Glass largely blurs the boundary between the real world and the reflection. The special transmittance and reflectance quality have confused the semantic tasks related to machine vision. Therefore, how to clear the boundary built by glass, and avoid over-capturing features as false positive information in deep structure, matters for constraining the segmentation of reflection surface and penetrating glass. We proposed the Fourier Boundary Features Network with Wider Catchers (FBWC), which might be the first attempt to utilize sufficiently wide horizontal shallow branches without vertical deepening for guiding the fine granularity segmentation boundary through primary glass semantic information. Specifically, we designed the Wider Coarse-Catchers (WCC) for anchoring large area segmentation and reducing excessive extraction from a structural perspective. We embed fine-grained features by Cross Transpose Attention (CTA), which is introduced to avoid the incomplete area within the boundary caused by reflection noise. For excavating glass features and balancing high-low layers context, a learnable Fourier Convolution Controller (FCC) is proposed to regulate information integration robustly. The proposed method has been validated on three different public glass segmentation datasets. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art (SOTA) methods in glass image segmentation.

翻译：玻璃在很大程度上模糊了真实世界与反射之间的边界。其特殊的透射与反射特性对机器视觉相关的语义任务造成了困扰。因此，如何廓清玻璃构筑的边界，并在深层结构中避免因过度捕捉特征而产生虚假阳性信息，对于约束反射表面与穿透性玻璃的分割至关重要。我们提出了具有更宽捕捉器的傅里叶边界特征网络（FBWC），这可能是首个尝试利用充分宽的浅层水平分支（而非垂直加深）通过初级玻璃语义信息引导细粒度边界分割的方法。具体而言，我们设计了宽域粗粒度捕捉器（WCC），用于锚定大面积分割区域并从结构层面减少过度提取。我们通过交叉转置注意力机制（CTA）嵌入细粒度特征，旨在避免由反射噪声导致的边界内部不完整区域。为挖掘玻璃特征并平衡高低层上下文，我们提出了一种可学习的傅里叶卷积控制器（FCC），以稳健地调控信息整合。该方法已在三个不同的公开玻璃分割数据集上得到验证。实验结果表明，与当前最先进（SOTA）方法相比，所提方法在玻璃图像分割中取得了更优的分割性能。