Large Scale Asset Extraction for Urban Images

Lama Ahmed Affara, Liangliang Nan, Bernard Ghanem, Peter Wonka

Research output: Chapter in Book/Report/Conference proceedingChapter

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. © Springer International Publishing AG 2016.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science
PublisherSpringer Nature
Pages437-452
Number of pages16
ISBN (Print)9783319464862
DOIs
StatePublished - Sep 17 2016

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