Affine Image Registration Based on Geometric Inference

2013 ◽  
Vol 427-429 ◽  
pp. 1610-1613
Author(s):  
Xing Wei Yan ◽  
Jie Min Hu ◽  
Jun Zhang ◽  
Jian Wei Wan
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Real Data ◽  
Matching Problem ◽  
Point Matching ◽  
Noncollinear Point ◽  
Geometric Inference ◽  
Affine Image ◽  
Novel Method ◽  
Geometric Relationship

Image registration is widely used in applications for mapping one image to another. As it is often formulated as a point matching problem, in this paper, a novel method, called the Geometric Inference (GI) algorithm, is proposed for feature point based image registration. Firstly, according to affine distance invariant, the global geometric relationship between collinear correspondences is deduced and used for collinear point matching. Secondly, utilizing affine area invariant, geometric relationship between noncollinear correspondences is inferred and used for noncollinear point matching. Finally, the best affine transformation can be discovered from the correspondences composed of the collinear and noncollinear corresponding point pairs. Experiments on synthesized and real data demonstrate that GI is well-adapt to image registration as it is fast and robust to missing points, outliers, and noise.

Scatter Search for the Point-Matching Problem in 3D Image Registration

2008 ◽  
Vol 20 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Oscar Cordón ◽  
Sergio Damas ◽  
Jose Santamaría ◽  
Rafael Martí
Keyword(s):  
Image Registration ◽  
Scatter Search ◽  
3D Image ◽  
Matching Problem ◽  
Point Matching ◽  
3D Image Registration

scholarly journals HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

2021 ◽  
Vol 13 (12) ◽  
pp. 2328
Author(s):  
Yameng Hong ◽  
Chengcai Leng ◽  
Xinyue Zhang ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Image Registration ◽  
Local Binary Pattern ◽  
Computational Time ◽  
Rotation Invariant ◽  
Edge Information ◽  
Important Research Topic ◽  
New Construction ◽  
Novel Method ◽  
Direction Information ◽  
Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

scholarly journals Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

2021 ◽  
Vol 11 (2) ◽  
pp. 582
Author(s):  
Zean Bu ◽  
Changku Sun ◽  
Peng Wang ◽  
Hang Dong
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Calibration Method ◽  
Multiple Sensors ◽  
Triangular Pyramid ◽  
World Coordinate System ◽  
Flash Lidar ◽  
Novel Method ◽  
3D Information ◽  
Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

Multi-Modal Medical Image Registration of by Combining Improved Mean Shift and RPM

2012 ◽  
Vol 239-240 ◽  
pp. 1472-1475
Author(s):  
Dan Ai ◽  
Jing Li Shi ◽  
Jun Jun Cao ◽  
Hong Yan Zhong
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Mean Shift ◽  
Decisive Role ◽  
Experimental Results ◽  
Medical Image Registration ◽  
Image Content ◽  
Point Matching ◽  
Target Mode ◽  
Convergence Process

Landmark correspondence plays a decisive role in the landmark-based multi-modality image registration. We combine RPM (Robust Point Matching) and improved Mean Shift to estimate the correspondence of landmarks in images. We improve the target mode and bandwidth used in Mean Shift, and we also perform RPM to estimate the initial landmark correspondence. Next, we use improved Mean Shift to adjust corresponding relations between points. Our method is benefit to make corresponding relations between points more accurate and impels the convergence process of RPM to be related to the image content. Experimental results show that our method can achieve accurate registration of the multi-modal images.

Breast image registration by using non-linear local affine transformation

2013 ◽  
Author(s):  
Feiyu Chen ◽  
Peng Zheng ◽  
Penglong Xu ◽  
Andrew D. A. Maidment ◽  
Predrag R. Bakic ◽  
...  
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Breast Image ◽  
Non Linear

Influence of Speckle Noise on Image Registration Based on Feature Point Matching

2017 ◽  
Vol 54 (12) ◽  
pp. 121103
Author(s):  
陈波 Chen Bo ◽  
孙天齐 Sun Tianqi ◽  
刘爱新 Liu Aixin
Keyword(s):  
Image Registration ◽  
Speckle Noise ◽  
Feature Point ◽  
Point Matching ◽  
Feature Point Matching

scholarly journals On the Affine Image of a Rational Surface of Revolution

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2061
Author(s):  
Juan G. Alcázar
Keyword(s):  
Differential Geometry ◽  
Affine Transformation ◽  
Rational Surface ◽  
Affine Differential Geometry ◽  
Affine Mapping ◽  
Surfaces Of Revolution ◽  
Surface Of Revolution ◽  
Affine Image ◽  
Fixed Line ◽  
The Given

We study the properties of the image of a rational surface of revolution under a nonsingular affine mapping. We prove that this image has a notable property, namely that all the affine normal lines, a concept that appears in the context of affine differential geometry, created by Blaschke in the first decades of the 20th century, intersect a fixed line. Given a rational surface with this property, which can be algorithmically checked, we provide an algorithmic method to find a surface of revolution, if it exists, whose image under an affine mapping is the given surface; the algorithm also finds the affine transformation mapping one surface onto the other. Finally, we also prove that the only rational affine surfaces of rotation, a generalization of surfaces of revolution that arises in the context of affine differential geometry, and which includes surfaces of revolution as a subtype, affinely transforming into a surface of revolution are the surfaces of revolution, and that in that case the affine mapping must be a similarity.

A novel method for automatic image registration based on wavelet and near fuzzy set

2013 ◽  
Author(s):  
Somoballi Ghoshal ◽  
Pubali Chatterjee ◽  
Biswajit Biswas ◽  
Amlan Chakrabarti ◽  
Kashi Nath Dey
Keyword(s):  
Image Registration ◽  
Fuzzy Set ◽  
Novel Method

scholarly journals Obtaining the Correspondence Between Bayesian and Neural Networks

1998 ◽  
Vol 12 (07) ◽  
pp. 901-920 ◽  
Author(s):  
A. Stassopoulou ◽  
M. Petrou
Keyword(s):  
Neural Network ◽  
Bayesian Network ◽  
Full Range ◽  
Closed Form Solution ◽  
Real Data ◽  
Form Solution ◽  
Least Square ◽  
Novel Method ◽  
Probability Matrices ◽  
Desertification Assessment

We present in this paper a novel method for eliciting the conditional probability matrices needed for a Bayesian network with the help of a neural network. We demonstrate how we can obtain a correspondence between the two networks by deriving a closed-form solution so that the outputs of the two networks are similar in the least square error sense, not only when determining the discriminant function, but for the full range of their outputs. For this purpose we take into consideration the probability density functions of the independent variables of the problem when we compute the least square error approximation. Our methodoloy is demonstrated with the help of some real data concerning the problem of risk of desertification assessment for some burned forests in Attica, Greece where the parameters of the Bayesian network constructed for this task are successfully estimated given a neural network trained with a set of data.

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