NONE 20260430091210293 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON COMPUTERS 1109-2750 2224-2872 Leila Essannouni LASTIMI Laboratory, Higher School of Technology of Salé, Mohammed V University, Rabat, MOROCCO Manal Taoufiki Maisonneuve College, Montreal, CANADA Fedwa Essannouni LRIT Laboratory, Faculty of Sciences, Mohammed V University, Rabat, MOROCCO This paper presents a fast and robust image matching approach based on the cosine M-estimator kernel and the Fast Fourier Transform (FFT). We show that the robust cosine M-estimator can be effectively used to compare image intensities through correlation of transformed images. The speed of the method derives from the use of FFT to compute correlation. Its computational complexity is O (N log N), compared to O(N²) for direct matching. Experimental results demonstrate that the proposed method maintains high matching accuracy even in the presence of up to 60% outliers and 60% occlusion. It outperforms traditional approaches such as Sum of Squared Differences (SSD) and Normalized Cross-Correlation (NCC). Moreover, the proposed approach achieves superior performance compared to recent deep learning-based methods such as LoFTR. By combining the cosine M-estimator with FFT, the method is suitable for real-time applications that require robust matching under challenging conditions. 12 31 2025 12 31 2025 287 30 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23205.2025.24.30 https://wseas.com/journals/computers/2025/a605105-029(2025).pdf Verykokou, S., and Ioannidis, C., "Image Matching: A Comprehensive Overview of Conventional and Learning-Based Methods", Encyclopedia, Vol. 5, No. 1, Article 4, 2025. https://doi.org/10.3390/encyclopedia501000 4 Cui, Z., Qi, W., and Liu, Y., "A Fast Image Template Matching Algorithm Based on Normalized Cross Correlation", Journal of Physics: Conference Series, Vol. 1693, Article 012163, Bristol, UK, 2020. https://doi.org/10.1088/1742- 6596/1693/1/012163 Sun, J., Shen, Z., Wang, Y., Bao, H., and Zhou, X., "LoFTR: Detector-Free Local Feature Matching with Transformers ", in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), IEEE, 2021, pp. 8918–8927. https://doi.org/10.1109/CVPR46437.2021.0 0881 Luo, C., Yang, W., Huang, P., and Zhou, J., "Overview of Image Matching Based on ORB Algorithm", Journal of Physics: Conference Series, Vol. 1237, Article 032020, Bristol, UK, 2019. https://doi.org/10.1088/1742- 6596/1237/3/032020 Kaso, A., "Computation of the Normalized Cross-Correlation by Fast Fourier Transform", PLoS ONE, Vol. 13, No. 8, e0203434, 2018. https://doi.org/10.1371/journal.pone.020343 4 Sarlin, P.-E., DeTone, D., Malisiewicz, T., and Rabinovich, A., "SuperGlue: Learning Feature Matching with Graph Neural Networks", in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR, Seattle, WA, USA, pp. 4938–4947, 2020. https://doi.org/10.1109/CVPR42600.2020.0 0499 Ayubi, G. A., Kowalski, B., and Dubra, A., "Normalized weighted cross correlation for multi-channel image registration", Optics Continuum, Vol. 3, No. 5, pp. 649–665, 2024. https://doi.org/10.1364/OPTCON.525065 Öfverstedt, J., Lindblad, J., and Sladoje, N., "Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment", Pattern Recognition Letters, Vol. 159,pp. 1–8, 2022. https://doi.org/10.1016/j.patrec.2022.05.022 Tombari, F., Di Stefano, L., Mattoccia, S., and Galanti, A., "Performance evaluation of robust matching measures", in Proceedings of the Third International Conference on Computer Vision Theory and Applications VISAPP, Funchal, Madeira, Portugal, Vol. 1, pp. 473–478, 2008. https://doi.org/10.5220/0001087304730478 Fitch, A., Kadyrov, A., Christmas, W., and Kittler, J., "Fast Exhaustive Robust Matching", in Proceedings of the 16th International Conference on Pattern Recognition (ICPR 2002), IEEE, 2002, pp. 903–906. https://doi.org/10.1109/ICPR.2002.1048178 Balntas, V., Lenc, K., Vedaldi, A., and Mikolajczyk, K., "HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors", in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR, Honolulu, HI, USA, 2017.