Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization

Murinto, Murinto and Prahara, Adhi and Iman HU, Erik (2002) Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization. [Artikel Dosen]

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Abstract

The image segmentatation technique that is often used is thresholding. Image
segmentation is a process of dividing the image into different regions according
to their similar characteristics. This research proposes a multilevel thresholding
algorithm using modified particle swarm optimization to solve a segmentation
problem. The threshold optimal values are determined by maximizing Otsu’s
objective function using optimization technique namely particle swarm
optimization based on the logarithmic decreasing inertia weight (LogDIWPSO).
The proposed method reduces the computational time to find the optimum
thresholds of multilevel thresholding which evaluated on several grayscale
images. A detailed comparison analysis with other multilevel thresholding based
techniques namely particle swarm optimization (PSO), iterative particle swarm
optimization (IPSO), and genetic algorithms (GA), From the experiments,
Modified particle swarm optimization (MoPSO) produces better performance
compared to the other methods in terms of fitness value, robustness and
convergence. Therefore, it can be concluded that MoPSO is a good approach in
finding the optimal threshold value.

Item Type:	Artikel Dosen
Keyword:	grayscale image, inertia weight, image segmentation, particle swarm optimization.
Subjects:	Q Science > Q Science (General)
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Informatics Engineering (S1-Teknik Informatika)
Depositing User:	murinto murinto
Date Deposited:	18 Apr 2023 09:45
Last Modified:	23 Sep 2023 06:13
URI:	http://eprints.uad.ac.id/id/eprint/42983

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