Multi-Sensor Image Registration by Combination of Relaxation Optimization Matching and Partitioning RANSAC

2013 ◽  
Vol 765-767 ◽  
pp. 2882-2885
Author(s):  
Ying Dan Wu ◽  
Yang Ming
Keyword(s):  
Image Registration ◽  
Optimization Technique ◽  
Registration Method ◽  
Approximate Relationship ◽  
Coarse Registration ◽  
Relationship Of ◽  
Coarse To Fine

This paper presents a multi-sensor image registration method by combination of relaxation optimization matching and Partitioning RANSAC. Firstly, the global coarse registration is performed to establish the approximate relationship of reference and slave image. Secondly, in each pyramid level, the normalized MI and relaxation optimization technique are adopted to get the matching points, and partitioning RANSAC is used to delete the existing false matches. The coarse-to-fine strategy is integrated to refine the results, and finally the rubber sheeting method is used to realize the image registration. Two datasets have been experimented, and it can be found that satisfactory registration method can be obtained.

scholarly journals A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

2019 ◽  
Vol 11 (15) ◽  
pp. 1833 ◽  
Author(s):  
Han Yang ◽  
Xiaorun Li ◽  
Liaoying Zhao ◽  
Shuhan Chen
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Phase Correlation ◽  
Mean Square ◽  
Extended Phase ◽  
Registration Method ◽  
Coarse Registration ◽  
Feature Based ◽  
Coarse To Fine ◽  
Fine Registration

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.

Complex Image Registration Method Based on Correlative Coefficient Interpolation

2011 ◽  
Vol 65 ◽  
pp. 514-517
Author(s):  
Rui Niu ◽  
Xiao Tao Tang ◽  
Yu Wang
Keyword(s):  
Image Registration ◽  
High Efficiency ◽  
Interpolation Method ◽  
Interferometric Synthetic Aperture Radar ◽  
Registration Method ◽  
The Earth ◽  
Complex Image ◽  
Interferometric Phase ◽  
Coarse Registration ◽  
Coefficient Method

Interferometric Synthetic Aperture Radar is a kind of technology to acquire the DEM information on the surface of the earth. It is concerned and researched by all over the world. Complex image registration of high precision is the key step in InSAR data processing, its results directly influence on the quantity of interferometric phase , even to the DEM precision.This paper introduces the complex image registration plans which is used the correlative coefficient method to make the coarse registration, and is used the correlative coefficient interpolation method to make the high precise registration.The experiments with spaceborne and aeroplane InSAR data prove that this method is with feasibility, high efficiency and practicability.

scholarly journals AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL

2016 ◽  
Vol XLI-B7 ◽  
pp. 593-600
Author(s):  
C. Xu ◽  
H. G. Sui ◽  
D. R. Li ◽  
K. M. Sun ◽  
J. Y. Liu
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Visual Saliency ◽  
Specific Area ◽  
Image Feature ◽  
Sar Image ◽  
Registration Method ◽  
Adaptive Parameter ◽  
Coarse Registration ◽  
Multi Level

Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

A coarse-to-fine image registration method based on visual attention model

2014 ◽  
Vol 57 (12) ◽  
pp. 1-10 ◽  
Author(s):  
Jing Feng ◽  
Long Ma ◽  
FuKun Bi ◽  
XueJing Zhang ◽  
He Chen
Keyword(s):  
Image Registration ◽  
Visual Attention ◽  
Registration Method ◽  
Visual Attention Model ◽  
Attention Model ◽  
Coarse To Fine

scholarly journals A Point Pattern Chamfer Registration of Optical and SAR Images Based on Mesh Grids

2018 ◽  
Vol 10 (11) ◽  
pp. 1837 ◽  
Author(s):  
Chu He ◽  
Peizhang Fang ◽  
Dehui Xiong ◽  
Wenwei Wang ◽  
Mingsheng Liao
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Point Pattern ◽  
Sar Image ◽  
Sar Images ◽  
Coarse Registration ◽  
Feature Based ◽  
Mesh Grid ◽  
Coarse To Fine ◽  
Fine Registration

Automatic image registration of optical-to-Synthetic aperture radar (SAR) images is difficult because of the inconsistency of radiometric and geometric properties between the optical image and the SAR image. The intensity-based methods may require many calculations and be ineffective when there are geometric distortions between these two images. The feature-based methods have high requirements on features, and there are certain challenges in feature extraction and matching. A new automatic optical-to-SAR image registration framework is proposed in this paper. First, modified holistically nested edge detection is employed to detect the main contours in both the optical and SAR images. Second, a mesh grid strategy is presented to perform a coarse-to-fine registration. The coarse registration calculates the feature matching and summarizes the preliminary results for the fine registration process. Finally, moving direct linear transformation is introduced to perform a homography warp to alleviate parallax. The experimental results show the effectiveness and accuracy of our proposed method.

