NDLI: A Minimum Error Vanishing Point Detection Approach for Uncalibrated Monocular Images of Man-Made Environments

Content Provider	IEEE Xplore Digital Library
Author	Yiliang Xu Sangmin Oh Hoogs, A.
Copyright Year	2013
Description	Author affiliation: Kitware Inc., Clifton Park, NY, USA (Yiliang Xu; Sangmin Oh; Hoogs, A.)
Abstract	We present a novel vanishing point detection algorithm for uncalibrated monocular images of man-made environments. We advance the state-of-the-art by a new model of measurement error in the line segment extraction and minimizing its impact on the vanishing point estimation. Our contribution is twofold: 1) Beyond existing hand-crafted models, we formally derive a novel consistency measure, which captures the stochastic nature of the correlation between line segments and vanishing points due to the measurement error, and use this new consistency measure to improve the line segment clustering. 2) We propose a novel minimum error vanishing point estimation approach by optimally weighing the contribution of each line segment pair in the cluster towards the vanishing point estimation. Unlike existing works, our algorithm provides an optimal solution that minimizes the uncertainty of the vanishing point in terms of the trace of its covariance, in a closed-form. We test our algorithm and compare it with the state-of-the-art on two public datasets: York Urban Dataset and Eurasian Cities Dataset. The experiments show that our approach outperforms the state-of-the-art.
Starting Page	1376
Ending Page	1383
File Size	1206585
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Estimation Measurement uncertainty Cameras Measurement errors Clustering algorithms Uncertainty
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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