A 4PCS Coarse Registration Algorithm Based on ISS Feature Points

2021 ◽  
Author(s):  
Zhongfan Yang ◽  
Xiaogang Wang ◽  
Jin Hou
Keyword(s):  
Feature Points ◽  
Registration Algorithm ◽  
Coarse Registration

Research on registration algorithm based on feature points for human eye retina images

2009 ◽  
Author(s):  
Yuran Liu ◽  
Huizhen Yang ◽  
Liyun Su ◽  
Yudong Zhang ◽  
Xuejun Rao
Keyword(s):  
Feature Points ◽  
Human Eye ◽  
Registration Algorithm ◽  
Eye Retina ◽  
Retina Images

A New Algorithm of Image Registration Based on Contour Feature

2014 ◽  
Vol 912-914 ◽  
pp. 1092-1097
Author(s):  
Fu Hua Song ◽  
Peng Hui Li ◽  
Jian Ran Deng
Keyword(s):  
Image Registration ◽  
Rotation Angle ◽  
Closed Contour ◽  
Feature Points ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Contour Feature ◽  
Circular Correlation ◽  
Open Contour ◽  
Calculated Amount

Image registration is an important task in image processing. In this paper, a new and fast contour-based image registration algorithm is proposed. In this algorithm, we fetch contour points and calculate the normal angles firstly, then figure out the histogram of the contour-feature points. By computing circular correlation of the histogram, the rotation angle can be gained. As the rotation angle is obtained, it vastly simplifies the complexity of estimating other registration parameters and reduces the calculated amount, thus achieving a fast image registration algorithm. This algorithm has the invariance of rotation, translation and scale, and it has high robustness for either open contour or closed contour.

A Robust Image Registration Algorithm Based on Feature Points Matching

2008 ◽  
Vol 28 (3) ◽  
pp. 454-461 ◽  
Author(s):  
刘贵喜 Liu Guixi ◽  
刘冬梅 Liu Dongmei ◽  
刘凤鹏 Liu Fengpeng ◽  
周亚平 Zhou Yaping
Keyword(s):  
Image Registration ◽  
Feature Points ◽  
Registration Algorithm ◽  
Feature Points Matching ◽  
Image Registration Algorithm ◽  
Robust Image

A New Fast Multi-Source Remote Sensing Image Registration Algorithm

2012 ◽  
Vol 452-453 ◽  
pp. 950-953
Author(s):  
Yong Mei Zhang ◽  
Li Ma
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Control Point ◽  
Feature Points ◽  
Remote Sensing Images ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Shift Rotation ◽  
Back Ground

Aiming at the registration of multi-sensor remote sensing images, a fast and effective image registration algorithm method is presented. In this algorithm, using SIFT to extract feature points, remove mismatching points through Delaunay triangulation, and introduce distance calculation into the determination of homologous control point pairs. The effect of image registration algorithm is evaluated by subjective and objective method. Experiment results show the proposed algorithm can accurately register multi-spectral and panchromatic images with some shift, rotation angles and back-ground noises, and it can inrease the speed and precision of registration.

scholarly journals A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Jianguo Li ◽  
Quanhai Ma
Keyword(s):  
Fourier Transform ◽  
Image Registration ◽  
Cross Correlation ◽  
Matrix Multiplication ◽  
Single Step ◽  
Phase Correlation ◽  
Brain Image ◽  
Registration Algorithm ◽  
Bidirectional Search ◽  
Coarse Registration

Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.

scholarly journals Pairwise Registration Algorithm for Large-Scale Planar Point Cloud Used in Flatness Measurement

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4860
Author(s):  
Zichao Shu ◽  
Songxiao Cao ◽  
Qing Jiang ◽  
Zhipeng Xu ◽  
Jianbin Tang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Three Dimensional ◽  
Planar Point ◽  
Registration Algorithm ◽  
Geometric Consistency ◽  
Coarse Registration ◽  
Pairwise Registration ◽  
Flatness Measurement

In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.

Sign in / Sign up

Export Citation Format

Share Document