• Laser & Optoelectronics Progress
  • Vol. 58, Issue 12, 1210010 (2021)
Didi Zhao1、2、3, Jiahui Li1、2、3, Fenli Tan1、2、3, Chenxin Zeng1、2、3, and Yiqun Ji1、2、3、*
Author Affiliations
  • 1School of Optoelectronic Science and Engineering, Soochow University, Suzhou, Jiangsu 215006, China
  • 2Jiangsu Key Laboratory of Advanced Optical Manufacturing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
  • 3Key Laboratory of Modern Optical Technologies, Ministry of Education, Soochow University, Suzhou, Jiangsu 215006, China
  • show less
    DOI: 10.3788/LOP202158.1210010 Cite this Article Set citation alerts
    Didi Zhao, Jiahui Li, Fenli Tan, Chenxin Zeng, Yiqun Ji. Image Registration Algorithm Based on Smoothness Constraint and Cluster Analysis[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210010 Copy Citation Text show less
    Image registration process of our algorithm
    Fig. 1. Image registration process of our algorithm
    Schematic of the SC
    Fig. 2. Schematic of the SC
    Screening of initial matches based on SC. (a) Selection of reliable matches; (b) checking the consistency of the global zoom scale; (c) local geometric constraints
    Fig. 3. Screening of initial matches based on SC. (a) Selection of reliable matches; (b) checking the consistency of the global zoom scale; (c) local geometric constraints
    RANSAC algorithm based on cluster analysis
    Fig. 4. RANSAC algorithm based on cluster analysis
    Selection process of optimal inliers. (a) Tentative inliers; (b) cluster analysis results of tentative inliers; (c) distribution quality of the luster center
    Fig. 5. Selection process of optimal inliers. (a) Tentative inliers; (b) cluster analysis results of tentative inliers; (c) distribution quality of the luster center
    Testing images. (a) Blur change; (b) viewpoint change; (c) rotation and scale changes; (d) illumination change; (e) rotation change; (f) scale change
    Fig. 6. Testing images. (a) Blur change; (b) viewpoint change; (c) rotation and scale changes; (d) illumination change; (e) rotation change; (f) scale change
    Effect of different parameters on the purification performance of initial matches. (a) Number of inliers with different α; (b) ratio of inliers with different α; (c) number of inliers with different β; (d) ratio of inliers with different β
    Fig. 7. Effect of different parameters on the purification performance of initial matches. (a) Number of inliers with different α; (b) ratio of inliers with different α; (c) number of inliers with different β; (d) ratio of inliers with different β
    Images pairs for homography estimation
    Fig. 8. Images pairs for homography estimation
    AlgorithmRANSACFLRSLPM+RANSACFLRS+IBCOSCCA
    Error (RMSE±STD)2.05±0.681.95±0.512.07±0.651.82±0.341.50±0.19
    P /%71.6170.8471.6470.3472.94
    R /%77.9685.7576.6789.0790.50
    Time /s0.170.130.039.660.51
    Table 1. Performances of different algorithms
    Didi Zhao, Jiahui Li, Fenli Tan, Chenxin Zeng, Yiqun Ji. Image Registration Algorithm Based on Smoothness Constraint and Cluster Analysis[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210010
    Download Citation