IEICE TRANSACTIONS on Information
Kernel Based Image Registration Incorporating with Both Feature and Intensity Matching
Summary :
Image sequence registration has attracted increasing attention due to its significance in image processing and computer vision. In this paper, we put forward a new kernel based image registration approach, combining both feature-based and intensity-based methods. The proposed algorithm consists of two steps. The first step utilizes feature points to roughly estimate a motion parameter between successive frames; the second step applies our kernel based idea to align all the frames to the reference frame (typically the first frame). Experimental results using both synthetic and real image sequences demonstrate that our approach can automatically register all the image frames and be robust against illumination change, occlusion and image noise.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.5 pp.1317-1320
- Publication Date
- 2010/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.1317
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Keyword
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Quan MIAO, Guijin WANG, Xinggang LIN, "Kernel Based Image Registration Incorporating with Both Feature and Intensity Matching" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 5, pp. 1317-1320, May 2010, doi: 10.1587/transinf.E93.D.1317.
Abstract: Image sequence registration has attracted increasing attention due to its significance in image processing and computer vision. In this paper, we put forward a new kernel based image registration approach, combining both feature-based and intensity-based methods. The proposed algorithm consists of two steps. The first step utilizes feature points to roughly estimate a motion parameter between successive frames; the second step applies our kernel based idea to align all the frames to the reference frame (typically the first frame). Experimental results using both synthetic and real image sequences demonstrate that our approach can automatically register all the image frames and be robust against illumination change, occlusion and image noise.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1317/_p
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@ARTICLE{e93-d_5_1317,
author={Quan MIAO, Guijin WANG, Xinggang LIN, },
journal={IEICE TRANSACTIONS on Information},
title={Kernel Based Image Registration Incorporating with Both Feature and Intensity Matching},
year={2010},
volume={E93-D},
number={5},
pages={1317-1320},
abstract={Image sequence registration has attracted increasing attention due to its significance in image processing and computer vision. In this paper, we put forward a new kernel based image registration approach, combining both feature-based and intensity-based methods. The proposed algorithm consists of two steps. The first step utilizes feature points to roughly estimate a motion parameter between successive frames; the second step applies our kernel based idea to align all the frames to the reference frame (typically the first frame). Experimental results using both synthetic and real image sequences demonstrate that our approach can automatically register all the image frames and be robust against illumination change, occlusion and image noise.},
keywords={},
doi={10.1587/transinf.E93.D.1317},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Kernel Based Image Registration Incorporating with Both Feature and Intensity Matching
T2 - IEICE TRANSACTIONS on Information
SP - 1317
EP - 1320
AU - Quan MIAO
AU - Guijin WANG
AU - Xinggang LIN
PY - 2010
DO - 10.1587/transinf.E93.D.1317
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2010
AB - Image sequence registration has attracted increasing attention due to its significance in image processing and computer vision. In this paper, we put forward a new kernel based image registration approach, combining both feature-based and intensity-based methods. The proposed algorithm consists of two steps. The first step utilizes feature points to roughly estimate a motion parameter between successive frames; the second step applies our kernel based idea to align all the frames to the reference frame (typically the first frame). Experimental results using both synthetic and real image sequences demonstrate that our approach can automatically register all the image frames and be robust against illumination change, occlusion and image noise.
ER -
English
