Blurred Image Registration by Combined Invariant of Legendre Moment and Harris-Laplace Detector

2010 ◽  
Author(s):  
Xiubin Dai ◽  
Hui Zhang ◽  
Huazhong Shu ◽  
Limin Luo ◽  
Tianliang Liu
Keyword(s):  
Image Registration ◽  
Blurred Image ◽  
Legendre Moment

scholarly journals Blurred Image Recognition by Legendre Moment Invariants

2010 ◽  
Vol 19 (3) ◽  
pp. 596-611 ◽  
Author(s):  
Hui Zhang ◽  
Huazhong Shu ◽  
G.N. Han ◽  
G. Coatrieux ◽  
Limin Luo ◽  
...  
Keyword(s):  
Image Recognition ◽  
Moment Invariants ◽  
Blurred Image ◽  
Legendre Moment

Comparative study of NOTES alone vs. NOTES guided by a new image registration system for navigation in the mediastinum: A study in a porcine model

Endoscopy ◽  
2012 ◽  
Vol 44 (10) ◽  
Author(s):  
H Córdova ◽  
R San José Estépar ◽  
A Rodríguez-D'Jesús ◽  
G Martínez-Pallí ◽  
P Arguis ◽  
...  
Keyword(s):  
Image Registration ◽  
Comparative Study ◽  
Porcine Model ◽  
Registration System

Estimation of PSF Using Logarithmic Amplitude Spectrum and Restoration of Blurred Image

2012 ◽  
Vol 132 (2) ◽  
pp. 284-290
Author(s):  
Yuka Nagashima ◽  
Shigeru Omatu ◽  
Michifumi Yoshioka
Keyword(s):  
Amplitude Spectrum ◽  
Blurred Image

Improved 3D Scene Modeling for Image Registration in Change Detection

2019 ◽  
Vol 2019 (7) ◽  
pp. 465-1-465-7
Author(s):  
Sjors van Riel ◽  
Dennis van de Wouw ◽  
Peter de With
Keyword(s):  
Image Registration ◽  
Change Detection ◽  
Scene Modeling ◽  
3D Scene

Development of New Image Registration Techniques – A Research Framework

2018 ◽  
Vol 8 (3) ◽  
pp. 50
Author(s):  
Sindhu Madhuri G. ◽  
Indira Gandhi M P
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Digital Image Processing ◽  
Digital Image ◽  
Creative Thinking ◽  
Reference Image ◽  
Transformation Technique ◽  
Research Framework ◽  
Registration Process ◽  
Data Source

Image is a basic and fundamental data source for the digital image processing. This image data source is required to be processed into information or intelligence and further to knowledge levels where it is required to understand and migrate into knowledge economy systems. Image registration is one of such key and most important process already identified in the digital image processing domain. Image registration is a process of bringing the reference image and sensed image into a common co-ordinate system, and application of complex transformation techniques for necessary comparison of reference with sensed images obtained from different - views, times, spaces, etc., in order to extract the valuable information and intelligence embedded in them. Due to the complexity of overall image registration process, it is difficult to suggest a single transformation technique even for a specific application. In addition, it is highly impossible to suggest one single transformation technique for comparison of various sensed images with a reference image during the image registration process. This research gap calls for the development of new image registration techniques for the application of more than one transformation technique during the image registration process for the necessary comparisons with reference image & sensed images, those are obtained from the available heterogeneous sources or sensors, based on the requirement. In addition, it is a basic need to attempt for the measurement of effectiveness of the image registration process also. Therefore, a research framework is developed for image registration process and attempted for the measurement of its effectiveness also. This new research area is a novel idea, and is expected to emerge as a provision for the knowledge computations with creative thinking through the embedded intelligence extraction during the complex image registration process.

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

INTRODUCING TENSOR ANALYSIS FOR BIOMEDICAL IMAGE REGISTRATION

2018 ◽  
Author(s):  
Wilian Fiirst ◽  
José Montero ◽  
ROGER RESMINI ◽  
Anselmo Antunes Montenegro ◽  
Trueman McHenry ◽  
...  
Keyword(s):  
Image Registration ◽  
Tensor Analysis ◽  
Biomedical Image ◽  
Biomedical Image Registration
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