Tianxiang Liu
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.
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Index Terms
- Mirror Detection in Frequency Domain
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