Horizon lines in the wild

Scott Workman, Menghua Zhai, Nathan Jacobs

Research output: Contribution to conferencePaperpeer-review

37 Scopus citations

Abstract

The horizon line is an important contextual attribute for a wide variety of image understanding tasks. As such, many methods have been proposed to estimate its location from a single image. These methods typically require the image to contain specific cues, such as vanishing points, coplanar circles, and regular textures, thus limiting their real-world applicability. We introduce a large, realistic evaluation dataset, Horizon Lines in the Wild (HLW), containing natural images with labeled horizon lines. Using this dataset, we investigate the application of convolutional neural networks for directly estimating the horizon line, without requiring any explicit geometric constraints or other special cues. An extensive evaluation shows that using our CNNs, either in isolation or in conjunction with a previous geometric approach, we achieve state-of-the-art results on the challenging HLW dataset and two existing benchmark datasets.

Original languageEnglish
Pages20.1-20.12
DOIs
StatePublished - 2016
Event27th British Machine Vision Conference, BMVC 2016 - York, United Kingdom
Duration: Sep 19 2016Sep 22 2016

Conference

Conference27th British Machine Vision Conference, BMVC 2016
Country/TerritoryUnited Kingdom
CityYork
Period9/19/169/22/16

Bibliographical note

Publisher Copyright:
© 2016. The copyright of this document resides with its authors.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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