Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization
Due to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new meth...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/632 |
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| author | Zhaopeng Deng Shengzhi Song Shuangyang Han Zeqi Liu Qiang Wang Liuyang Jiang |
| author_facet | Zhaopeng Deng Shengzhi Song Shuangyang Han Zeqi Liu Qiang Wang Liuyang Jiang |
| author_sort | Zhaopeng Deng |
| collection | DOAJ |
| description | Due to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new method of stitching an unfolded borehole image, which uses the image generated from the video to construct a large-scale panorama. Firstly, the speeded-up robust feathers (SURF) algorithm is used to extract the image feature points and complete the rough matching. Then, the M-estimator sample consensus (MSAC) algorithm is introduced to remove the mismatched point pairs and obtain the homography matrix. Subsequently, we propose a local homography matrix offset optimization (LHOO) algorithm to obtain the optimal offset. Finally, the above process is cycled frame by frame, and the image sequence is continuously stitched to complete the construction of a cylindrical borehole panorama. The experimental results show that compared with those of the SIFT, Harris, ORB and SURF algorithms, the matching accuracy of our algorithm has been greatly improved. The final test is carried out on 225 consecutive video frames, and the panorama has a good visual effect, and the average time of each frame is 100 ms, which basically meets the requirements of the project. |
| first_indexed | 2024-03-09T11:17:55Z |
| format | Article |
| id | doaj.art-1febed7de1244488b8affbae1984ed51 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:17:55Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1febed7de1244488b8affbae1984ed512023-12-01T00:24:57ZengMDPI AGSensors1424-82202023-01-0123263210.3390/s23020632Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset OptimizationZhaopeng Deng0Shengzhi Song1Shuangyang Han2Zeqi Liu3Qiang Wang4Liuyang Jiang5College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, ChinaCollege of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, ChinaCollege of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, ChinaCollege of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, ChinaCollege of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, ChinaCollege of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, ChinaDue to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new method of stitching an unfolded borehole image, which uses the image generated from the video to construct a large-scale panorama. Firstly, the speeded-up robust feathers (SURF) algorithm is used to extract the image feature points and complete the rough matching. Then, the M-estimator sample consensus (MSAC) algorithm is introduced to remove the mismatched point pairs and obtain the homography matrix. Subsequently, we propose a local homography matrix offset optimization (LHOO) algorithm to obtain the optimal offset. Finally, the above process is cycled frame by frame, and the image sequence is continuously stitched to complete the construction of a cylindrical borehole panorama. The experimental results show that compared with those of the SIFT, Harris, ORB and SURF algorithms, the matching accuracy of our algorithm has been greatly improved. The final test is carried out on 225 consecutive video frames, and the panorama has a good visual effect, and the average time of each frame is 100 ms, which basically meets the requirements of the project.https://www.mdpi.com/1424-8220/23/2/632image stitchingimage matchinghomography matrixfeature detectionborehole panorama |
| spellingShingle | Zhaopeng Deng Shengzhi Song Shuangyang Han Zeqi Liu Qiang Wang Liuyang Jiang Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization Sensors image stitching image matching homography matrix feature detection borehole panorama |
| title | Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization |
| title_full | Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization |
| title_fullStr | Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization |
| title_full_unstemmed | Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization |
| title_short | Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization |
| title_sort | geological borehole video image stitching method based on local homography matrix offset optimization |
| topic | image stitching image matching homography matrix feature detection borehole panorama |
| url | https://www.mdpi.com/1424-8220/23/2/632 |
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