scholarly journals Feature Matching Combining Radiometric and Geometric Characteristics of Images, Applied to Oblique- and Nadir-Looking Visible and TIR Sensors of UAV Imagery

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4587
Author(s):  
Hyoseon Jang ◽  
Sangkyun Kim ◽  
Suhong Yoo ◽  
Soohee Han ◽  
Hong-Gyoo Sohn
Keyword(s):  
Image Matching ◽  
Information Needs ◽  
Feature Matching ◽  
Phase Congruency ◽  
Scale Invariant ◽  
Surface Information ◽  
Speeded Up Robust Features ◽  
Image Pairs ◽  
3D Information ◽  
Matching Techniques

A large amount of information needs to be identified and produced during the process of promoting projects of interest. Thermal infrared (TIR) images are extensively used because they can provide information that cannot be extracted from visible images. In particular, TIR oblique images facilitate the acquisition of information of a building’s facade that is challenging to obtain from a nadir image. When a TIR oblique image and the 3D information acquired from conventional visible nadir imagery are combined, a great synergy for identifying surface information can be created. However, it is an onerous task to match common points in the images. In this study, a robust matching method of image pairs combined with different wavelengths and geometries (i.e., visible nadir-looking vs. TIR oblique, and visible oblique vs. TIR nadir-looking) is proposed. Three main processes of phase congruency, histogram matching, and Image Matching by Affine Simulation (IMAS) were adjusted to accommodate the radiometric and geometric differences of matched image pairs. The method was applied to Unmanned Aerial Vehicle (UAV) images of building and non-building areas. The results were compared with frequently used matching techniques, such as scale-invariant feature transform (SIFT), speeded-up robust features (SURF), synthetic aperture radar–SIFT (SAR–SIFT), and Affine SIFT (ASIFT). The method outperforms other matching methods in root mean square error (RMSE) and matching performance (matched and not matched). The proposed method is believed to be a reliable solution for pinpointing surface information through image matching with different geometries obtained via TIR and visible sensors.

scholarly journals YOLOv3-Based Matching Approach for Roof Region Detection from Drone Images

2021 ◽  
Vol 13 (1) ◽  
pp. 127
Author(s):  
Chia-Cheng Yeh ◽  
Yang-Lang Chang ◽  
Mohammad Alkhaleefah ◽  
Pai-Hui Hsu ◽  
Weiyong Eng ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Matching ◽  
Feature Matching ◽  
Computational Cost ◽  
Similarity Index ◽  
Large Data ◽  
Residential Areas ◽  
Search Range ◽  
Scale Invariant ◽  
Speeded Up Robust Features

Due to the large data volume, the UAV image stitching and matching suffers from high computational cost. The traditional feature extraction algorithms—such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST Rotated BRIEF (ORB)—require heavy computation to extract and describe features in high-resolution UAV images. To overcome this issue, You Only Look Once version 3 (YOLOv3) combined with the traditional feature point matching algorithms is utilized to extract descriptive features from the drone dataset of residential areas for roof detection. Unlike the traditional feature extraction algorithms, YOLOv3 performs the feature extraction solely on the proposed candidate regions instead of the entire image, thus the complexity of the image matching is reduced significantly. Then, all the extracted features are fed into Structural Similarity Index Measure (SSIM) to identify the corresponding roof region pair between consecutive image sequences. In addition, the candidate corresponding roof pair by our architecture serves as the coarse matching region pair and limits the search range of features matching to only the detected roof region. This further improves the feature matching consistency and reduces the chances of wrong feature matching. Analytical results show that the proposed method is 13× faster than the traditional image matching methods with comparable performance.

scholarly journals ROBUST LOW-ALTITUDE IMAGE MATCHING BASED ON LOCAL REGION CONSTRAINT AND FEATURE SIMILARITY CONFIDENCE

2016 ◽  
Vol III-3 ◽  
pp. 19-26
Author(s):  
Min Chen ◽  
Qing Zhu ◽  
Shengzhi Huang ◽  
Han Hu ◽  
Jingxue Wang
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Local Region ◽  
Geometric Transformation ◽  
Interest Point ◽  
Interest Points ◽  
Scale Invariant ◽  
Feature Similarity ◽  
Low Altitude ◽  
Matching Strategy

