scholarly journals Hierarchical Point Matching Method Based on Triangulation Constraint and Propagation

2020 ◽  
Vol 9 (6) ◽  
pp. 347
Author(s):  
Jingxue Wang ◽  
Ning Zhang ◽  
Xiangqian Wu ◽  
Weixi Wang
Keyword(s):  
Image Matching ◽  
Three Dimensional ◽  
Similarity Score ◽  
Reference Image ◽  
Range Image ◽  
Matching Method ◽  
Point Matching ◽  
Second Stage ◽  
Edge Matching ◽  
Close Range

Reliable image matching is the basis of image-based three-dimensional (3D) reconstruction. This study presents a quasi-dense matching method based on triangulation constraint and propagation as applied to different types of close-range image matching, such as illumination change, large viewpoint, and scale change. The method begins from a set of sparse matched points that are used to construct an initial Delaunay triangulation. Edge-to-edge matching propagation is then conducted for the point matching. Two types of matching primitives from the edges of triangles with areas larger than a given threshold in the reference image, that is, the midpoints of edges and the intersections between the edges and extracted line segments, are used for the matching. A hierarchical matching strategy is adopted for the above-mentioned primitive matching. The points that cannot be matched in the first stage, specifically those that failed in a gradient orientation descriptor similarity constraint, are further matched in the second stage. The second stage combines the descriptor and the Mahalanobis distance constraints, and the optimal matching subpixel is determined according to an overall similarity score defined for the multiple constraints with different weights. Subsequently, the triangulation is updated using the newly matched points, and the aforementioned matching is repeated iteratively until no new matching points are generated. Twelve sets of close-range images are considered for the experiment. Results reveal that the proposed method has high robustness for different images and can obtain reliable matching results.

The three-dimensional kinematics analysis of the shoulder joint using the image matching method with image correlation

2016 ◽  
Vol 2016 (0) ◽  
pp. S0220204
Author(s):  
健 下戸 ◽  
Takeshi SHIMOTO ◽  
Yoshitaka SHIRAISHI ◽  
Yifeng WANG ◽  
Satoru IKEBE ◽  
...  
Keyword(s):  
Shoulder Joint ◽  
Image Matching ◽  
Three Dimensional ◽  
Image Correlation ◽  
Kinematics Analysis ◽  
Matching Method

Research on image matching method of big data image of three-dimensional reconstruction

2015 ◽  
Author(s):  
Chunsen Zhang ◽  
Zhenguo Qiu ◽  
Shihuan Zhu ◽  
Xiqi Wang ◽  
Xiaolei Xu ◽  
...  
Keyword(s):  
Big Data ◽  
Image Matching ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Matching Method ◽  
Dimensional Reconstruction

scholarly journals Exploiting High Geopositioning Accuracy of SAR Data to Obtain Accurate Geometric Orientation of Optical Satellite Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3535
Author(s):  
Zhongli Fan ◽  
Li Zhang ◽  
Yuxuan Liu ◽  
Qingdong Wang ◽  
Sisi Zlatanova
Keyword(s):  
Satellite Imagery ◽  
Image Matching ◽  
Satellite Images ◽  
Three Dimensional ◽  
Phase Correlation ◽  
Feature Descriptor ◽  
Matching Method ◽  
Reference Images ◽  
Sar Data ◽  
Optical Satellite Images

Accurate geopositioning of optical satellite imagery is a fundamental step for many photogrammetric applications. Considering the imaging principle and data processing manner, SAR satellites can achieve high geopositioning accuracy. Therefore, SAR data can be a reliable source for providing control information in the orientation of optical satellite images. This paper proposes a practical solution for an accurate orientation of optical satellite images using SAR reference images to take advantage of the merits of SAR data. Firstly, we propose an accurate and robust multimodal image matching method to match the SAR and optical satellite images. This approach includes the development of a new structural-based multimodal applicable feature descriptor that employs angle-weighted oriented gradients (AWOGs) and the utilization of a three-dimensional phase correlation similarity measure. Secondly, we put forward a general optical satellite imagery orientation framework based on multiple SAR reference images, which uses the matches of the SAR and optical satellite images as virtual control points. A large number of experiments not only demonstrate the superiority of the proposed matching method compared to the state-of-the-art methods but also prove the effectiveness of the proposed orientation framework. In particular, the matching performance is improved by about 17% compared with the latest multimodal image matching method, namely, CFOG, and the geopositioning accuracy of optical satellite images is improved, from more than 200 to around 8 m.

