Bai Xiuxiu, Ye Lele, Zhu Jihua, Zhu Li, Komura Taku
IEEE Trans Image Process. 2019 Oct 7. doi: 10.1109/TIP.2019.2944560.
Robustly computing the skeletons of objects in natural images is difficult due to the large variations in shape boundaries and the large amount of noise in the images. Inspired by recent findings in neuroscience, we propose the Skeleton Filter, which is a novel model for skeleton extraction from natural images. The Skeleton Filter consists of a pair of oppositely oriented Gabor-like filters; by applying the Skeleton Filter in various orientations to an image at multiple resolutions and fusing the results, our system can robustly extract the skeleton even under highly noisy conditions. We evaluate the performance of our approach using challenging noisy text datasets and demonstrate that our pipeline realizes state-of-the-art performance for extracting the text skeleton. Moreover, the presence of Gabor filters in the human visual system and the simple architecture of the Skeleton Filter can help explain the strong capabilities of humans in perceiving skeletons of objects, even under dramatically noisy conditions.
由于自然图像中形状边界的巨大变化以及图像中大量的噪声，稳健地计算自然图像中物体的骨架是困难的。受神经科学最新研究结果的启发，我们提出了骨架滤波器，这是一种从自然图像中提取骨架的新型模型。骨架滤波器由一对方向相反的类Gabor滤波器组成；通过在多个分辨率下将骨架滤波器以各种方向应用于图像并融合结果，我们的系统即使在高噪声条件下也能稳健地提取骨架。我们使用具有挑战性的噪声文本数据集评估了我们方法的性能，并证明我们的流程在提取文本骨架方面实现了最先进的性能。此外，人类视觉系统中存在Gabor滤波器以及骨架滤波器的简单架构有助于解释人类即使在噪声极大的条件下也能感知物体骨架的强大能力。
