Abstract
The usage of feature detectors for image stitching has become a popular research area in computer vision. Various feature extraction algorithms can be used in the process of image stitching process, but they perform varyingly when handling different images and no single algorithm could outperform all others. This paper focuses on the comparison of feature extraction algorithms used for panoramic image stitching. The research utilizes the SIFT, ORB, AKAZE, and BRISK to conduct feature points and match feature points on a group of image sets. The RANSAC algorithm is then used to filter out the outliers and calculate the homograph matrix. Completes the panoramic with image splicing and smoothing through the matrix transformation. Derived from the comparison of the matching and stitching results, the AKAZE detector is found to be the fastest feature point detection and extraction algorithm, while the SIFT detector will provide more feature points to make more accurate matches possible. These findings have implications for the development of efficient and effective computer vision technologies for various applications.
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