Skeletonization Combined with Deep Neural Networks for Superpixel Temporal Propagation

Adam Fodor, Aron Fothi, Laszlo Kopacsi, Ellak Somfai, Andras Lorincz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Medial axis representation (a.k.a. shape skeleton) seems to be present in visual processing, but its relevance has remained unclear. Here, we show the potentials of the medial axis transformation in the temporal propagation of superpixels. We combine (i) state-of-the-art deep neural network 'sensors' for optical flow and for depth estimation and (ii) a superpixel algorithm with (iii) the medial axis transformation to obtain frame-to-frame propagation of visual objects. We study the precision of this deep learning facilitated superpixel temporal propagation. We discuss the advantages of the method compared to the temporal propagation of the superpixels themselves.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: Jul 14 2019Jul 19 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period7/14/197/19/19

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Keywords

  • Deep networks
  • depth estimation
  • medial axis
  • optical flow
  • superpixel
  • temporal propagation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Fodor, A., Fothi, A., Kopacsi, L., Somfai, E., & Lorincz, A. (2019). Skeletonization Combined with Deep Neural Networks for Superpixel Temporal Propagation. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852391] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852391