Research on Image Mosaic Method Based on Fracture Edge Contour of Bone Tag
Source abstract
Abstract
Damaged edges of bone tag images contain external factors such as impurities and damage, which affect the stitching process and lead to repair errors. Therefore, this paper proposes a stitching method based on image edge position feature matching. The objective is to improve the accuracy of image stitching by matching feature points based on the position of the image edge pixel so as to solve the accurate stitching of broken edge contour. In the first step of this method, the image containing the broken edge is preprocessed by edge detection, and the location of the broken edge pixel is proposed. Secondly, the feature descriptors were calculated to extract the shape and texture information of the feature points on the fracture edge. Finally, the feature points are optimized by minimum correction and image mosaic is carried out. In terms of image stitching, pre-registration is performed by finding the feature descriptors that are most similar to the edge of the optimum fracture surface profile. The matching operator is added to the overlapping region to obtain the corrected image, and the panoramic image mosaic of the image fracture surface is performed. The experimental results show that feature descriptor matching can ensure the integrity of the fracture, improve the matching accuracy, optimize the uneven deformation of the fracture, ensure the quality of image stitching, and reduce the degree of image distortion.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Image and Video Stabilization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
School of Science, Xi’an University of Architecture and Technology, Xi’an 710055, China
School of Information and Control Engineering, Xi′an University of Architecture and Technology, Xi’an 710055, China
Funding
National Science Foundation of China · Natural Science Basic Research Plan in Shaanxi Province of China
References (25)
Show source-provided references
1. Survey on image mosaic algorithm of unmanned aerial vehicle
2. Wavelet-based image fusion tutorial10.1016/j.patcog.2004.03.010 ↗
3. Combination of Sobel+Prewitt Edge Detection Method with Roberts+Canny on Passion Flower Image Identification10.1088/1742-6596/1933/1/012037 ↗
4. Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm10.1177/14759217211053546 ↗
5. (2022, December 28). Feryanto, Pengenalan Pola Berdasarkan Edge Detection Operator Sobel, Isotropic, Roberts, Prewitt, Kirsch Dengan Position Based Matching dan Backpropagation, Skripsi S-1 Teknik Informatika. Available online: https://www.semanticscholar.org/paper/Pengenalan-Pola-Berdasarkan-Edge-Detection-Operator-Feryanto/ef1518a40997547b4ac1454794709b63af750ab7.
6. A Computational Approach to Edge Detection10.1109/TPAMI.1986.4767851 ↗
7. He, F., and Ye, Q. (2022). A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm. Sensors, 22.10.20944/preprints202201.0068.v1 ↗
8. A combined corner and edge detector
9. BRIEF: Binary Robust Independent Elementary Features
10. Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6–13). ORB: An efficient alternative to SIFT or SURF. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.10.1109/ICCV.2011.6126544 ↗
11. Fragment Splicing Method Combined with Surface Texture and Fracture Contour
12. Lin, W.Y., Liu, S., Matsushita, Y., Ng, T.T., and Cheong, L.F. (2011, January 20–25). Smoothly varying affine stitching. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.10.1109/CVPR.2011.5995314 ↗
13. Chen, Y.S., and Chuang, Y.Y. (2016). Natural image stitching with the global similarity prior, In European Conference on Computer Vision, Springer.10.1007/978-3-319-46454-1_12 ↗
14. Multi-viewpoint panorama construction with widebaseline images10.1109/TIP.2016.2535225 ↗
15. Lin, K., Jiang, N.J., Liu, S.C., Cheong, L.F., Minh, D., and Lu, J.B. (2017, January 21–26). Direct photometric alignment by mesh deformation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.289 ↗
16. Quasi-homography warps in image stitching10.1109/TMM.2017.2771566 ↗
17. Image Stitching of the Target Object Based on Large Parallax Images
18. Image Binarization Processing Method Using -Medoids Clustering
19. Boundary Artifact Reduction in View Synthesis of 3D Video: From Perspective of Texture-Depth Alignment10.1109/TBC.2011.2120730 ↗
20. Feng, Y., and Li, S. (2018, January 9–11). Research on an Image Mosaic Algorithm Based on Improved ORB Feature Combined with SURF. Proceedings of the Chinese Control And Decision Conference, Shenyang, China.10.1109/CCDC.2018.8407963 ↗
21. Rosten, E., and Drummond, T. (2006). Machine learning for highspeed corner detection. European Conference on Computer Vision, Springer.10.1007/11744023_34 ↗
22. Affine Model for Generating Stereo Mosaic Image from Video Frames
23. Rankov, V., Locke, R.J., and Edens, R.J. (2005). An algorithm for image stitching and blending. Three-Dimensional and Multidimensional Microscopy, SPIE.10.1117/12.590536 ↗
24. Depth map misalignment correction and dilation for DIBR view synthesis
25. A medical big data access control model based on fuzzy trust prediction and regression analysis10.1016/j.asoc.2022.108423 ↗