Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Editors | Andreas Kerren, Christophe Hurter, Jose Braz |
| Volume | 5: VISAPP |
| Publisher | SCITEPRESS Digital Library |
| Publication date | 2019 |
| Pages | 135-142 |
| ISBN (Electronic) | 978-989-758-354-4 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 14th International Conference on Computer Vision Theory and Applications - Prague, Czech Republic Duration: 25. Feb 2019 → 27. Feb 2019 |
Conference
| Conference | 14th International Conference on Computer Vision Theory and Applications |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 25/02/2019 → 27/02/2019 |
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Keywords
- Machine Learning Technologies for Vision
- Shape Representation and Matching
- Features Extraction
- Object Detection and Localization
- Bayesian Optimization
- Object Detection
- Pose Estimation
- Machine Learning
- Optimization
- Feature Matching
Cite this
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Bayesian Optimization of 3D Feature Parameters for 6D Pose Estimation. / Hagelskjær, Frederik; Krüger, Norbert; Buch, Anders Glent.
Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. ed. / Andreas Kerren; Christophe Hurter; Jose Braz. Vol. 5: VISAPP SCITEPRESS Digital Library, 2019. p. 135-142.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Bayesian Optimization of 3D Feature Parameters for 6D Pose Estimation
AU - Hagelskjær, Frederik
AU - Krüger, Norbert
AU - Buch, Anders Glent
PY - 2019
Y1 - 2019
N2 - 6D pose estimation using local features has shown state-of-the-art performance for object recognition and pose estimation from 3D data in a number of benchmarks. However, this method requires extensive knowledge and elaborate parameter tuning to obtain optimal performances. In this paper, we propose an optimization method able to determine feature parameters automatically, providing improved point matches to a robust pose estimation algorithm. Using labeled data, our method measures the performance of the current parameter setting using a scoring function based on both true and false positive detections. Combined with a Bayesian optimization strategy, we achieve automatic tuning using few labeled examples. Experiments were performed on two recent RGB-D benchmark datasets. The results show significant improvements by tuning an existing algorithm, with state-of-art performance.
AB - 6D pose estimation using local features has shown state-of-the-art performance for object recognition and pose estimation from 3D data in a number of benchmarks. However, this method requires extensive knowledge and elaborate parameter tuning to obtain optimal performances. In this paper, we propose an optimization method able to determine feature parameters automatically, providing improved point matches to a robust pose estimation algorithm. Using labeled data, our method measures the performance of the current parameter setting using a scoring function based on both true and false positive detections. Combined with a Bayesian optimization strategy, we achieve automatic tuning using few labeled examples. Experiments were performed on two recent RGB-D benchmark datasets. The results show significant improvements by tuning an existing algorithm, with state-of-art performance.
KW - Machine Learning Technologies for Vision
KW - Shape Representation and Matching
KW - Features Extraction
KW - Object Detection and Localization
KW - Bayesian Optimization
KW - Object Detection
KW - Pose Estimation
KW - Machine Learning
KW - Optimization
KW - Feature Matching
U2 - 10.5220/0007568801350142
DO - 10.5220/0007568801350142
M3 - Article in proceedings
VL - 5: VISAPP
SP - 135
EP - 142
BT - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
A2 - Kerren, Andreas
A2 - Hurter, Christophe
A2 - Braz, Jose
PB - SCITEPRESS Digital Library
ER -