Acknowledgement
이 논문은 2021년도 4단계 BK21 사업에 의하여 지원되었음.
References
- Y. M. Baek and D. H. Bae, "Automated model-based android gui testing using multi-level gui comparison criteria," in Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, pp.238-249, 2016.
- W. Yang, M. R. Prasad, and T. Xie, "A grey-box approach for automated GUI-model generation of mobile applications," in International Conference on Fundamental Approaches to Software Engineering, Springer, Berlin, Heidelberg, pp.250-265, 2013.
- M. Dusmanu, et al., "D2-net: A trainable cnn for joint detection and description of local features," arXiv preprint arXiv:1905.03561, 2019.
- K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
- Z. Wang, et al., "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, Vol.13, No.4, pp.600-612, 2004. https://doi.org/10.1109/TIP.2003.819861
- M. A. Fischler and R. C. Bolles, "Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, Vol.24, No.6, pp.381-395, 1981. https://doi.org/10.1145/358669.358692
- D. Barath, J. Matas, and J. Noskova, "Magsac: Marginalizing sample consensue," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.10197-10205, 2019.
- S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in International Conference on Machine Learning, PMLR, pp.448-456. 2015.