An Abdominal Registration Technology for Integration of Nanomaterial Imaging-Aided Diagnosis and Treatment

2021 ◽  
Vol 17 (5) ◽  
pp. 952-959
Author(s):  
Shao-Di Yang ◽  
Yu-Qian Zhao ◽  
Fan Zhang ◽  
Miao Liao ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Image Registration ◽  
Unsupervised Learning ◽  
Large Scale ◽  
Three Dimensional ◽  
Initial Step ◽  
Abdominal Ct ◽  
Registration Method ◽  
Therapy Effect ◽  
Coarse To Fine ◽  
Aided Diagnosis

Image registration technology is a key technology used in the process of nanomaterial imaging-aided diagnosis and targeted therapy effect monitoring for abdominal diseases. Recently, the deep-learning based methods have been increasingly used for large-scale medical image registration, because their iteration is much less than those of traditional ones. In this paper, a coarse-to-fine unsupervised learning-based three-dimensional (3D) abdominal CT image registration method is presented. Firstly, an affine transformation was used as an initial step to deal with large deformation between two images. Secondly, an unsupervised total loss function containing similarity, smoothness, and topology preservation measures was proposed to achieve better registration performances during convolutional neural network (CNN) training and testing. The experimental results demonstrated that the proposed method severally obtains the average MSE, PSNR, and SSIM values of 0.0055, 22.7950, and 0.8241, which outperformed some existing traditional and unsupervised learning-based methods. Moreover, our method can register 3D abdominal CT images with shortest time and is expected to become a real-time method for clinical application.

A Coarse-to-Fine Registration on 3D Multi-Phase Abdominal CT Images

2020 ◽  
Vol 12 (7) ◽  
pp. 909-914
Author(s):  
Shao-Di Yang ◽  
Fan Zhang ◽  
Zhen Yang ◽  
Xiao-Yu Yang ◽  
Shu-Zhou Li
Keyword(s):  
Three Dimensional ◽  
Ct Images ◽  
Abdominal Ct ◽  
Registration Method ◽  
Coarse Registration ◽  
Multi Phase ◽  
Coarse To Fine ◽  
Aided Diagnosis ◽  
Fine Registration ◽  
Spatial Regularization

Registration is a technical support for the integration of nanomaterial imaging-aided diagnosis and treatment. In this paper, a coarse-to-fine three-dimensional (3D) multi-phase abdominal CT images registration method is proposed. Firstly, a linear model is used to coarsely register the paired multiphase images. Secondly, an intensity-based registration framework is proposed, which contains the data and spatial regularization terms and performs fine registration on the paired images obtained in the coarse registration step. The results illustrate that the proposed method is superior to some existing methods with the average MSE, PSNR, and SSIM values of 0.0082, 21.2695, and 0.8956, respectively. Therefore, the proposed method provides an efficient and robust framework for 3D multi-phase abdominal CT images registration.

scholarly journals A Medical Image Registration Method Based on Progressive Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qian Zheng ◽  
Qiang Wang ◽  
Xiaojuan Ba ◽  
Shan Liu ◽  
Jiaofen Nan ◽  
...  
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Reference Image ◽  
Medical Image Registration ◽  
Registration Method ◽  
Multimodal Registration ◽  
Coarse Registration ◽  
Registration Result

Background. Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy. Methods. As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method. Results. For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods. Conclusions. The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.

scholarly journals Registration of the Cone Beam CT and Blue-Ray Scanned Dental Model Based on the Improved ICP Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Xue Mei ◽  
Zhenhua Li ◽  
Songsong Xu ◽  
Xiaoyan Guo
Keyword(s):  
Image Registration ◽  
Cone Beam Ct ◽  
Transformation Method ◽  
Cone Beam ◽  
Registration Method ◽  
Icp Algorithm ◽  
Coarse Registration ◽  
Dental Model ◽  
Point Set ◽  
Multimodality Image

Multimodality image registration and fusion has complementary significance for guiding dental implant surgery. As the needs of the different resolution image registration, we develop an improved Iterative Closest Point (ICP) algorithm that focuses on the registration of Cone Beam Computed Tomography (CT) image and high-resolution Blue-light scanner image. The proposed algorithm includes two major phases, coarse and precise registration. Firstly, for reducing the matching interference of human subjective factors, we extract feature points based on curvature characteristics and use the improved three point’s translational transformation method to realize coarse registration. Then, the feature point set and reference point set, obtained by the initial registered transformation, are processed in the precise registration step. Even with the unsatisfactory initial values, this two steps registration method can guarantee the global convergence and the convergence precision. Experimental results demonstrate that the method has successfully realized the registration of the Cone Beam CT dental model and the blue-ray scanner model with higher accuracy. So the method could provide researching foundation for the relevant software development in terms of the registration of multi-modality medical data.

scholarly journals AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL

2016 ◽  
Vol XLI-B7 ◽  
pp. 593-600 ◽  
Author(s):  
C. Xu ◽  
H. G. Sui ◽  
D. R. Li ◽  
K. M. Sun ◽  
J. Y. Liu
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Visual Saliency ◽  
Specific Area ◽  
Image Feature ◽  
Sar Image ◽  
Registration Method ◽  
Adaptive Parameter ◽  
Coarse Registration ◽  
Multi Level

Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

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