Improving the matching reliability of low-altitude images is one of the most challenging issues in recent years, particularly for images with large viewpoint variation. In this study, an approach for low-altitude remote sensing image matching that is robust to the geometric transformation caused by viewpoint change is proposed. First, multiresolution local regions are extracted from the images and each local region is normalized to a circular area based on a transformation. Second, interest points are detected and clustered into local regions. The feature area of each interest point is determined under the constraint of the local region which the point belongs to. Then, a descriptor is computed for each interest point by using the classical scale invariant feature transform (SIFT). Finally, a feature matching strategy is proposed on the basis of feature similarity confidence to obtain reliable matches. Experimental results show that the proposed method provides significant improvements in the number of correct matches compared with other traditional methods.

scholarly journals An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques

2021 ◽  
Vol 11 (11) ◽  
pp. 5225
Author(s):  
Nuha Odeh ◽  
Anas Toma ◽  
Falah Mohammed ◽  
Yousef Dama ◽  
Farah Oshaibi ◽  
...  
Keyword(s):  
Image Matching ◽  
Human Error ◽  
Low Cost ◽  
Blood Type ◽  
Scale Invariant ◽  
Efficient System ◽  
Emergency Situations ◽  
Long Time ◽  
Speed Up Robust Feature ◽  
Matching Techniques

This paper presents a fast and accurate system to determine the type of blood automatically based on image processing. Blood type determination is important in emergency situations, where there is a need for blood transfusion to save lives. The traditional blood determination techniques are performed manually by a specialist in medical labs, where the result requires a long time or may be affected by human error. This may cause serious consequences or even endanger people’s lives. The proposed approach performs blood determination in real-time with low cost using any available mobile device equipped with a camera. A total of 500 blood samples were processed in this study using different image matching techniques including oriented fast and rotated brief (ORB), scale invariant feature transform (SIFT), and speed-up robust feature (SURF). The evaluation results show that our proposed system, which adopts the ORB algorithm, is the fastest and the most accurate among the state-of-the-art systems. It can achieve an accuracy of 99.6% in an average time of 250 ms.

scholarly journals ROBUST LOW-ALTITUDE IMAGE MATCHING BASED ON LOCAL REGION CONSTRAINT AND FEATURE SIMILARITY CONFIDENCE

2016 ◽  
Vol III-3 ◽  
pp. 19-26
Author(s):  
Min Chen ◽  
Qing Zhu ◽  
Shengzhi Huang ◽  
Han Hu ◽  
Jingxue Wang
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Local Region ◽  
Geometric Transformation ◽  
Interest Point ◽  
Interest Points ◽  
Scale Invariant ◽  
Feature Similarity ◽  
Low Altitude ◽  
Matching Strategy

Improving the matching reliability of low-altitude images is one of the most challenging issues in recent years, particularly for images with large viewpoint variation. In this study, an approach for low-altitude remote sensing image matching that is robust to the geometric transformation caused by viewpoint change is proposed. First, multiresolution local regions are extracted from the images and each local region is normalized to a circular area based on a transformation. Second, interest points are detected and clustered into local regions. The feature area of each interest point is determined under the constraint of the local region which the point belongs to. Then, a descriptor is computed for each interest point by using the classical scale invariant feature transform (SIFT). Finally, a feature matching strategy is proposed on the basis of feature similarity confidence to obtain reliable matches. Experimental results show that the proposed method provides significant improvements in the number of correct matches compared with other traditional methods.

scholarly journals Portrayal Matching Algorithm By using Sift

2020 ◽  
Vol 9 (1) ◽  
pp. 2711-2713
Keyword(s):  
Image Matching ◽  
True Positive ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Image Identification ◽  
Invariant Feature ◽  
Feature Transform ◽  
Image Pairs ◽  
Matching Criteria ◽  
Scale Invariant Feature

Image identification and matching is one of the very difficult assignment in different areas of mainframe vie w. Scale-Invariant Feature Transform is an algorithm to perceive and represent specific features in portryals to further use them as an image matching criteria. In this paper, the extracted SIFT matching features are against various image distortions such as rotation, scaling, fisheye and motion distortion are evaluated and false and true positive rates for a large number of image pairs are calculated and presented.

scholarly journals A MATCHING METHOD TO REDUCE THE INFLUENCE OF SAR GEOMETRIC DEFORMATION

