Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Series Vol. 2 , 22 March 2023
* Author to whom correspondence should be addressed.
The existing method does not seem to be a killer application that combines image segmentation and inpainting to do image processing tasks for ordinary people. Therefore, we propose a region-based inpainting method, namely Remover. Remover is a method that can analyze the content of images, perform automatic image segmentation tasks with or without manual intervention, and inpainting the segmented part to achieve unwanted objects appearing to have been removed from an image without affecting the content of the image. With the help of open-source code and technical support. Remover stands as an Ubuntu desktop application. The code is under development and will be available at https://github.com/WPCJATH/remover soon.
Computer Vision, Machine Learning, Image Inpainting, Detectron2, Edge-Connect
1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
2. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
3. Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., ... & Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5), 1-36.
4. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architec-tures. IEEE Access, 7, 53040-53065.
5. Nazeri, K., Ng, E., Joseph, T., Qureshi, F. Z., & Ebrahimi, M. (2019, January 1). Edge-Connect: Generative image inpainting with adversarial edge learning. ArXiv.Org.
6. Xie, C., Liu, S., Li, C., Cheng, M.-M., Zuo, W., Liu, X., Wen, S., & Ding, E. (2019, September 3). Image inpainting with learnable bidirectional attention maps. ArXiv.Org.
7. Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., & Li, H. (2016, November 30). High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis. ArXiv.Org.
8. Deng, J., Dong, W., Socher, R., Li, L.-J., Kai Li, & Li Fei-Fei. (2009, June). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition.
9. Honda, H. (2022, January 18). Digging into Detectron 2 — part 1 - Hiroto Honda. Me-dium. https://medium.com/@hirotoschwert/digging-into-detectron-2-47b2e794fabd.
10. Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time ob-ject detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149.
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).