Analysis and Implementation of a Fast Corner Detector on Image Stitching in the Formation of Aerial Photogrammetry Images
DOI:
https://doi.org/10.21108/ijoict.v10i2.1031Keywords:
Image Stitching, FAST Corner Detector, SURF, RegistrationAbstract
Aerial Photogrammetry is one of the products from the fields of geography in taking the object, area, or phenomenon on the surface of the earth. Using kamera with a photographic recording process and by the help of a detector in the form of film. In the application, required proper technique in merging images of aerial photographs in order to gain a broader perspective. In this final project, stitching method used in order to merging the images. Image stitching is a method for combining multiple images with overlapping fields of view to produce a panoramic image or picture, the image has a wider viewing angle. Image stitching takes some of the features of the image as a reference in merging overlapping areas, it's named FAST corner detector. The results suggest that the FAST corner detector with a threshold n = 9 appropriate if used in image stitching process that resulted in three classes based on the panoramic image of the cross correlation results, which is the upper class (cc>0.9) on the image with stitching horizontal, diagonal and vertical, the medium class (0.8<cc<0.9) on the image with stitching involves rotation and the lower class (cc <0.8) on a stitched image with scale.
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[1] K. Dimitrov, I. Damyanov, D. Saliev and T. Valkovski, "Pasture Research Using Aerial Photography and Photogrammetry", in 2021 29th National Conference with International Participation (TELECOM), Sofia, Bulgaria, pp. 121-124, 2021 doi: 10.1109/TELECOM53156.2021.9659796.
[2] ArcGIS, "What is photogrammetry?", 2024. [Online]. Available: https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/introduction-to-ortho-mapping.htm. [Accessed: Dec. 30, 2024].
[3] N. Bruno, L. Perfetti, F. Fassi, and R. Roncella, "Photogrammetric survey of narrow spaces in cultural heritage: Comparison of two multi-camera approaches", International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 48, pp. 87-94, 2024.
[4] J. Shan, W. Jiang, Y. Huang, D. Yuan, and Y. Liu, "Unmanned Aerial Vehicle (UAV)-Based Pavement Image Stitching Without Occlusion, Crack Semantic Segmentation, and Quantification," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 11, pp. 17038-17053, Nov. 2024, doi: 10.1109/TITS.2024.3424525.
[5] Z. Wang and Z. Yang, "Review on image-stitching techniques”, Multimedia Systems, vol. 26, no. 4, pp. 413-430, 2020.
[6] R. Yazdi and H. Khotanlou, "Robust Corner Detector Based on Local Maximum and Minimum Differences", in 2024 10th International Conference on Web Research (ICWR), 2024.
[7] E. Rosten and T. Drummond, "Machine learning for high-speed corner detection", in 9th European Conference on Computer Vision, vol. 1, pp. 430-443, 2006.
[8] A. Riabko and Y. Averyanova, "Comparative analysis of SIFT and SURF methods for local feature detection in satellite imagery", 2024.
[9] G. Zhang and Y. Chen, "More informed random sample consensus", in 2020 8th International Conference on Control, Mechatronics and Automation (ICCMA), 2020, pp. 197-201, doi: 10.1109/ICCMA.2020.9271234.
[10] R. Lionnie, T. M. Kadarina, and M. Alaydrus, "Analisis Metode SIFT dan SURF untuk Sistem Pendeteksi Gambar Termanipulasi Penyerangan Copy-Move Forgery," in ComTech Universitas Mercubuana, 2018.
[11] S. A. K. Tareen and Z. Saleem, "A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK", in 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 2018, pp. 1-10, doi: 10.1109/ICOMET.2018.8346440.
[12] Y. Ai and J. Kan, "Image mosaicing based on improved optimal seam-cutting", IEEE Access, vol. 8, pp. 181526-181533, 2020.
[13] R. Szeliski, "Image Alignment and Stitching: A Tutorial", Microsoft Research Microsoft Corporation: Redmond, 2006.
[14] M. Cheon, S. J. Yoon, B. Kang, and J. Lee, "Perceptual image quality assessment with transformers", in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 433-442.
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