scholarly journals Image registration comparative analysis: normalized correlation versus SIFT-based registration

Radiotekhnika ◽  
2020 ◽  
pp. 191-196
Author(s):  
V.A. Dushepa ◽  
Y.A. Tiahnyriadno ◽  
I.V. Baryshev
Keyword(s):  
Image Registration ◽  
High Accuracy ◽  
Correction Algorithm ◽  
Sift Algorithm ◽  
Computational Costs ◽  
Scale Change ◽  
Feature Based ◽  
Position Shift ◽  
Python Programming ◽  
Accuracy And Stability

The paper compares the image registration algorithms: the classical normalized correlation (as a representative of intensity-based algorithms) and the SIFT-based algorithm (feature-based registration). A gradient subpixel correction algorithm was also used for normalized correlation. We compared the effectiveness of their work on real images (including a terrain map) when modeling artificial distortions. The accuracy of determining the position (shift) of one image relative to another in the presence of rotation and scale changes was studied. The experiment was carried out using a simulation model created in the Python programming language using the OpenCV computer vision library. The results of the experiments show that in the absence of rotation and scale changes between the registered images the normalized correlation provides a slightly smaller root-mean-square error. At the same time, if there are even small such distortions, for example, a rotation of more than 2 degrees and a scale change of more than 2 percent, the probability of correct registration for the normalized correlation drops sharply. It was also noted that the advantages of normalized correlation are almost 5 times higher speed and the possibility of using it for small fragments (50x50 or less), where it is problematic for the SIFT algorithm to allocate a sufficient number of keypoints. It was also shown that the use of a two-stage algorithm (SIFT-based registration at the first stage, and optimization with normalized correlation as a criterion at the second) allows you to get both high accuracy and stability to rotation and scale change, but this will be accompanied by high computational costs.

scholarly journals An Automated Method for Feature-Based Image Registration with High-Accuracy

2008 ◽  
Vol 19 (9) ◽  
pp. 2293-2301 ◽  
Author(s):  
Gong-Jian WEN ◽  
Jin-Jian LÜ ◽  
Ji-Yang WANG
Keyword(s):  
Image Registration ◽  
High Accuracy ◽  
Automated Method ◽  
Feature Based

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 Extracting image features for classification by two-tier genetic programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
A Song ◽  
K Neshatian ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Process ◽  
High Accuracy ◽  
Classification Performance ◽  
Image Features ◽  
Classification Methods ◽  
Feature Based ◽  
Second Tier

Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process. © 2012 IEEE.

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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