Archives
International Journal of Zoology and Applied Biosciences Research Article
Enhanced image Mosaicing using Matlab: A feature-based approach for seamless panoramic image generation
Aiswarya R, Ezhil T, Lavanya R, Karthick K, Jenifer E
Year : 2025 | Pages: 649-653
Received on: 06/10/2025
Revised on: 27/10/2025
Accepted on: 26/11/2025
Published on: 01/12/2025
-
Aiswarya R, Ezhil T, Lavanya R, Karthick K, Jenifer E( 2025).
Enhanced image Mosaicing using Matlab: A feature-based approach for seamless panoramic image generation
. International Journal of Zoology and Applied Biosciences, 10( 6), 649-653.
-
click to view the cite format
Abstract
Image mosaicing is a fundamental digital image processing technique used to combine multiple overlapping images into a single, seamless panoramic output. This study presents an improved mosaicing framework implemented in MATLAB, emphasizing robust feature detection, image registration, adaptive blending, and geometric correction. The Scale-Invariant Feature Transform (SIFT) algorithm is employed to extract distinctive, scale-invariant control points, ensuring accurate matching across images with variations in illumination, scale, and viewpoint. The proposed approach integrates effective calibration and blending strategies, enabling the creation of radiometrically balanced mosaics without noticeable seams. Simulation results demonstrate the capability of this method to handle image distortions, camera motion, and environmental variations, thereby providing a reliable solution for panoramic image construction in applications such as remote sensing, biomedical imaging, virtual reality, and object visualization.
Keywords
Image Mosaicing, MATLAB, SIFT Algorithm, Image Registration, Feature Extraction.
-
Full Article PDF (
10)
- View HTML Article
Copy Rights
© The Author(s) 2025. This article is published by International Journal of Zoology and Applied Biosciences under the terms of the Creative Commons Attribution 4.0 International License (creativecommons.org), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
