Details
| Original language | English |
|---|---|
| Title of host publication | Pattern Recognition |
| Subtitle of host publication | 39th German Conference, GCPR 2017, Proceedings |
| Publisher | Springer Verlag |
| Pages | 17-28 |
| Number of pages | 12 |
| ISBN (print) | 9783319667089 |
| Publication status | Published - 15 Aug 2017 |
| Event | 39th German Conference on Pattern Recognition, GCPR 2017 - Basel, Switzerland Duration: 12 Sept 2017 → 15 Sept 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10496 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Pattern Recognition: 39th German Conference, GCPR 2017, Proceedings. Springer Verlag, 2017. p. 17-28 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10496 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
AU - Kluger, Florian
AU - Ackermann, Hanno
AU - Yang, Michael Ying
AU - Rosenhahn, Bodo
PY - 2017/8/15
Y1 - 2017/8/15
N2 - We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.
AB - We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.
UR - http://www.scopus.com/inward/record.url?scp=85029596395&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66709-6_2
DO - 10.1007/978-3-319-66709-6_2
M3 - Conference contribution
AN - SCOPUS:85029596395
SN - 9783319667089
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 17
EP - 28
BT - Pattern Recognition
PB - Springer Verlag
T2 - 39th German Conference on Pattern Recognition, GCPR 2017
Y2 - 12 September 2017 through 15 September 2017
ER -