Large scale asset extraction for urban images

Lama Affara, Liangliang Nan, Bernard Ghanem, Peter Wonka

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

1 Scopus citations

Abstract

Object proposals are currently used for increasing the computational efficiency of object detection. We propose a novel adaptive pipeline for interleaving object proposals with object classification and use it as a formulation for asset detection. We first preprocess the images using a novel and efficient rectification technique. We then employ a particle filter approach to keep track of three priors, which guide proposed samples and get updated using classifier output. Tests performed on over 1000 urban images demonstrate that our rectification method is faster than existing methods without loss in quality, and that our interleaved proposal method outperforms current state-of-the-art. We further demonstrate that other methods can be improved by incorporating our interleaved proposals.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
PublisherSpringer Verlag
Pages437-452
Number of pages16
Volume9907 LNCS
ISBN (Print)9783319464862
DOIs
StatePublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9907 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Affara, L., Nan, L., Ghanem, B., & Wonka, P. (2016). Large scale asset extraction for urban images. In Computer Vision - 14th European Conference, ECCV 2016, Proceedings (Vol. 9907 LNCS, pp. 437-452). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9907 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46487-9_27