Feature Hourglass Network for Skeleton Detection

Nan Jiang, Yifei Zhang, Dezhao Luo, Chang Liu, Yu Zhou, Zhenjun Han

Abstract: Geometric shape understanding provides an intuitive representation of object shapes. Skeleton is typical geometrical information. Lots of traditional approaches are developed for skeleton extraction and pruning, while it is still a new area to investigate deep learning for geometric shape understanding. In this paper, we build a fully convolutional network named Feature Hourglass Network (FHN) for skeleton detection. FHN uses rich features of a fully convolutional network by hierarchically integrating side-outputs with a deep-to-shallow manner to decrease the residual between the prediction result and the ground-truth. Experiment data shows that FHN achieves better performance compared with baseline on both Pixel SkelNetOn and Point SkelNetOn datasets.