Abstract
Multiple model estimation is an important problem in computer vision. Through estimation, one can detect important structural information in an image. A crucial step in multiple model estimation is the ability to dichotomize inliers of a model from outliers. This paper proposes a novel technique for estimating the scale of a model. In contrast to previous adaptive scale estimate works, our method removes the need for user provided input. We achieve accurate scale estimation through consecutive inspection of the ordered residuals. Our results show the ability of the proposed scale estimate metric to maintain accurate scale estimation even with over 90% outliers present in the data. Likewise, we also apply our scale estimator with multiple model estimation problems for detecting planes and two-view motions, demonstrating the ability of our approach to accurately estimate scale in real application oriented scenarios.
Original language | English (US) |
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Title of host publication | Proceedings - International Conference on Image Processing, ICIP |
Publisher | IEEE Computer Society |
Pages | 3377-3381 |
Number of pages | 5 |
Volume | 2015-December |
ISBN (Print) | 9781479983391 |
DOIs | |
State | Published - Dec 9 2015 |
Event | IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: Sep 27 2015 → Sep 30 2015 |
Other
Other | IEEE International Conference on Image Processing, ICIP 2015 |
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Country/Territory | Canada |
City | Quebec City |
Period | 9/27/15 → 9/30/15 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing