Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching.
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| Název: | Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching. |
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| Autoři: | Liu, Yan, Tian, Jiawei, Hu, Rongrong, Yang, Bo, Liu, Shan, Yin, Lirong, Zheng, Wenfeng |
| Zdroj: | Frontiers in Neurorobotics; 2/15/2022, Vol. 16, p1-14, 14p |
| Témata: | ALGORITHMS, PROBLEM solving, STATISTICAL sampling |
| Abstrakt: | Endoscopic imaging plays a very important role in the diagnosis and treatment of lesions. However, the imaging range of endoscopes is small, which may affect the doctors' judgment on the scope and details of lesions. Image mosaic technology can solve the problem well. In this paper, an improved feature-point pair purification algorithm based on SIFT (Scale invariant feature transform) is proposed. Firstly, the K-nearest neighbor-based feature point matching algorithm is used for rough matching. Then RANSAC (Random Sample Consensus) method is used for robustness tests to eliminate mismatched point pairs. The mismatching rate is greatly reduced by combining the two methods. Then, the image transformation matrix is estimated, and the image is determined. The seamless mosaic of endoscopic images is completed by matching the relationship. Finally, the proposed algorithm is verified by real endoscopic image and has a good effect. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Biomedical Index |
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