An improved feature matching method base on gradient constraint

2014 ◽  
Author(s):  
Lizhi Yin ◽  
Jian Hou ◽  
Wenxing Li
Keyword(s):  
Feature Matching ◽  
Matching Method ◽  
Gradient Constraint ◽  
Method Base

Identification of the Hook on Investment Casting Shell Line Based on Machine Vision

2012 ◽  
Vol 220-223 ◽  
pp. 1356-1361
Author(s):  
Xi Jie Tian ◽  
Jing Yu ◽  
Chang Chun Li
Keyword(s):  
Machine Vision ◽  
Investment Casting ◽  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Image Acquisition ◽  
Target Range ◽  
Target Area ◽  
Matching Method ◽  
Vertical Projection

In this paper, the idea identify the hook on investment casting shell line based on machine vision has been proposed. According to the characteristic of the hook, we do the image acquisition and preprocessing, we adopt Hough transform to narrow the target range, and find the target area based on the method combining the level projection and vertical projection, use feature matching method SIFT to do the image matching. Finally, we get the space information of the target area of the hook.

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

2021 ◽  
Vol 5 (4) ◽  
pp. 783-793
Author(s):  
Muhammad Muttabi Hudaya ◽  
Siti Saadah ◽  
Hendy Irawan
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Image Feature ◽  
Brute Force ◽  
K Nearest Neighbor ◽  
Matching Method ◽  
Average Precision ◽  
Detection Model ◽  
Matching Process

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

scholarly journals Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

2019 ◽  
Vol 9 (2) ◽  
pp. 309-314
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Local Features ◽  
Feature Descriptor ◽  
Matching Method ◽  
Multiple Images ◽  
Feature Sets ◽  
Precise Relationship

Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

R-K algorithm: A novel Dynamic Feature Matching Method of flotation froth

Measurement ◽  
2020 ◽  
Vol 156 ◽  
pp. 107581
Author(s):  
Ming Lu ◽  
Duan Liu ◽  
Yupeng Deng ◽  
Lianghong Wu ◽  
Yongfang Xie ◽  
...  
Keyword(s):  
Feature Matching ◽  
Dynamic Feature ◽  
Matching Method

A Feature Matching Method: Sparse Feature Tree

2006 ◽  
Vol 17 (5) ◽  
pp. 1026
Author(s):  
Ya-Qian ZHOU
Keyword(s):  
Feature Matching ◽  
Matching Method

Feature Matching Based on Triangle Guidance and Constraints

2018 ◽  
Vol 32 (08) ◽  
pp. 1855014 ◽  
Author(s):  
Hongmin Liu ◽  
Hongya Zhang ◽  
Zhiheng Wang ◽  
Yiming Zheng
Keyword(s):  
Feature Matching ◽  
High Accuracy ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
Matching Method ◽  
Shared Feature ◽  
Triangular Structure ◽  
Circle Center ◽  
Image Transformations

For images with distortions or repetitive patterns, the existing matching methods usually work well just on one of the two kinds of images. In this paper, we present novel triangle guidance and constraints (TGC)-based feature matching method, which can achieve good results on both kinds of images. We first extract stable matched feature points and combine these points into triangles as the initial matched triangles, and triangles combined by feature points are as the candidates to be matched. Then, triangle guidance based on the connection relationship via the shared feature point between the matched triangles and the candidates is defined to find the potential matching triangles. Triangle constraints, specially the location of a vertex relative to the inscribed circle center of the triangle, the scale represented by the ratio of corresponding side lengths of two matching triangles and the included angles between the sides of two triangles with connection relationship, are subsequently used to verify the potential matches and obtain the correct ones. Comparative experiments show that the proposed TGC can increase the number of the matched points with high accuracy under various image transformations, especially more effective on images with distortions or repetitive patterns due to the fact that the triangular structure are not only stable to image transformations but also provides more geometric constraints.

scholarly journals The Application and Accuracy of Feature Matching on Automated Cephalometric Superimposition

2019 ◽  
Author(s):  
Yiran Jiang ◽  
Guangying Song ◽  
Xiaonan Yu ◽  
Yuanbo Dou ◽  
Qingfeng Li ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Feature Matching ◽  
Academic Research ◽  
Cranial Base ◽  
Matching Method ◽  
Automated Method ◽  
Computer Aided ◽  
Registration Number ◽  
Pre Treatment ◽  
Maxilla And Mandible

Abstract Background: The aim of this study was to establish a computer-aided automated method for cephalometric superimposition and evaluate the accuracy of this method based on free-hand tracing. Methods: 28 pairs of pre-treatment (T1) and post-treatment (T2) cephalograms were selected. Structural superimpositions of the cranial base, maxilla and mandible were independently completed by three operators performing traditional hand tracing methods and by computerized automation using the feature matching method. To quantitatively evaluate the differences between the two methods, the manually superimposed patterns were digitized. In addition to registering automated and manual superimposition to digital T1 films, landmark distances between corresponding automated and manual T2 cephalometric landmarks were measured, and differences in manual superimposition among the operators were calculated. Results: The T2 landmark differences in hand tracing between the operators ranged from 0.61 mm to 1.65 mm for the three types of superimposition. There were no significant differences in accuracy between manual and automated superimposition (p > 0.05). Conclusions: Computer-aided cephalometric superimposition provides comparably accurate results to those of traditional hand tracing and will provide a powerful tool for academic research in the era of digital cephalograms and big data. Trial registration: The clinical trial has been registered in April 1st 2016, the registration number is ChiCTR1800017694, URL is: http://www.chictr.org.cn/showproj.aspx?proj=29144

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