The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이미지 시퀀스 등록은 이미지 처리 및 컴퓨터 비전에서의 중요성으로 인해 점점 더 많은 관심을 받고 있습니다. 본 논문에서는 특징 기반 방법과 강도 기반 방법을 결합한 새로운 커널 기반 이미지 등록 접근 방식을 제시합니다. 제안하는 알고리즘은 두 단계로 구성된다. 첫 번째 단계에서는 특징점을 활용하여 연속 프레임 간의 모션 매개변수를 대략적으로 추정합니다. 두 번째 단계에서는 커널 기반 아이디어를 적용하여 모든 프레임을 참조 프레임(일반적으로 첫 번째 프레임)에 정렬합니다. 합성 이미지 시퀀스와 실제 이미지 시퀀스를 모두 사용한 실험 결과는 우리의 접근 방식이 모든 이미지 프레임을 자동으로 등록하고 조명 변화, 폐색 및 이미지 노이즈에 대해 견고하다는 것을 보여줍니다.
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부
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
부
@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},}
부
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 -