Robust image registration using subspace method in Radon transform domain

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 645-651
Author(s):  
Ning Wei ◽  
Yu He ◽  
Junqing Liu ◽  
Peng Chen
Keyword(s):  
Image Registration ◽  
Radon Transform ◽  
Median Filter ◽  
Least Square ◽  
Phase Correlation ◽  
Transform Domain ◽  
Registration Method ◽  
Content Type ◽  
Structural Differences ◽  
Robust Image

Purpose The purpose of this paper is to represent a robust image registration method to align noisy and deformed images in their Radon transform domain. Due to the limitation of imaging mechanism, the images are often highly noisy. Even worse, the objects in images have structural differences from time to time. Design/methodology/approach To eliminate these degressions, the proposed method is equipped with subspace-based power spectrum analysis algorithm for rotation estimation and a new global median filter least square algorithm for displacement computation. Findings Experiments on strongly noisy and degenerated images show that the proposed method exhibits better accuracy and robustness than phase correlation-based method. In addition, the method can also be applied to multi-modal registration, where the results are comparable to mutual information method but spending much less time. Originality/value A robust image registration method is proposed, which has better performance than traditional methods.

Polarized image registration method based on phase correlation and sub-graph

2019 ◽  
Vol 34 (5) ◽  
pp. 530-536
Author(s):  
万钇良 WAN Yi-liang ◽  
王建立 WANG Jian-li ◽  
张 楠 ZHANG Nan ◽  
姚凯男 YAO Kai-nan ◽  
王昊京 WANG Hao-jing
Keyword(s):  
Image Registration ◽  
Phase Correlation ◽  
Registration Method

Positional deviation detection of silicone caps on FPCB

Circuit World ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 23-29
Author(s):  
Dejian Li ◽  
Shaoli Li ◽  
Weiqi Yuan
Keyword(s):  
Image Registration ◽  
Printed Circuit Board ◽  
Nearest Neighbor ◽  
Displacement Vector ◽  
Mean Value ◽  
Circuit Board ◽  
Registration Method ◽  
Content Type ◽  
Control Center ◽  
Flexible Deformation

Purpose The purpose of this paper is to propose a defect detection method of silicone caps positional deviation on flexible printed circuit board (FPCB) of keyboard based on automatic optical inspection. Design/methodology/approach First, the center of silicone caps of target keyboard FPCB image was extracted as feature points for generating the feature image which is used for registration rigidly with the reference feature image generated from the CAD drawings. Then, a flexible image registration method based on the surrounding-control-center B-splines (SCCB) strategy was proposed, which could correct the flexible deformation of the image generated by FPCB substrate while keeping the pasting deviation information about silicone caps unchanged. Finally, on this basis, a nearest neighbor strategy was proposed to detect the positional deviation of silicone caps. Findings Experimental results show that the proposed method can effectively detect the positional deviation defect of silicone caps. The G-mean value of the proposed method is 0.941746, which is 0.3 higher compared to that of similar research. Originality/value This paper presents a method to detect positional deviation defect of silicone caps on keyboard FPCB. Different from the classic B-spline image registration method, the proposed SCCB method used the neighborhood information of the pixel to be registered selectively to calculate the displacement vector needed for its registration, which overcame the problem that the silicone cap pasting deviation information disappears with the correction of the flexible deformation of the image.

A Modified Phase Correlation Method for Image Registration

2011 ◽  
Vol 48-49 ◽  
pp. 48-51
Author(s):  
Lu Jing Yang ◽  
Wei Hao ◽  
Chong Lun Li
Keyword(s):  
Image Registration ◽  
Rotation Angle ◽  
Translational Motion ◽  
Correlation Method ◽  
Phase Correlation ◽  
Flow Chart ◽  
Reference Image ◽  
Sar Image ◽  
Registration Method ◽  
Simulation Results

Image registration is a very fundamental and important part in many multi-sensor image based applications. Phase correlation-based image registration method is widely concerned for its small computation amount, strong anti-interference property. However, it can only solve the image registration problem with translational motion. Hence, we proposed a modified phase correlation registration method in the paper. We analyzed the principle of registration, gave the flow chart, and applied the method to the SAR image registration problems with scaling, rotation and translation transformation. Simulation results show that the method can accurately estimate the translation parameters, zoom scale and rotation angle of registrating image relative to the reference image.

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.

scholarly journals An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

2018 ◽  
Vol 10 (11) ◽  
pp. 1719 ◽  
Author(s):  
Yunyun Dong ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Guojin He ◽  
Chengjuan Gong
Keyword(s):  
Fourier Transform ◽  
Image Registration ◽  
Fractional Fourier Transform ◽  
Correlation Method ◽  
High Accuracy ◽  
Phase Correlation ◽  
Estimation Methods ◽  
Registration Method ◽  
Similarity Transform ◽  
Polar Grid

Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1 / 10 or even 1 / 100 . However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.

An Underwater Image Real-Time Registration Approach Based on SURF

2013 ◽  
Vol 437 ◽  
pp. 888-893 ◽  
Author(s):  
Chao Li ◽  
Yong Jie Pang ◽  
Ming Wei Sheng ◽  
Hai Huang
Keyword(s):  
Image Registration ◽  
Real Time ◽  
Median Filter ◽  
Filter Method ◽  
Interest Points ◽  
Registration Method ◽  
Speeded Up Robust Features ◽  
Time Performance ◽  
Underwater Image ◽  
Image Contrast Enhancement

In order to meet the demands of real-time performance and robustness for underwater image registration, a novel image registration method based on the SURF (Speeded-Up Robust Features) algorithm is proposed. During the image acquisition process, noise was generated inevitably because of many influencing factors such as atmospheric turbulence, camera defocus during image capturing or relative motion between the camera and the object. Firstly, median filter method was involved during the image preprocessing for underwater image contrast enhancement. Secondly, the SURF algorithm was used to obtain the interest points of the reference and registering images, and the nearest neighbor method was applied to search for coarse matching points. To obtain the precise matching points, the dominant orientations of the coarse matching points were used to eliminate the mismatching points. Finally, the precise matching points were adapted to calculate the mapping relationship between the registering and reference images, the bilinear interpolation method was applied to resample the registering image, and then the registered image was obtained. Experimental results indicated that the proposed preprocessing methods obviously enhanced the image quality, and the introduced image registration approach effectively improved the real-time performance and guaranteed the robustness at the same time.

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