A Fast UAV Aerial Image Mosaic Method Based on Improved KAZE
| Autor: | Yongfu Li, Kaibi Zhang, Hengqi Cui |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Retina
Matching (graph theory) Computer science business.industry 010401 analytical chemistry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-invariant feature transform 02 engineering and technology RANSAC 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Scale space medicine.anatomical_structure Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition Feature descriptor medicine Computer vision Artificial intelligence Noise (video) 0210 nano-technology business Aerial image |
| Zdroj: | 2019 Chinese Automation Congress (CAC). |
| DOI: | 10.1109/cac48633.2019.8997399 |
| Popis: | This paper presents a novel image mosaic method for the unmanned aerial vehicle (UAV) aerial image based on the improved KAZE algorithm. For practical implementation and cost efficiency, the matching speed of KAZE algorithm is not desirable and random sample consensus (RANSAC) algorithm takes plenty of time to screen the correct feature pairs. In addition, the UAV aerial image is susceptible to environmental and other factors, such as scale, noise and brightness change. To overcome these problems, the novel image mosaic method is proposed. In particular, the non-linear scale space is constructed using the fast-explicit diffusion (FED) algorithm Then, the feature points are described by the improved fast retina key-point (FREAK) feature descriptor. Further, we adopt the Hamming algorithm for a rough match of these feature points, and select the improved progressive sample consensus (PROSAC) algorithm to exact correct matches. Finally, the image is stitched by the weighted average algorithm Results from numerical experiments show that compared with the scale invariant feature transform (SIFT), speed-up robust features (SURF), oriented FAST and rotated BRIEF (ORB) it as well as the KAZE algorithm, the proposed algorithm has better performance on feature extraction speed, feature matching speed, as well as matching accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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