part of Artificial intelligence which was inspired by the biological behaviors of biological systems. Swarm intelligence is the collective behavior of an organized group in day-to-day life. Common examples of swarm intelligence include ant colony, bee colony, etc. and some are non-swarm intelligence like bat algorithm, etc. This study mainly focuses on application areas of various bio inspired computing based swarm and non-swarm intelligence. This review also discusses the newly developed algorithms. Specific application areas of such algorithms have been discussed in this research. This research highlighted the future scope of present algorithms.

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“…Because the WDO algorithm has only a few parameters that need to be controlled, and it is very easy to implement, it has received much attention by various scholars since it was put forward. In recent years, the WDO algorithm has also been applied in many fields, for instance, in satellite image segmentation for multilevel thresholding [11], cloud resource allocation scheme [12], collision avoidance for dynamic environments [13], design of two-channel filter bank [14], synthesis of linear array antenna [15], and so on.…”

Section: Introductionmentioning

confidence: 99%

Quantum Wind Driven Optimization for Unmanned Combat Air Vehicle Path Planning

et al. 2015

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Unmanned combat air vehicle (UCAV) path planning aims to calculate the optimal or suboptimal flight path considering the different threats and constraints under the complex battlefield environment. This flight path can help the UCAV avoid enemy threats and improve the efficiency of the investigation. This paper presents a new quantum wind driven optimization (QWDO) for the path planning of UCAV. QWDO algorithm uses quantum rotation gate strategy on population evolution and the quantum non-gate strategy to realize the individual variation of population. These operations improve the diversity of population and avoid premature convergence. This paper tests this optimization in two instances. The experimental results show that the proposed algorithm is feasible in these two cases. Compared to quantum bat algorithm (QBA), quantum particle swarm optimization (QPSO), wind driven optimization (WDO), bat algorithm (BA), particle swarm optimization (PSO), and differential evolution (DE), the QWDO algorithm exhibited better performance. The simulation results demonstrate that the QWDO algorithm is an effective and feasible method for solving UCAV path planning.Keywords: wind driven optimization (WDO); unmanned combat air vehicle (UCAV); path planning; quantum wind driven optimization (QWDO) OPEN ACCESSAppl. Sci. 2015Sci. , 5 1458

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Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy

Cited by 417 publications

References 49 publications

Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding

Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding

A Review of Bio Inspired Computing and its Applications

Quantum Wind Driven Optimization for Unmanned Combat Air Vehicle Path Planning

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