ProMask：用于骨架检测的概率掩码
ProMask: Probability Mask for Skeleton Detection
摘要：
由于自然图像中物体尺度的变化、背景的复杂性以及各种噪声的存在，使得物体骨架的检测具有挑战性。骨架是一种高度压缩的形状表示，它可以带来一些本质的优点，但也给检测带来了困难。这种骨架线占据了图像的一个罕见的比例，并且对空间位置过于敏感。受这些问题的启发，我们提出了ProMask，这是一种新的骨架检测模型。ProMask包括概率掩码和向量路由器。骨架概率掩码表示用分割信号显式地编码骨架，这可以提供更多的监督信息来学习并更多地关注地面真实骨架像素。此外，向量路由器模块具有两组在二维空间中正交的基向量，可以动态调整预测的骨架位置。我们在著名的骨架数据集上评估了我们的方法，实现了比最先进的方法更好的性能。特别是，ProMask在具有挑战性的SYM-PASCAL数据集上的表现明显优于竞争对手DeepFlux 6.2%。我们认为，我们提出的骨架概率掩模可以作为未来骨架检测的坚实基线，因为它是非常有效的，它需要大约10行代码。
英文摘要：
Detecting object skeletons in natural images presents challenging, due to varied object scales, the complexity of backgrounds and various noises. The skeleton is a highly compressing shape representation, which can bring some essential advantages but cause the difficulties of detection. This skeleton line occupies a rare proportion of an image and is overly sensitive to spatial position. Inspired by these issues, we propose the ProMask, which is a novel skeleton detection model. The ProMask includes the probability mask and vector router. The skeleton probability mask representation explicitly encodes skeletons with segmentation signals, which can provide more supervised information to learn and pay more attention to ground-truth skeleton pixels. Moreover, the vector router module possesses two sets of orthogonal basis vectors in a two-dimensional space, which can dynamically adjust the predicted skeleton position. We evaluate our method on the well-known skeleton datasets, realizing the better performance than state-of-the-art approaches. Especially, ProMask significantly outperforms the competitive DeepFlux by 6.2% on the challenging SYM-PASCAL dataset. We consider that our proposed skeleton probability mask could serve as a solid baseline for future skeleton detection, since it is very effective and it requires about 10 lines of code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
