Mirror Detection in Frequency Domain
Pages 327 - 331
Abstract
Mirrors often appear in various places, and personal privacy information will be reflected and leaked out without the user's awareness, affecting the security of personal information. Mirror detection is a very challenging task due to the non-uniform size of mirrors and the presence of reflections. This paper proposes a frequency-domain based mirror detection method. Aiming at the reflection phenomenon existing on the mirror surface, we first proposed a frequency domain feature extraction module (FEM), which maps the multi-scale features of the mirror to the frequency domain, extracts the mirror features in the frequency domain, and suppresses the interference caused by the reflection of objects outside the mirror. In addition, for the edge inconsistency problem of the mirror surface, we propose a cross-level fusion module (CLFM) based on reverse attention, which fuses features of different levels and enhances image edge information. The experimental results show the good effect of our model.
References
[1]
Wang, W., Xie, E., Li, X., Fan, D. P., Song, K., Liang, D., ... & Shao, L. 2022. Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media, 8(3), 415-424. 2021.
[2]
Yang, X., Mei, H., Xu, K., Wei, X., Yin, B., & Lau, R. W. 2019. Where is my mirror ?. In Proceedings of the IEEE/CVF International Conference on Computer Vision(pp.8809-8818). 2019.
[3]
Lin, J., Wang, G., & Lau, R. W. 2020. Progressive mirror detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3697-3705).
[4]
Guan, H., Lin, J., & Lau, R. W. 2022. Learning semantic associations for mirror detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5941-5950).
[5]
He, R., Lin, J., & Lau, R. W. 2023, June. Efficient Mirror Detection via Multi-Level Heterogeneous Learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 1, pp. 790-798).
[6]
Lin, S., Zhang, Z., Huang, Z., Lu, Y., Lan, C., Chu, P., ... & Chen, Z. 2023. Deep frequency filtering for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11797-11807).
[7]
Sun, Y., Wang, S., Chen, C., & Xiang, T. Z. 2022. Boundary-guided camouflaged object detection. arXiv preprint arXiv:2207.00794.
[8]
Chen, G., Shao, F., Chai, X., Chen, H., Jiang, Q., Meng, X., & Ho, Y. S. 2022. Modality-induced transfer-fusion network for RGB-D and RGB-T salient object detection. IEEE Transactions on Circuits and Systems for Video Technology, 33(4), 1787-1801.
[9]
Wei, J., Wang, S., & Huang, Q. 2020, April. F³Net: fusion, feedback and focus for salient object detection. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 07, pp. 12321-12328).
[10]
Wu, Z., Su, L., & Huang, Q. 2019. Cascaded partial decoder for fast and accurate salient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3907-3916).
[11]
Chen, Z., Xu, Q., Cong, R., & Huang, Q. 2020, April. Global context-aware progressive aggregation network for salient object detection. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 07, pp. 10599-10606).
[12]
Liu, N., Zhang, N., Wan, K., Shao, L., & Han, J. 2021). Visual saliency transformer. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4722-4732).
Index Terms
- Mirror Detection in Frequency Domain
Index terms have been assigned to the content through auto-classification.
Recommendations
GPU-based frequency domain volume rendering
SCCG '04: Proceedings of the 20th Spring Conference on Computer GraphicsFrequency domain volume rendering (FVR) is a volume rendering technique with lower computational complexity as compared to other techniques. In this paper the FVR algorithm is accelerated by factor of 17 by mapping the rendering stage to the GPU. The ...
Frequency domain normal map filtering
Filtering is critical for representing detail, such as color textures or normal maps, across a variety of scales. While MIP-mapping texture maps is commonplace, accurate normal map filtering remains a challenging problem because of nonlinearities in ...
A Theory Of Frequency Domain Invariants: Spherical Harmonic Identities for BRDF/Lighting Transfer and Image Consistency
This paper develops a theory of frequency domain invariants in computer vision. We derive novel identities using spherical harmonics, which are the angular frequency domain analog to common spatial domain invariants such as reflectance ratios. These ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2023
446 pages
ISBN:9798400716683
DOI:10.1145/3640912
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 February 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CNML 2023
CNML 2023: 2023 International Conference on Communication Network and Machine Learning
October 27 - 28, 2023
Zhengzhou, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 21Total Downloads
- Downloads (Last 12 months)21
- Downloads (Last 6 weeks)2
Reflects downloads up to 12 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format