Abstract:
Background: The image fusion model integrates information from two or more images into a single image that is more informative and appropriate for visual perception or computer analysis.
Material and methods: A Samsung 315 digital camera was used to collect the 2,800 datasets, which are photos and postures of randomly chosen students from the Department of Computer Science at Ladoke Akintola University of Technology. The datasets were then normalized to a consistent size of 300 x 300 pixels. Forty percent of the photos were used for testing, and sixty percent of the images were used for training.
Results: The results showed that the EIHS-POA technique has a better performance in accuracy, sensitivity, specificity, precision, and false positive rate than the IHS-POA and POA techniques as enumerated for EIHS-POA datasets, with a recognition accuracy of 96.90%, a sensitivity of 99.37%, a specificity of 92.59%, and a precision of 96.90% compared to the IHS-POA technique, with a recognition accuracy of 95.56%, 97.46% sensitivity, 91.11% specificity, and 96.24% precision. Also, with the EIHS-POA technique, 98.44% of recognition accuracy, 98.41% of sensitivity, 98.52% of specificity, and 99.36% of precision. With the IHS-POA technique, recognition accuracy is 96.44%, with 96.51% of sensitivity, 96.30% of specificity, and 98.38% of precision. The EIHS-POA technique has a lower false positive rate of 7.41%, 5.19%, 2.96%, and 1.48% for enhanced PAN and MULT-SPEC images with recognition times of 100.56s, 101.67s, 107.75s, and 106.97s.
Conclusion: It was concluded that the evaluation obtained in the Enhanced Intensity Hue Saturation Pelican Optimization Algorithm (EIHS-POA)-based procedure had improved high resolution and high visual perception in all instances. The result provided evidence of the importance of applying a Pelican Optimization Algorithm-Based Model to find the high resolution and high visual perception of the system.
Keywords: Enhanced Intensity Hue Saturation Pelican Optimization Algorithm (EIHS-POA), image fusion, pelican optimization algorithm
Cite:
Isiaka Akinkunmi Adeyemo, Oyewale Oladoyin Tunbosun, Stephen Olatunde Olabiyisi, Funmilayo Alaba Ajala, "IMPLEMENTATION OF THE FORMULATED PELICAN OPTIMIZATION ALGORITHM-BASED INTENSITY HUE SATURATION MODEL USING MATLAB (R2016a) INTEGRATED ENVIRONMENT", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 2, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13216.
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DOI:
10.17148/IJARCCE.2024.13216
