COMPARING ROTATION-ROBUST MECHANISMS IN LOCAL FEATURE MATCHING: HAND-CRAFTED VS. DEEP LEARNING ALGORITHMS
Abstract
Keywords
Full Text:
PDFReferences
Ali, H. K., & Whitehead, A. (2014). Modern to Historic Image Matching: ORB/SURF an Effective Matching Technique. In Proceedings of International Conference on Computers and Their Application (CATA 2014).
Gupta, S., Chakarvarti, S. K., & Zaheeruddin. (2016). Medical image registration based on fuzzy c-means clustering segmentation approach using SURF. International Journal of Biomedical Engineering and Technology, 20(1), 33-50.
Kaucha, D. P., Prasad, P. W. C., Alsadoon, A., Elchouemi, A., & Sreedharan, S. (2017, September). Early detection of lung cancer using SVM classifier in biomedical image processing. In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) (pp. 3143-3148). IEEE.
Yang, W., Zhong, L., Chen, Y., Lin, L., Lu, Z., Liu, S., ... & Chen, W. (2018). Predicting CT image from MRI data through feature matching with learned nonlinear local descriptors. IEEE Transactions on Medical Imaging, 37(4), 977-987.
Song, F., Dan, T., Yu, R., Yang, K., Yang, Y., Chen, W., ... & Ong, S. H. (2019). Small UAV-based multi-temporal change detection for monitoring cultivated land cover changes in mountainous terrain. Remote Sensing Letters, 10(6), 573-582.
Shao, Z., Li, C., Li, D., Altan, O., Zhang, L., & Ding, L. (2020). An accurate matching method for projecting vector data into surveillance video to monitor and protect cultivated land. ISPRS International Journal of Geo-Information, 9(7), 448.
Sun, J., Shen, Z., Wang, Y., Bao, H., & Zhou, X. (2021). LoFTR: Detector-free local feature matching with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision And Pattern Recognition (pp. 8922-8931).
Huang, X., Wan, X., & Peng, D. (2020). Robust feature matching with spatial smoothness constraints. Remote Sensing, 12(19), 3158.
Bojanić, D., Bartol, K., Pribanić, T., Petković, T., Donoso, Y. D., & Mas, J. S. (2019, September). On the comparison of classic and deep keypoint detector and descriptor methods. In 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) (pp. 64-69). IEEE.
Karami, E., Prasad, S., & Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint arXiv:1710.02726.
Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010). Brief: Binary robust independent elementary features. In Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV 11 (pp. 778-792). Springer Berlin Heidelberg.
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011, November). ORB: An efficient alternative to SIFT or SURF. In 2011 International Conference on Computer Vision (pp. 2564-2571). IEEE.
Cai, L., Ye, Y., Gao, X., Li, Z., & Zhang, C. (2021). An improved visual SLAM based on affine transformation for ORB feature extraction. Optik, 227, 165421.
Alcantarilla, P. F., & Solutions, T. (2011). Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans. Patt. Anal. Mach. Intell, 34(7), 1281-1298.
Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011, November). BRISK: Binary robust invariant scalable keypoints. In 2011 International Conference on Computer Vision (pp. 2548-2555). IEEE.
Alahi, A., Ortiz, R., & Vandergheynst, P. (2012, June). Freak: Fast retina keypoint. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 510-517). IEEE.
Mohammad, A., Saleh, O., & Abdeen, R. A. (2006). Occurrences algorithm for string searching based on brute-force algorithm. Journal of Computer Science, 2(1), 82-85.
Pandey, A., & Jain, A. (2017). Comparative analysis of KNN algorithm using various normalization techniques. International Journal of Computer Network and Information Security, 11(11), 36.
Kuang, Q., & Zhao, L. (2009). A practical GPU based kNN algorithm. In Proceedings. The 2009 International Symposium on Computer Science and Computational Technology (ISCSCI 2009) (p. 151). Academy Publisher.
Muja, M., & Lowe, D. (2009). Flann-fast library for approximate nearest neighbors user manual. Computer Science Department, University of British Columbia, Vancouver, BC, Canada, 5, 6.
Megalingam, R. K., Sriteja, G., Kashyap, A., Apuroop, K. G. S., Gedala, V. V., & Badhyopadhyay, S. (2018). Performance Evaluation of SIFT & FLANN and HAAR Cascade Image Processing Algorithms for Object Identification in Robotic Applications. International Journal of Pure and Applied Mathematics, 118(18), 2605-2612.
George, J., & Raj, S. G. (2021). Leaf Identification using Harris Corner Detection, SURF Feature and FLANN Matcher. Int. J. Innov. Technol. Explor. Eng, 8(8).
Wang, S., Guo, Z., & Liu, Y. (2021, September). An image matching method based on sift feature extraction and FLANN search algorithm improvement. In Journal of Physics: Conference Series (Vol. 2037, No. 1, p. 012122). IOP Publishing.
Bolles, R. C., & Fischler, M. A. (1981, August). A RANSAC-based approach to model fitting and its application to finding cylinders in range data. In IJCAI (Vol. 1981, pp. 637-643).
Liu, J., & Bu, F. (2019). Improved RANSAC features image‐matching method based on SURF. The Journal of Engineering, 2019(23), 9118-9122.
Derpanis, K. G. (2010). Overview of the RANSAC Algorithm. Image Rochester NY, 4(1), 2-3.
Parihar, U. S., Gujarathi, A., Mehta, K., Tourani, S., Garg, S., Milford, M., & Krishna, K. M. (2021, September). RoRD: Rotation-robust descriptors and orthographic views for local feature matching. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1593-1600). IEEE.
Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., & Sattler, T. (2019). D2-net: A trainable cnn for joint description and detection of local features. In Proceedings of The Ieee/CVF Conference On Computer Vision And Pattern Recognition (pp. 8092-8101).
Revaud, J., Weinzaepfel, P., De Souza, C., Pion, N., Csurka, G., Cabon, Y., & Humenberger, M. (2019). R2D2: repeatable and reliable detector and descriptor. arXiv preprint arXiv:1906.06195.
Noh, H., Araujo, A., Sim, J., Weyand, T., & Han, B. (2017). Large-scale image retrieval with attentive deep local features. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3456-3465).
Tareen, S. A. K., & Saleem, Z. (2018, March). A comparative analysis of sift, surf, kaze, akaze, orb, and brisk. In 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-10). IEEE.
Bayraktar, E., & Boyraz, P. (2017). Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics. Turkish Journal of Electrical Engineering and Computer Sciences, 25(3), 2444-2454.
Ha, Y. S., Lee, J., & Kim, Y. T. (2022). Performance Evaluation of Feature Matching Techniques for Detecting Reinforced Soil Retaining Wall Displacement. Remote Sensing, 14(7), 1697.
Efe, U., Ince, K. G., & Alatan, A. (2021). Dfm: A performance baseline for deep feature matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4284-4293).
DOI: http://dx.doi.org/10.22441/ijimeam.v5i3.24794
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Aulia Rahman, Louis Gautama Lie, Haris Wahyudi, Fahri Heltha
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
INDEXED IN
Publisher Address:
Universitas Mercu Buana
Program Studi S2 Teknik Mesin
Jl. Meruya Selatan No. 1, Jakarta 11650, Indonesia
Phone/Fax. (+6221) 5871335
Email [email protected]
Homepage http://teknikmesin.ft.mercubuana.ac.id/
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.