2018 ◽  
Vol XLII-3 ◽  
pp. 355-358
Author(s):  
C. Gao ◽  
G. Xue
Keyword(s):  
Image Matching ◽  
Cross Correlation ◽  
Geometric Distortion ◽  
Control Points ◽  
Sar Image ◽  
Speeded Up Robust Features ◽  
Geometric Deformation ◽  
Sparse Control ◽  
Image Pairs ◽  
The Impact

There are large geometrical deformations in SAR image, including foreshortening, layover, shade，which leads to SAR Image matching with low accuracy. Especially in complex terrain area, the control points are difficult to obtain, and the matching is difficult to achieve. Considering the impact of geometric distortions in SAR image pairs, a matching algorithm with a combination of speeded up robust features (SURF) and summed of normalize cross correlation (SNCC) was proposed, which can avoid the influence of SAR geometric deformation. Firstly, SURF algorithm was utilized to predict the search area. Then the matching point pairs was selected based on summed of normalized cross correlation. Finally, false match points were eliminated by the bidirectional consistency. SURF algorithm can control the range of matching points, and the matching points extracted from the deformation area are eliminated, and the matching points with stable and even distribution are obtained. The experimental results demonstrated that the proposed algorithm had high precision, and can effectively avoid the effect of geometric distortion on SAR image matching. Meet accuracy requirements of the block adjustment with sparse control points.

scholarly journals RESEARCH PROGRESS ON FEATURE MATCHING OF SAR AND OPTICAL IMAGES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 77-82 ◽  
Author(s):  
H. Qian ◽  
J. W. Yue ◽  
M. Chen
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Optical Image ◽  
Research Progress ◽  
Phase Congruency ◽  
Matching Method ◽  
Sift Algorithm ◽  
Matching Problems ◽  
Optical Images ◽  
Edge Features

Abstract. Before obtaining information and identifying ground target from images, image matching is necessary. However, problems of strong pixel noise interference and nonlinear gray scale differences in synthetic aperture radar image still exist. Feature matching becomes a kind possible solution. To learn the research progress of SAR and optical image matching, as well as finding solutions for above matching problems, a summary for feature matching with SAR and optical image is indispensable. By listing three typical methods below, we can discuss and compare how researchers improve and innovate methods for feature matching from different angles in matching process. First method is feature matching method proposed by CHEN Min et. It uses phase congruency method to detect point features. Feature descriptors are based on gaussian-gamma-shaped edge strength maps instead of original images. This method combines both edge features and point features to reach a match target. The second one is SAR-SIFT algorithm of F. Dellinger et. This kind of method is based on improvement of sift algorithm. It proposes a SAR-Harris method and also a calculation method for features descriptors named gradient by ratio. Thirdly, it is feature matching method proposed by Yu Qiuze et. By using edge features of image and improvement of hausdorff distance for similarity measure, it applies genetic algorithm to accelerate matching search process to complete matching tasks. Those methods are implemented by using python programs, and are compared by some indexes. Experimental data used multiple sets of terrasar and optical image pairs of different resolutions. To some extent, the results demonstrate that all three kinds of feature methods can improve the matching effect between SAR and optical images. It can be easier to reach match purposes of SAR and optical images by using image edge features, while such methods are too dependent on the edge features.

Vehicle Logo Recognition Based on Local Feature Descriptor

2012 ◽  
Vol 263-266 ◽  
pp. 2418-2421
Author(s):  
Sheng Ke Wang ◽  
Lili Liu ◽  
Xiaowei Xu
Keyword(s):  
Feature Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Feature Descriptor ◽  
Training Set ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Logo Recognition ◽  
Vehicle Logo Recognition ◽  
Scale Invariant Feature

In this paper, we present a comparison of the scale-invariant feature transforms (SIFT)-based feature-matching scheme and the speeded up robust features (SURF)-based feature-matching scheme in the field of vehicle logo recognition. We capture a set of logo images which are varied in illumination, blur, scale, and rotation. Six kinds of vehicle logo training set are formed using 25 images in average and the rest images are used to form the testing set. The Logo Recognition system that we programmed indicates a high recognition rate of the same kind of query images through adjusting different parameters.

Sign in / Sign up

Export Citation Format

Share Document