Image Stitching Quality Evaluation and Improvement Based on SIFT Features and RANSAC Algorithm

DOI

10.2991/978-94-6463-540-9_77How to use a DOI?

Keywords

Image Stitching Quality Evaluation; SIFT Features; RANSAC Algorithm; Matching Accuracy

Abstract

Due to factors such as perspective and lighting, traditional stitching such as perspective and lighting algorithms find it difficult to achieve high-quality stitching results. Therefore, how to effectively improve the image stitching effect has become a hot research topic. The traditional image stitching technology mainly uses the SIFT (Scale Invariant Feature Transform) algorithm. By extracting key points from the image and describing their features, the local structural information of the image is obtained, laying the foundation for the next step of feature matching. This article uses the RANSAC (Random Sample Consensus) algorithm to identify and remove outliers, improving matching accuracy and robustness. This article takes the consistency of overlapping areas, color consistency, clarity, and geometric conversion accuracy as evaluation criteria. Based on this, the performance and effectiveness of the algorithm are comprehensively evaluated, providing a reliable solution for its practical application. The average gradient of Experiment Method 1 (Standard SIFT-RANSAC image mosaic) is 20.5, the edge sharpness is 0.78, and the detail retention is 75%. This article combines the SIFT characteristics with the RANSAC algorithm to achieve better and more natural stitching results.

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  - Jinsong Shen
PY  - 2024
DA  - 2024/10/16
TI  - Image Stitching Quality Evaluation and Improvement Based on SIFT Features and RANSAC Algorithm
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 755
EP  - 766
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_77
DO  - 10.2991/978-94-6463-540-9_77
ID  - Shen2024
ER  -