Rotation-Invariant Fast Feature Based Image Registration for Motion Compensation in Aerial Image Sequences

2017 ◽  
pp. 211-221
Author(s):  
Vindhya P. Malagi ◽  
D. R. Ramesh Babu
Keyword(s):  
Image Registration ◽  
Motion Compensation ◽  
Image Sequences ◽  
Aerial Image ◽  
Rotation Invariant ◽  
Feature Based

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 Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

2021 ◽  
Vol 205 ◽  
pp. 106085
Author(s):  
Monire Sheikh Hosseini ◽  
Mahammad Hassan Moradi ◽  
Mahdi Tabassian ◽  
Jan D'hooge
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Cardiac Motion ◽  
Inference System ◽  
Feature Based

A Comparison of Image Registration Methods Used for Printed Circuit Board Inspection

2008 ◽  
Vol 381-382 ◽  
pp. 295-298
Author(s):  
Shin Chieh Lin ◽  
C.T. Chen ◽  
C.H. Chou
Keyword(s):  
Image Registration ◽  
Printed Circuit Board ◽  
Circuit Board ◽  
Loading Test ◽  
Area Method ◽  
Printed Circuit ◽  
Feature Based ◽  
Position And Orientation ◽  
Inspection Machine

In this study, registration methods used to estimate both position and orientation differences between two images had been evaluated. This is an important issue since that there are always some position and orientation differences when loading test samples on the inspection machine. These differences should be calculated and compensated before further analysis. Registration methods tested including one area method and three feature based method. It was shown that the area method had better performance than other feature based method in these cases studied. And it is shown that it is much easy to detect defect by analyzing the subtracted image with position and orientation compensation instead of those without compensation.

scholarly journals Efficient feature-based image registration by mapping sparsified surfaces

2018 ◽  
Vol 55 ◽  
pp. 561-571 ◽  
Author(s):  
Chun Pang Yung ◽  
Gary P.T. Choi ◽  
Ke Chen ◽  
Lok Ming Lui
Keyword(s):  
Image Registration ◽  
Feature Based

scholarly journals Implementation of RANSAC Algorithm for Feature-Based Image Registration

2013 ◽  
Vol 01 (06) ◽  
pp. 46-50 ◽  
Author(s):  
Lan-Rong Dung ◽  
Chang-Min Huang ◽  
Yin-Yi Wu
Keyword(s):  
Image Registration ◽  
Feature Based ◽  
Ransac Algorithm

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

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