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 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 A non-target structural displacement measurement method using advanced feature matching strategy

2019 ◽  
Vol 22 (16) ◽  
pp. 3461-3472 ◽  
Author(s):  
Chuan-Zhi Dong ◽  
F Necati Catbas
Keyword(s):  
Measurement Method ◽  
Feature Matching ◽  
Displacement Measurement ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Matching Strategy ◽  
Scale Invariant Feature ◽  
Visual Geometry

Most of the existing vision-based displacement measurement methods require manual speckles or targets to improve the measurement performance in non-stationary imagery environments. To minimize the use of manual speckles and targets, feature points regarded as virtual markers can be utilized for non-target measurement. In this study, an advanced feature matching strategy is presented, which replaces the handcrafted descriptors with learned descriptors called Visual Geometry Group, of the University of Oxford descriptors to achieve better performance. The feasibility and performance of the proposed method is verified by comparative studies with a laboratory experiment on a two-span bridge model and then with a field application on a railway bridge. The proposed approach of integrated use of Scale Invariant Feature Transform and Visual Geometry Group improved the measurement accuracy by about 24% when compared with the commonly used existing feature matching-based displacement measurement method using Scale Invariant Feature Transform feature and descriptor.

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 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.

The Study of Reduce the Mismatch Based on the SIFT Feature Matching

2012 ◽  
Vol 580 ◽  
pp. 378-382
Author(s):  
Xiao Yu Liu ◽  
Yan Piao ◽  
Lei Liu
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Space Rotation ◽  
Feature Transform ◽  
The Difference ◽  
Scale Invariant Feature ◽  
Keypoints Matching

The algorithm of SIFT (scale-invariant feature transform) feature matching is an international hotspot in the areas of the keypoints matching and target recognition at the present time. The algorithm is used in the image matching widely because of the good invariance of scale, illumination and space rotation .This paper proposes a new theory to reduce the mismatch—the theory to reduce the mismatch based on the main orientation of keypoints. This theory should firstly compute the grads of the main orientation of a couple of matched keypoints in the two images and the difference between them. Because the difference of the main orientation of matched keypoints should be much larger than the couples which are matched correctly, we can distinguish and reduce the mismatch through setting the proper threshold, and it can improve the accuracy of the SIFT algorithm greatly.

Invariant Features Tracking Based on Transiently Chaotic Neural Network for Markerless Augmented Reality

2011 ◽  
Vol 181-182 ◽  
pp. 37-42
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Neural Network ◽  
Augmented Reality ◽  
Image Matching ◽  
Feature Matching ◽  
Feature Point ◽  
Image Feature ◽  
Chaotic Neural Network ◽  
Scale Invariant ◽  
Chaotic Neural Networks ◽  
Invariant Features

Tracking and registration of camera and object is one of the most important issues in Augmented Reality (AR) systems. Markerless visual tracking technologies with image feature are used in many AR applications. Feature point based neural network image matching method has attracted considerable attention in recent years. This paper proposes an approach to feature point correspondence of image sequence based on transient chaotic neural networks. Rotation and scale invariant features are extracted from images firstly, and then transient chaotic neural network is used to perform global feature matching and perform the initialization phase of the tracking. Experimental results demonstrate the efficiency and the effectiveness of the proposed method.

scholarly journals LGS: LOCAL GEOMETRICAL STRUCTURE-BASED INTEREST POINT MATCHING FOR WIDE-BASELINE IMAGERY IN URBAN AREAS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 13-20
Author(s):  
M. Chen ◽  
Q. Zhu ◽  
S. Yan ◽  
Y. Zhao
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Feature Matching ◽  
Geometrical Structure ◽  
Consensus Algorithm ◽  
Interest Point ◽  
Interest Points ◽  
Point Matching ◽  
Viewpoint Change ◽  
Difficult Situations

<p><strong>Abstract.</strong> Feature matching is a fundamental technical issue in many applications of photogrammetry and remote sensing. Although recently developed local feature detectors and descriptors have contributed to the advancement of point matching, challenges remain with regard to urban area images that are characterized by large discrepancies in viewing angles. In this paper, we define a concept of local geometrical structure (LGS) and propose a novel feature matching method by exploring the LGS of interest points to specifically address difficult situations in matching points on wide-baseline urban area images. In this study, we first detect interest points from images using a popular detector and compute the LGS of each interest point. Then, the interest points are classified into three categories on the basis of LGS. Thereafter, a hierarchical matching framework that is robust to image viewpoint change is proposed to compute correspondences, in which different feature region computation methods, description methods, and matching strategies are designed for various types of interest points according to their LGS properties. Finally, random sample consensus algorithm based on fundamental matrix is applied to eliminate outliers. The proposed method can generate similar feature descriptors for corresponding interest points under large viewpoint variation even in discontinuous areas that benefit from the LGS-based adaptive feature region construction. Experimental results demonstrate that the proposed method provides significant improvements in correct match number and matching precision compared with other traditional matching methods for urban area wide-baseline images.</p>

scholarly journals A Novel Approach for Interest Point Detection via Laplacian-of-Bilateral Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Mingming Huang ◽  
Zhichun Mu ◽  
Hui Zeng ◽  
Hongbo Huang
Keyword(s):  
Bilateral Filter ◽  
Scale Space ◽  
Detection Algorithm ◽  
Interest Point ◽  
Interest Points ◽  
Strong Response ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Interest Point Detection ◽  
Point Detection

Scale-invariant feature transform (SIFT) algorithm, one of the most famous and popular interest point detectors, detects extrema by using difference-of-Gaussian (DoG) filter which is an approximation to the Laplacian-of-Gaussian (LoG) for improving speed. However, DoG filter has a strong response along edge, even if the location along the edge is poorly determined and therefore is unstable to small amounts of noise. In this paper, we propose a novel interest point detection algorithm, which detects scale space extrema by using a Laplacian-of-Bilateral (LoB) filter. The LoB filter, which is produced by Bilateral and Laplacian filter, can preserve edge characteristic by fully utilizing the information of intensity variety. Compared with the SIFT algorithm, our algorithm substantially improves the repeatability of detected interest points on a very challenging benchmark dataset, in which images were generated under different imaging conditions. Extensive experimental results show that the proposed approach is more robust to challenging problems such as illumination and viewpoint changes, especially when encountering large illumination change.

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