Abstract
The objective of this study is to improve the robustness of homography estimation using deep learning for images superimposed with various types of disturbances. In conventional deep learning homography estimation, the original image and the image with perturbations and disturbances are simultaneously input to the model for estimation. The disadvantages of this approach are that the original image is affected by noise and the model itself is unclear. In this study, features are extracted separately for each of the two images, and a model is constructed based on ResNet using these features as input. In addition, when extracting the features of the perturbed and disturbed images, WaveCNet, which integrates the discrete wavelet transform into the CNN, is used to add pinpoint tolerance to the disturbance. The estimation accuracy of the homography matrix in the method proposed in this study shows improved accuracy in various noises. These results suggest that the proposed method is effective in reducing the effect of disturbance by extracting features robust to disturbance for each image.
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Yokono, M., Kamata, H. (2022). Improving the Accuracy of Homography Matrix Estimation for Disturbance Images Using Wavelet Integrated CNN. In: Fan, W., Zhang, L., Li, N., Song, X. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2022. Communications in Computer and Information Science, vol 1712. Springer, Singapore. https://doi.org/10.1007/978-981-19-9198-1_42
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DOI: https://doi.org/10.1007/978-981-19-9198-1_42
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