Neonatal Fundus Image Registration and Mosaic Using Improved Speeded Up Robust Features Based on Shannon Entropy

2021 ◽  
Author(s):  
Hongyang Jiang ◽  
Mengdi Gao ◽  
Kang Yang ◽  
Dongdong Zhang ◽  
He Ma ◽  
...  
Keyword(s):  
Image Registration ◽  
Shannon Entropy ◽  
Fundus Image ◽  
Speeded Up Robust Features

scholarly journals Fast Image Registration for Spacecraft Autonomous Navigation Using Natural Landmarks

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yun-Hua Wu ◽  
Lin-Lin Ge ◽  
Feng Wang ◽  
Bing Hua ◽  
Zhi-Ming Chen ◽  
...  
Keyword(s):  
Image Registration ◽  
Autonomous Navigation ◽  
Computational Cost ◽  
Segmentation Algorithm ◽  
Speeded Up Robust Features ◽  
Speed Up ◽  
Random Sample Consensus ◽  
Invariant Properties ◽  
Low Computational Cost ◽  
Ransac Algorithm

In order to satisfy the real-time requirement of spacecraft autonomous navigation using natural landmarks, a novel algorithm called CSA-SURF (chessboard segmentation algorithm and speeded up robust features) is proposed to improve the speed without loss of repeatability performance of image registration progress. It is a combination of chessboard segmentation algorithm and SURF. Here, SURF is used to extract the features from satellite images because of its scale- and rotation-invariant properties and low computational cost. CSA is based on image segmentation technology, aiming to find representative blocks, which will be allocated to different tasks to speed up the image registration progress. To illustrate the advantages of the proposed algorithm, PCA-SURF, which is the combination of principle component analysis and SURF, is also analyzed in this paper for comparison. Furthermore, random sample consensus (RANSAC) algorithm is applied to eliminate the false matches for further accuracy improvement. The simulation results show that the proposed strategy obtains good results, especially in scaling and rotation variation. Besides, CSA-SURF decreased 50% of the time in extraction and 90% of the time in matching without losing the repeatability performance by comparing with SURF algorithm. The proposed method has been demonstrated as an alternative way for image registration of spacecraft autonomous navigation using natural landmarks.

A Improved Infrared and Visible Images Matching Based on SURF

2013 ◽  
Vol 325-326 ◽  
pp. 1637-1640
Author(s):  
Dong Mei Li ◽  
Jing Lei Zhang
Keyword(s):  
Image Registration ◽  
Euclidean Distance ◽  
Experimental Results ◽  
Interest Points ◽  
Speeded Up Robust Features ◽  
Visible Images ◽  
Data Points

Images matching is the basis of image registration. For their difference, a improved SURF(speeded up robust features) algorithm was proposed for the infrared and visible images matching. Firstly, edges were extracted from the images to improve the similarity of infrared and visible images. Then SURF algorithm was used to detect interest points, and the dimension of the point descriptor was 64. Finally, found the matching points by Euclidean distance. Experimental results show that some invalid data points were eliminated.

Fundus image registration for vestibularis research

2010 ◽  
Author(s):  
Vamsi K. Ithapu ◽  
Armin Fritsche ◽  
Ariane Oppelt ◽  
Martin Westhofen ◽  
Thomas M. Deserno
Keyword(s):  
Image Registration ◽  
Fundus Image

scholarly journals UAV AND LIDAR IMAGE REGISTRATION: A SURF-BASED APPROACH FOR GROUND CONTROL POINTS SELECTION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 413-418 ◽  
Author(s):  
B. Kalantar ◽  
N. Ueda ◽  
H. A. H. Al-Najjar ◽  
H. Moayedi ◽  
A. A. Halin ◽  
...  
Keyword(s):  
Image Registration ◽  
Water Resource Management ◽  
Feature Matching ◽  
Image Data ◽  
Ground Control ◽  
Control Points ◽  
Speeded Up Robust Features ◽  
Ground Control Points ◽  
Edge Detectors ◽  
Edge Features

<p><strong>Abstract.</strong> Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results.</p>

Two-stage Retinal Fundus Image Registration Based on Local Blood Structure Features

2012 ◽  
Vol 41 (10) ◽  
pp. 1236-1241
Author(s):  
沈奔 SHEN Ben ◽  
张东波 ZHANG Dong-bo ◽  
彭英辉 PENG Ying-hui
Keyword(s):  
Image Registration ◽  
Fundus Image ◽  
Two Stage ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Local descriptor for retinal fundus image registration

2020 ◽  
Vol 14 (4) ◽  
pp. 144-153
Author(s):  
Roziana Ramli ◽  
Mohd Yamani Idna Idris ◽  
Khairunnisa Hasikin ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab ◽  
...  
Keyword(s):  
Image Registration ◽  
Fundus Image ◽  
Local Descriptor ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Comparison between Three Registration Methods in the Case of Non-Georeferenced Close Range of Multispectral Images

2021 ◽  
Vol 13 (3) ◽  
pp. 396
Author(s):  
Claudio Ignacio Fernández ◽  
Ata Haddadi ◽  
Brigitte Leblon ◽  
Jinfei Wang ◽  
Keri Wang
Keyword(s):  
Image Registration ◽  
Detection System ◽  
Projective Transformation ◽  
Multispectral Images ◽  
Affine Transformations ◽  
Blue Band ◽  
Greenhouse Production ◽  
Registration Method ◽  
Speeded Up Robust Features ◽  
Close Range

Cucumber powdery mildew, which is caused by Podosphaera xanthii, is a major disease that has a significant economic impact in cucumber greenhouse production. It is necessary to develop a non-invasive fast detection system for that disease. Such a system will use multispectral imagery acquired at a close range with a camera attached to a mobile cart’s mechanic extension. This study evaluated three image registration methods applied to non-georeferenced multispectral images acquired at close range over greenhouse cucumber plants with a MicaSense® RedEdge camera. The detection of matching points was performed using Speeded-Up Robust Features (SURF), and outliers matching points were removed using the M-estimator Sample Consensus (MSAC) algorithm. Three geometric transformations (affine, similarity, and projective) were considered in the registration process. For each transformation, we mapped the matching points of the blue, green, red, and NIR band images into the red-edge band space and computed the root mean square error (RMSE in pixel) to estimate the accuracy of each image registration. Our results achieved an RMSE of less than 1 pixel with the similarity and affine transformations and of less than 2 pixels with the projective transformation, whatever the band image. We determined that the best image registration method corresponded to the affine transformation because the RMSE is less than 1 pixel and the RMSEs have a Gaussian distribution for all of the bands, but the blue band.

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