Multi-objective optimization algorithm for improving the efficiency of speeded up robust features of image stitching - Sanprasit, Kittisak; Butsathip, Uraiwan; Chaiwong, Khomyuth (International Journal of Advances in Intelligent Informatics, 2025)
Image stitching to generate panoramic or composite images. This research proposes improved parameters for the fundamental matrix in the standard SURF method via multi-objective optimization. This paper compares three metaheuristic algorithms (MOWOA, MOGWO, MOGA) and evaluates their performance using the hypervolume indicator (HV). The optimal points were selected from non-dominated solutions using the MCDM and the weighted-sum method (WSM). There were two objective functions: 1) minimum of image subtraction and 2) minimum of histogram. The MOWOA is superior to the other. This approach significantly reduces stitching errors and improves performance by 24.48% over standard SURF. The proposed multi-objective optimization of fundamental matrix parameters significantly enhances SURF-based image stitching by reducing alignment and blending errors, resulting in smoother, more coherent panoramic or composite images. This is achieved by leveraging superior metaheuristic performance, particularly from MOWOA, which outperforms other algorithms. This approach increases stitching robustness and accuracy, making it highly valuable for real-world applications such as mapping, surveillance, and visual reconstruction.