@inproceedings{b48d84309ed84ec8b6c1a9280ac41eea,
title = "Linear Span Network for Object Skeleton Detection",
abstract = "Robust object skeleton detection requires to explore rich representative visual features and effective feature fusion strategies. In this paper, we first re-visit the implementation of HED, the essential principle of which can be ideally described with a linear reconstruction model. Hinted by this, we formalize a Linear Span framework, and propose Linear Span Network (LSN) which introduces Linear Span Units (LSUs) to minimizes the reconstruction error. LSN further utilizes subspace linear span besides the feature linear span to increase the independence of convolutional features and the efficiency of feature integration, which enhances the capability of fitting complex ground-truth. As a result, LSN can effectively suppress the cluttered backgrounds and reconstruct object skeletons. Experimental results validate the state-of-the-art performance of the proposed LSN.",
keywords = "Linear span framework, Linear span network, Linear span unit, Skeleton detection",
author = "Chang Liu and Wei Ke and Fei Qin and Qixiang Ye",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01216-8\_9",
language = "英语",
isbn = "9783030012151",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "136--151",
editor = "Martial Hebert and Yair Weiss and Vittorio Ferrari and Cristian Sminchisescu",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
}