Distance and Edge Transform for Skeleton Extraction

Xiaojun Tang, Rui Zheng, Yinghao Wang

Abstract: The shape skeleton or medial axis, is a concise shape description, defined by the centers of the maximally inscribed circles. Skeletonization algorithms support many applications, including optical character recognition, object recognition, pose estimation, shape matching, biomedical image analysis, etc. Usually, classical algorithms tend to produce redundant skeleton branches at edge noise regions and require a branch pruning post process. Recently many CNN based algorithms achieved significant performance improvements compared with classical algorithms. Most deep learning algorithms directly used the shape of image as input data and its complex for end-to-end learning algorithms to fit the transformation from shape to skeleton. In this work, we proposed to use Smooth Distance Estimation (SDE) and Edge transformation to preprocess the input shape. Combined with a modified U-Net model and multimodel ensemble, the proposed method achieved 0.8129 F1 score in the Pixel SkelNetOn validation set, 1.5752 symmetric chamfer distance in the Point SkelNetOn validation set and 6407.4 squared distance score in the Parametric SkelNetOn validation set.