Image Registration with a Comparative Feature Matching Approach

2021 ◽  
pp. 313-325
Author(s):  
Biprojit Roy ◽  
Geetanjali Oishe
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Comparative Feature

Image Registration Based on Spectral and Spatial Feature Matching

2020 ◽  
Vol 57 (2) ◽  
pp. 021005
Author(s):  
陈泽锋 Chen Zefeng ◽  
吴庆阳 Wu Qingyang ◽  
陈顺治 Chen Shunzhi ◽  
李奇锋 Li Qifeng ◽  
卢晓婷 Lu Xiaoting ◽  
...  
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Spatial Feature

scholarly journals Feature Matching for Remote Sensing Image Registration via Manifold Regularization

2020 ◽  
Vol 13 ◽  
pp. 4564-4574
Author(s):  
Huabing Zhou ◽  
Anna Dai ◽  
Tian Tian ◽  
Yulu Tian ◽  
Zhenghong Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Manifold Regularization

On Feature Matching and Image Registration for Two-dimensional Forward-scan Sonar Imaging

2013 ◽  
Vol 30 (4) ◽  
pp. 602-623 ◽  
Author(s):  
Murat D. Aykin ◽  
Shahriar Negahdaripour
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Two Dimensional ◽  
Sonar Imaging

A CNN based Hybrid approach towards automatic image registration

2013 ◽  
Vol 62 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Pattathal V. Arun ◽  
Sunil K. Katiyar
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Feature Matching ◽  
Spatial Information ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Cellular Neural Network ◽  
Contextual Knowledge ◽  
Neural Network Approach ◽  
Statistical Measures

Abstract Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

HYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCER

2007 ◽  
Vol 19 (06) ◽  
pp. 359-374 ◽  
Author(s):  
Yih-Chih Chiou ◽  
Chern-Sheng Lin ◽  
Cheng-Yu Lin
Keyword(s):  
Breast Cancer ◽  
Image Registration ◽  
Early Detection ◽  
Feature Matching ◽  
Automatic Detection ◽  
Spatial Transformation ◽  
Registration Method ◽  
Feature Based ◽  
Mammogram Images ◽  
Hybrid Registration

Mammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, a few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method for the early detection of breast cancer is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during the registration to obtain a global spatial transformation. The experimental results indicates that the similarity between the two mammograms increases significantly after a proper registration using the proposed TPS-registration procedures.

Robust feature matching via Gaussian field criterion for remote sensing image registration

2018 ◽  
Vol 15 (3) ◽  
pp. 523-536 ◽  
Author(s):  
Qing Ma ◽  
Xu Du ◽  
Jiahao Wang ◽  
Yong Ma ◽  
Jiayi Ma
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Gaussian Field ◽  
Field Criterion
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