Book Chapter10.1007/978-3-030-37218-7_38
Performance Analysis of Differential Evolution Algorithm Variants in Solving Image Segmentation
V. SandhyaSree,S. Thangavelu +1 more
- 25 Sep 2019
- pp 329-337
4
TL;DR: Experimental results shows that DE/best/1/bin algorithm out performs than the other variants of DE algorithms in solving image segmentation.
read more
Abstract: Image segmentation is an activity of dividing an image into multiple segments. Thresholding is a typical step for analyzing image, recognizing the pattern, and computer vision. Threshold value can be calculated using histogram as well as using Gaussian mixture model. but those threshold values are not the exact solution to do the image segmentation. To overcome this problem and to find the exact threshold value, differential evolution algorithm is applied. Differential evolution is considered to be meta-heuristic search and useful in solving optimization problems. DE algorithms can be applied to process Image Segmentation by viewing it as an optimization problem. In this paper, Different Differential evolution (DE) algorithms are used to perform the image segmentation and their performance is compared in solving image segmentation. Both 2 class and 3-class segmentation is applied and the algorithm performance is analyzed. Experimental results shows that DE/best/1/bin algorithm out performs than the other variants of DE algorithms
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Distributed Multithreaded Evolutionary Computing Frame Work using Differential Evolution Algorithm
S Raghul,Jeyakumar. G +1 more
- 20 Jan 2021
TL;DR: In this paper, an enhanced distributed differential algorithm framework (mtdDE) with multi-threaded islands in a distributed framework was proposed to attain maximum level of data parallelism. And the main objective of mtdDE is to reduce the computation time and to improve the solution quality for the targeted optimization problems.
5
A Comparative Study on Parameter Estimation of COVID Epidemiological Models Using Differential Evolution Algorithm
Sai Sudha Panigrahi,Arul Jayanth Muthukumar,S. Thangavelu,G. Jeyakumar,C. Shunmuga Velayutham +4 more
TL;DR: In this paper , the authors compared epidemiological models with different machine learning models based on evaluation techniques and compared the top-five heavily affected states of India having the highest number of cases are considered for the study.
4
Optimal Ship Route Search Based on Multi-Objective Genetic Algorithm
Ashwin R,I. Cr,Pavan Teja Ramana,Paladugu Shilpa,J. G +4 more
- 06 Jul 2023
TL;DR: This work presents an algorithm to plan safe and optimal routes for ships moving inaccessible areas and with unlimited traffic direction that is optimized with multiple objectives which makes it more unique and optimal.
A hybrid multi-population reinitialization strategy to tackle dynamic optimization problems
S. Raghul,G. Jeyakumar +1 more
TL;DR: This hybrid multi-population reinitialization strategy showcases its effectiveness in the successful handling of increased shift lengths and number of peaks, which are pivotal parameters in solving moving peak benchmark function.
References
Image Segmentation Based on Differential Evolution Optimization
Erik Cuevas,Daniel Zaldivar,Marco Pérez-Cisneros +2 more
- 01 Jan 2016
TL;DR: In this chapter, an automatic image multi-threshold approach based on differential evolution optimization is presented, and experimental results demonstrate the algorithm’s ability to perform automatic threshold selection while preserving main features from the original image.
13
Contiguous binomial crossover in differential evolution
Matthieu Weber,Ferrante Neri +1 more
- 29 Apr 2012
TL;DR: Experimental results show that this variant of the binomial crossover exhibits in general similar or better performance than the original one, and allows to increase significantly the execution speed of the Differential Evolution, especially in higher dimension problems.
13
An investigation on mixing heterogeneous differential evolution variants in a distributed framework
TL;DR: This paper attempts a preliminary investigation to gain insight about the cooperative dynamics of mixing the four classical differential evolution DE variants viz.
13