Vertex-voting-based polygonal object detection

Master thesis (2020)

Authors

K. Lang Electrical Engineering, Mathematics and Computer Science

Contributors

J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (mentor)
Y. Lin Pattern Recognition and Bioinformatics - EEMCS (mentor)
A Katsifodimos Web Information Systems - EEMCS (graduation committee member)
S. Pintea Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Kang Lang
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Published Date

02-11-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Although the pixel-wise labelling approaches have been exploited in depth and achieve good results in segmentation tasks, the grouped pixels are not ideal output for many end-users. In this paper, we propose a vertex-voting-based approach that can directly extract the polygon representations of objects. In order to better solve overlapping scenarios, we also propose a novel method that distinguishes objects by learning a virtual depth axis. When compared with the state-of-the-art method, our experiments demonstrate that this voting-based method is more robust to occlusion and shows a potential research direction.