Abstract:
Image stitching, also known as mosaicing, is considered a current area of research in computer
vision. The goal of image stitching is to create a high-resolution panoramic image by combining
two or more photos of the same scene. Feature-based techniques try to create a relationship
between the photographs using distinctive features derived from pictures, Feature-based
techniques can automatically identify associations between an unordered set of overlapping
photos. Within the subject of computer vision, image fusion is considered a current study topic.
It has many different algorithms for feature detection and description. This project provided a
technique for building a seamless image panorama that uses feature extraction methods to extract
visual features. This project compares various feature detection techniques, such as SIFT, ORB,
and BRISK. Using feature extractor algorithms, numerous features from both images are
extracted. Next, compare the features in both images and stay with the more compatible pairs.
For functional matching — KNN (K-Nearest Neighbors) Matcher and BF (Brute Force) Matcher
are used and these matchers are examined. The homography was computed using RANSAC
algorithm, which stands for Random Sampling Consensus.
