Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings |
| Untertitel | 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 381-388 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781479966820 |
| Publikationsstatus | Veröffentlicht - 19 Feb. 2015 |
| Veranstaltung | 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, USA / Vereinigte Staaten Dauer: 5 Jan. 2015 → 9 Jan. 2015 |
Abstract
Based on the development of image registration, sequence registration can be done by computing the transformations between consecutive frames. To take into account the accumulated error, global registration method is usually employed as a global error minimizing approach. However, in real surveillance applications, the visible sequence and infrared sequence may be taken at different times, or from different viewpoints, and may have different dynamic contents. Therefore, global registration is only an approximate estimation for two sequences, resulting in inferior local contents. In this paper we present a novel integrated global-to-local framework that addresses the problems of dynamic infrared and visible image sequence registration. We propose to maximize the sum of the mutual information of two sequences for the global homography estimation. Then, frame-to-frame registration is performed to estimate the per-frame local homography. Finally, a smoothing strategy is adopted to smooth the local homographies in the temporal domain to enforce temporal consistency. We evaluate our proposed framework by comparing it to the state-of-the art sequence registration algorithm. Our method achieves improved performance on the public benchmark dataset.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
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Proceedings: 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. S. 381-388 7045911.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A global-to-local framework for infrared and visible image sequence registration
AU - Yang, Michael Ying
AU - Qiang, Yu
AU - Rosenhahn, Bodo
PY - 2015/2/19
Y1 - 2015/2/19
N2 - Based on the development of image registration, sequence registration can be done by computing the transformations between consecutive frames. To take into account the accumulated error, global registration method is usually employed as a global error minimizing approach. However, in real surveillance applications, the visible sequence and infrared sequence may be taken at different times, or from different viewpoints, and may have different dynamic contents. Therefore, global registration is only an approximate estimation for two sequences, resulting in inferior local contents. In this paper we present a novel integrated global-to-local framework that addresses the problems of dynamic infrared and visible image sequence registration. We propose to maximize the sum of the mutual information of two sequences for the global homography estimation. Then, frame-to-frame registration is performed to estimate the per-frame local homography. Finally, a smoothing strategy is adopted to smooth the local homographies in the temporal domain to enforce temporal consistency. We evaluate our proposed framework by comparing it to the state-of-the art sequence registration algorithm. Our method achieves improved performance on the public benchmark dataset.
AB - Based on the development of image registration, sequence registration can be done by computing the transformations between consecutive frames. To take into account the accumulated error, global registration method is usually employed as a global error minimizing approach. However, in real surveillance applications, the visible sequence and infrared sequence may be taken at different times, or from different viewpoints, and may have different dynamic contents. Therefore, global registration is only an approximate estimation for two sequences, resulting in inferior local contents. In this paper we present a novel integrated global-to-local framework that addresses the problems of dynamic infrared and visible image sequence registration. We propose to maximize the sum of the mutual information of two sequences for the global homography estimation. Then, frame-to-frame registration is performed to estimate the per-frame local homography. Finally, a smoothing strategy is adopted to smooth the local homographies in the temporal domain to enforce temporal consistency. We evaluate our proposed framework by comparing it to the state-of-the art sequence registration algorithm. Our method achieves improved performance on the public benchmark dataset.
UR - http://www.scopus.com/inward/record.url?scp=84925456694&partnerID=8YFLogxK
U2 - 10.1109/wacv.2015.57
DO - 10.1109/wacv.2015.57
M3 - Conference contribution
AN - SCOPUS:84925456694
SP - 381
EP - 388
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
Y2 - 5 January 2015 through 9 January 2015
ER -