U-Net Based Convolutional Neural Network for Skeleton Extraction

Oleg Panichev, Alona Voloshyna; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Skeletonization is a process aimed to extract a line-like object shape representation, skeleton, which is of great interest for optical character recognition, shape-based object matching, recognition, biomedical image analysis, etc.. Existing methods for skeleton extraction are typically based on topological, morphological or distance transform and are known to be sensitive to the noise on the boundary and require post-processing procedure for redundant branches pruning. In this work, we introduce U-net based approach for direct skeleton extraction of the object within Pixel SkelNetOn - CVPR 2019 challenge, inspired by CNNs success in skeleton extraction from real images task. The main idea of our approach is to consistently edit a skeleton mask by feature propagation through different scale layers. It opposes final skeleton generation from different scale object shape representations as occurs in approaches with deep supervision for skeleton extraction from the real image. Our U-net based model showed 0.75 F1-score on the validation set and the ensemble of eight identical models, trained on different data subsets, got 0.7846 F1-score on the test data.