A Edge Feature Matching Algorithm Based on Evolutionary Strategies and Least Trimmed Square Hausdorff Distance

2006 ◽  
pp. 475-478
Author(s):  
Li JunShan ◽  
Han XianFeng ◽  
Li Long ◽  
Li Kun ◽  
Li JianJun
Keyword(s):  
Hausdorff Distance ◽  
Feature Matching ◽  
Evolutionary Strategies ◽  
Matching Algorithm ◽  
Edge Feature

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

Feature Matching Algorithm of Trees in Natrual Scenery

2009 ◽  
Author(s):  
Yan Yuqin ◽  
Diao Xunlin ◽  
He Shujuan ◽  
Cheng Lin ◽  
Ma Lvzhou ◽  
...  
Keyword(s):  
Feature Matching ◽  
Matching Algorithm

A monocular visual measurement system for UAV probe-and-drogue autonomous aerial refueling

2018 ◽  
Vol 11 (2) ◽  
pp. 166-180 ◽  
Author(s):  
Long Xin ◽  
Delin Luo ◽  
Han Li
Keyword(s):  
Measurement System ◽  
Feature Matching ◽  
Perspective Transformation ◽  
High Accuracy ◽  
Noise Elimination ◽  
Aerial Refueling ◽  
Visual Measurement ◽  
Matching Algorithm ◽  
Content Type ◽  
Elimination Algorithm

PurposeThe purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling (AAR) for unmanned aerial vehicle, which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.Design/methodology/approachMethods and techniques for marker detection, feature matching and pose estimation have been designed and implemented in the visual measurement system.FindingsThe simple blob detection (SBD) method is adopted, which outperforms the Laplacian of Gaussian method. And a novel noise-elimination algorithm is proposed for excluding the noise points. Besides, a novel feature matching algorithm based on perspective transformation is proposed. Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.Practical implicationsThe visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.Originality/valueThe SBD method is used to detect the features and a novel noise-elimination algorithm is proposed. Besides, a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

A Tree-Structured Feature Matching Algorithm

2015 ◽  
pp. 190-200 ◽  
Author(s):  
Xiongwei Sun ◽  
Xiubo Ma ◽  
Lei Chen ◽  
Li Wan ◽  
Xinhua Zeng
Keyword(s):  
Feature Matching ◽  
Matching Algorithm

Improved KAZE feature matching algorithm with viewpoint change robustness

2016 ◽  
Vol 24 (3) ◽  
pp. 616-625
Author(s):  
耿则勋 GENG Ze-xun ◽  
徐志军 XU Zhi-jun ◽  
卢兰鑫 LU Lan-xin ◽  
沈忱 SHEN Chen ◽  
曾德佳 ZENG De-jia
Keyword(s):  
Feature Matching ◽  
Matching Algorithm ◽  
Viewpoint Change ◽  
Kaze Feature

scholarly journals EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching

2019 ◽  
Vol 11 (24) ◽  
pp. 3026
Author(s):  
Bin Fang ◽  
Kun Yu ◽  
Jie Ma ◽  
Pei An
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Maximum Clique ◽  
Feature Space ◽  
Multispectral Image ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Edge Feature

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.

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