多探测器拼接成像系统实时图像配准

王新华; 黄玮; 欧阳继红

doi:10.3788/CO.20150802.0211

Volume 8 Issue 2

Apr. 2015

Turn off MathJax

Article Contents

Chinese Optics > 2015 > 8(2): 211-219.

WANG Xin-hua, HUANG Wei, OUYANG Ji-hong. Real-time image registration of the multi-detectors mosaic imaging system[J]. Chinese Optics, 2015, 8(2): 211-219. doi: 10.3788/CO.20150802.0211

Citation:

WANG Xin-hua, HUANG Wei, OUYANG Ji-hong. Real-time image registration of the multi-detectors mosaic imaging system[J]. Chinese Optics, 2015, 8(2): 211-219. doi: 10.3788/CO.20150802.0211

WANG Xin-hua, HUANG Wei, OUYANG Ji-hong. Real-time image registration of the multi-detectors mosaic imaging system[J]. Chinese Optics, 2015, 8(2): 211-219. doi: 10.3788/CO.20150802.0211

Citation:

WANG Xin-hua, HUANG Wei, OUYANG Ji-hong. Real-time image registration of the multi-detectors mosaic imaging system[J]. Chinese Optics, 2015, 8(2): 211-219. doi: 10.3788/CO.20150802.0211

PDF( 1245 KB)

Real-time image registration of the multi-detectors mosaic imaging system

doi: 10.3788/CO.20150802.0211

1.
College of Computer Science and Technology, Jilin University, Changchun 130012, China;
2.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

Received Date: 21 Nov 2014
Accepted Date: 23 Feb 2015
Publish Date: 25 Apr 2015

Abstract

Abstract

According to the detector arrays mosaic imaging system designed with four lenses based on concentric spherical lens, its applied algorithms about the image registration is investigated, such as feature detection and extraction, feature vector matching and screening, spatial transformation model and parameter estimation, <em>etc</em>. First, the fast-hessian detection algorithm is used to find features, and generate feature vector of SURF descriptors. Second, the fast approximate nearest neighbor search algorithm is used to obtain the initial matching points and to sort the Euclidean distance between feature vectors in the matching points. Then after screening the feature points, the good ones are preserved based on a reasonable threshold interval from the optical design parameters. Finally, the transform parameters are estimated by using the improved progressive sample consensus method and the spatial geometry transformation relationship is obtained about the reference image and registration image. Experimental results indicate that the algorithm has some invariance about the size, rotation and illumination changes; the feature matching time is 0.542 s, and the registration transform time is 0.031 s; the registration error precision is less than 0.1 pixel, which can meet the requirements of the imaging system about the image registration including good real-time and accuracy performance.
- detector array,
- image mosaic,
- image registration

FullText(HTML)

References(12)

References

[1]	[1] MARKS D L,LLULL P R,et al.. Characterization of the AWARE 10 two-gigapixel wide-field-of-view visible imager[J]. Applied Optics,2014,53(13):54-63.
[2]	[2] 史光辉,杨威.用于图像拼接的电视摄像光学系统[J].中国光学,2014,7(4):638-645. SHI G H,YANG W. Optical system used to compose images in television photograph[J]. Chinese Optics,2014,7(4):638-645.(in Chinese)
[3]	[3] 杨磊,曹剑中,唐利孬,等.全景图像自动拼接算法的优化设计[J].红外与激光工程,2014,43(3):985-990. YANG L,CAO J Z,TANG L A,et al.. Optimized design of automatic panoramic images mosaic[J]. Infrared and Laser Engineering,2014,43(3):985-990.(in Chinese)
[4]	[4] 王志社,杨风暴,纪利娥,等.基于聚类分割和形态学的可见光与SAR图像配准[J].光学学报,2014,34(2):0215002-1-7. WANG Z X,YANG F B,JI L E,et al.. Optical and SAR image registration based on cluster segmentation[J]. Acta Optica Sinica,2014,34(2):0215002-1-7.(in Chinese)
[5]	[5] 闫钧华,杭谊青,许俊峰,等.基于CUDA的高分辨率数字视频图像配准快速实现[J].仪器仪表学报,2014,35(2):380-386. YANG J H,HANG Y Q,XU J F,et al.. Quick realization of CUDA-based registration of high-resolution digital video images[J]. Chinese J. Scientific Instrument,2014,35(2):380-386.(in Chinese)
[6]	[6] LOWE D G. Distinctive image features from scale-invariant Key-points[J]. International J. Computer Vision,2004,60(2):91-110.
[7]	[7] PANCHAL P M,PANCHAL S R,SHAH S K. A comparison of SIFT and SURF[J]. Computer and Communication Engineering,2013,1(2):323-327.
[8]	[8] BAY H,TUYTELAARS T,VAN G L. SURF:speeded up robust features[J]. Computer Vision and Image Understanding,2008,110(3):346-359.
[9]	[9] 王健博,朱明.基于字典描述向量的实时图像配准[J].光学精密工程,2014,22(6):1613-1621. WANG J B,ZHU M. Real time image registration based on dictionary feature descriptor[J]. Opt. Precision Eng.,2014,22(6):1613-1621.(in Chinese)
[10]	[10] 张锐娟,张建奇,杨翠.基于SURF的图像配准方法研究[J].红外与激光工程,2009,38(1):160-165. ZHANG R J,ZHANG J Q,YANG C. Image registration approach based on SURF[J]. Infrared and Laser Engineering,2009,38(1):160-165.(in Chinese)
[11]	[11] MUJA M,LOWE D G. Scalable nearest neighbor algorithms for high dimensional data[J]. IEEE,2014,36(11):2227-2240.
[12]	[12] GUANG J S,XIANG Y X,YA P D. SIFT feature Point matching based on Improved RANSAC algorithm[C]. 2013 Fifth International Conference on Intelligent Human-Machine Systems and Cybernetics(IHMSC2013),Hangzhou,2013:474-477.