Comparison and Application of Implementing Image Homographs in Computer Vision

DOI

10.2991/978-94-6463-540-9_79How to use a DOI?

Keywords

Feature Detect Algorithms; Deep Learning; Panorama; Homograph

Abstract

In the field of computer vision, planar homography plays a pivotal role in our research process. The homography matrix is capable of performing a variety of functions such as image warping, stitching, and video stitching. Within the realm of epipolar-geometry, it enables the execution of numerous tasks, including 3D reconstruction. This paper primarily focuses on the creation of panoramic images through automatic stitching of photographs with using homographic matrix, comparing the efficacy and efficiency of different feature extraction algorithms in terms of feature point matching, like Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), and KAZE Scale-Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and put forward some applications. Consequently, this leads to variations in the effectiveness and efficiency of images stitched using the homography matrix. This paper finished a feature matching experiment based on comparing the panoramic image with using different feature detect algorithms. For scenarios requiring high accuracy where processing time can be longer, SIFT, KAZE, or SURF might be better choices. On the other hand, for applications that need fast response, FAST or ORB would be more appropriate.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xingqi Qiu
PY  - 2024
DA  - 2024/10/16
TI  - Comparison and Application of Implementing Image Homographs in Computer Vision
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 779
EP  - 792
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_79
DO  - 10.2991/978-94-6463-540-9_79
ID  - Qiu2024
ER  -