| Abstract: | Object skeleton extraction is a fundamental task in computer vision and shape analysis,
which aims to generate a compact, single-pixel-width medial axis representation that preserves the
topological and geometric properties of the original 2D shape. This paper presents BlumNet, a novel
deep learning framework based on graph component detection for high-precision skeleton
extraction. Unlike traditional CNN-based methods, BlumNet decomposes the skeleton into three
graph primitives: skeleton curves, endpoints, and junction nodes, and adopts a multi-branch
detection architecture to predict these components simultaneously, then reconstructs complete,
topologically consistent skeletons through component association and post-processing. Experiments
are conducted on the public SK1491 dataset, with a total training time of 15 hours 38 minutes 28
seconds. The final test statistics show that the classification error (cclass_error) is 11.66 %, the
detection error (pclass_error) is 28.57 %, and the total loss is 2.3040. Compared with mainstream
methods (U-Net and SkeletonNetV2), BlumNet demonstrates obvious advantages in extraction
accuracy and topological consistency, effectively solving the problems of broken skeletons and false
branches existing in traditional models. |
