Drone Image Stitching Using Local Least Square Alignment
| Autor: | Jun Chen, Donghai Guo, Yong Wang, Qi Wan, Linbo Luo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Similarity (geometry)
business.industry Distortion (optics) Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 02 engineering and technology Drone Image stitching Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Parallax business 021101 geological & geomatics engineering Homography (computer vision) |
| Zdroj: | IGARSS |
| Popis: | This paper proposes a strategy for drone image stitching using local least square alignment, which aims to effectively stitch multiple overlapping drone images into a natural panoramic image. Existing traditional methods using simple homography cannot handle the situation that the input drone images have parallax effect, and the mosaic result always suffers from artifacts. In order to achieve natural-looking stitching results without the above limitation, we divide the proposed method into the following two steps, namely, local least square alignment and global similarity constraint. Starting from initial feature sets obtained by traditional feature extraction methods, we construct a robust alignment energy based on parallax errors to adaptively eliminate parallax effects. The energy can be efficiently minimized used least square estimate. Combined with global similarity constraint, our proposed strategy can flexibly improve the naturalness of the results. Experiments show that our stitching strategy can more effectively eliminate parallax effects and achieve natural-looking results compared to other state-of-the-art methods. |
| Databáze: | OpenAIRE |
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