scholarly journals Particle Swarm Image Matching on Epipolar Line

2014 ◽  
Vol XL-4 ◽  
pp. 87-91
Author(s):  
J.-S. Hsia
Keyword(s):  
Image Matching ◽  
Particle Swarm ◽  
Window Size ◽  
Pso Algorithm ◽  
Least Square ◽  
Image Point ◽  
Reference Image ◽  
Epipolar Line ◽  
Matching Method ◽  
The Difference

This paper presents a method for determining the 3D position of an image point on a reference image using particle swarm optimization (PSO) to search the height (Z value) that gives the biggest Normalized Cross Correlation (NCC) coefficient. The searching area is in the surrounding of the height of the image point. The NCC coefficient evaluates the similarity with the image point and a corresponding point on an epipolar line in the search image. The position of corresponding image point on the epipolar line is determined by the height point on a sloping line locus. The PSO algorithm starts with a swarm of random particles. The position of each particle is a potential solution in the problem space. Each particle is given a randomized velocity and attracted toward the location of the best fitness. The position of each particle is iteratively modified by adding a newly computed velocity to its current position. The velocity is updated by three factors which are two attractions from local best position and global best position, two strengths of the attractions, and two uniform random numbers for each attraction. The iteration will stop when the current solution is convergent. The time of computation is highly related to the range of height and the interval of height enumeration when the approach to find a corresponding image point of an image point on a reference image is based on the height enumeration along sloping line locus. The precision of results can be improved by decreasing the interval of height enumeration. This shows the limitation of the enumeration method in the efficiency and accuracy. The issue is overcome by a method of using PSO algorithm. The proposed method using different parameters such as the size of image window, the number of particles, and the size of the height searching range has been applied to aerial stereo images. The accuracy of tested results is evaluated on the base of the comparison to the reference data from the results of least-square matching being manually given initial points. The evaluation result shows that tested results has given a solution to a level of less than 1 centimetre without using refined image matching method. The same level of accuracy can reach even when the searching range is bigger than 90 meters. But the difference of image window size may lead to the change of the matching result. And, without the procedures of both coarse-to-fine hierarchical solution and refined image matching method, the algorithm still can give the same accuracy level of least-square image matching resulting. This method also shows its ability to give reasonable matching results without manual assistance.

scholarly journals Three-dimensional motion analysis of the patellar component in total knee arthroplasty by the image matching method using image correlations

2014 ◽  
Vol 32 (5) ◽  
pp. 619-626 ◽  
Author(s):  
Masami Ishimaru ◽  
Yoshitaka Shiraishi ◽  
Satoru Ikebe ◽  
Hidehiko Higaki ◽  
Kazunori Hino ◽  
...  
Keyword(s):  
Total Knee Arthroplasty ◽  
Motion Analysis ◽  
Knee Arthroplasty ◽  
Image Matching ◽  
Three Dimensional ◽  
Matching Method ◽  
Patellar Component ◽  
Total Knee

Multiple close-range image matching based on a self-adaptive triangle constraint

2010 ◽  
Vol 25 (132) ◽  
pp. 437-453 ◽  
Author(s):  
Qing Zhu ◽  
Yunsheng Zhang ◽  
Bo Wu ◽  
Yeting Zhang
Keyword(s):  
Image Matching ◽  
Range Image ◽  
Close Range ◽  
Self Adaptive

scholarly journals SIFT Saliency Analysis for Matching Repetitive Structures

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Yang ◽  
Xiaopeng Hu ◽  
Lijuan Xu ◽  
Fan Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Image Matching ◽  
Feature Matching ◽  
Region Of Interest ◽  
Reference Image ◽  
Matching Method ◽  
Feature Saliency ◽  
Saliency Analysis ◽  
Feature Stability

The ambiguity resulting from repetitive structures in a scene presents a major challenge for image matching. This paper proposes a matching method based on SIFT feature saliency analysis to achieve robust feature matching between images with repetitive structures. The feature saliency within the reference image is estimated by analyzing feature stability and dissimilarity via Monte-Carlo simulation. In the proposed method, feature matching is performed only within the region of interest to reduce the ambiguity caused by repetitive structures. The experimental results demonstrate the efficiency and robustness of the proposed method, especially in the presence of respective structures.

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