Details
| Original language | English |
|---|---|
| Pages | 498-504 |
| Number of pages | 7 |
| Publication status | Published - 30 Aug 2017 |
| Event | 2017 18th International Conference on Advanced Robotics (ICAR) - Hong Kong Science and Technology Park, Hong Kong, China Duration: 10 Jul 2017 → 12 Jul 2017 |
Conference
| Conference | 2017 18th International Conference on Advanced Robotics (ICAR) |
|---|---|
| Abbreviated title | ICAR |
| Country/Territory | China |
| City | Hong Kong |
| Period | 10 Jul 2017 → 12 Jul 2017 |
Abstract
In this paper, an approach for identifying corresponding image features across different imaging modalities is presented. The method includes spatial alignment of sensor images on short and long distance as well as a probabilistic fusion approach for combining multiple unimodal to multimodal image features. An experimental statistical comparison of uni- and multimodal image features is performed using RGB, IR and thermal cameras. Therefore, the sensors are mounted on an Ackermann steering platform in a typical industrial environment. The multimodal features are examined regarding repetitive characteristics, quantity and spatial distribution.
Keywords
- Image Features, Multimodal Image Features, Sensor Fusion
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Control and Optimization
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2017. 498-504 Paper presented at 2017 18th International Conference on Advanced Robotics (ICAR), Hong Kong, China.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Probabilistic fusion and analysis of multimodal image features.
AU - Kleinschmidt, Sebastian P.
AU - Wagner, Bernardo
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions. Publisher Copyright: © 2017 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/8/30
Y1 - 2017/8/30
N2 - In this paper, an approach for identifying corresponding image features across different imaging modalities is presented. The method includes spatial alignment of sensor images on short and long distance as well as a probabilistic fusion approach for combining multiple unimodal to multimodal image features. An experimental statistical comparison of uni- and multimodal image features is performed using RGB, IR and thermal cameras. Therefore, the sensors are mounted on an Ackermann steering platform in a typical industrial environment. The multimodal features are examined regarding repetitive characteristics, quantity and spatial distribution.
AB - In this paper, an approach for identifying corresponding image features across different imaging modalities is presented. The method includes spatial alignment of sensor images on short and long distance as well as a probabilistic fusion approach for combining multiple unimodal to multimodal image features. An experimental statistical comparison of uni- and multimodal image features is performed using RGB, IR and thermal cameras. Therefore, the sensors are mounted on an Ackermann steering platform in a typical industrial environment. The multimodal features are examined regarding repetitive characteristics, quantity and spatial distribution.
KW - Image Features
KW - Multimodal Image Features
KW - Sensor Fusion
UR - http://www.scopus.com/inward/record.url?scp=85031682993&partnerID=8YFLogxK
U2 - 10.1109/icar.2017.8023656
DO - 10.1109/icar.2017.8023656
M3 - Paper
SP - 498
EP - 504
T2 - 2017 18th International Conference on Advanced Robotics (ICAR)
Y2 - 10 July 2017 through 12 July 2017
ER -