Shridhar, H.Harakannanavar, S.S.Chetan, R.Kanabur, V.Jayalaxmi, H.Prashanth, R.C.2026-02-062024Proceedings - 2024 2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024, 2024, Vol., , p. 105-109https://doi.org/10.1109/ICACCTech65084.2024.00027https://idr.nitk.ac.in/handle/123456789/29208Image mosaicing combines overlapping images of the same scene into a larger, seamless image. This work aims to develop a model that efficiently merges images while evaluating its performance in terms of runtime and the number of key features used. Histogram Equalization (HE) is applied to handle intensity variability, while key features are extracted using SIFT, ORB, and BRISK descriptors. K-Nearest Neighbor (KNN) matches the features, and homography is estimated using the RANSAC algorithm to align the images. A smoothing filter is applied to create the final panorama. Experimental results show that ORB with HE is the most efficient technique, using 500 key features and achieving a runtime of 0.0836 seconds. The average runtime of the entire image mosaicing process, from feature extraction to panorama generation, was significantly lower for ORB than for SIFT (0.45 seconds) and BRISK (0.30 seconds). The minimal runtime and reduced computational cost of ORB make it ideal for applications requiring real-time image stitching, such as video processing or live panoramic imaging. The proposed model addresses limitations in feature matching and offers improved performance in image mosaicing. © 2024 IEEE.HistogramHomographyMosaicingNearest NeighborPanorama