scholarly journals An Image Matching Method Based on SIFT Feature Extraction and FLANN Search Algorithm Improvement

2021 ◽  
Vol 2037 (1) ◽  
pp. 012122
Author(s):  
Shigang Wang ◽  
Zhenjin Guo ◽  
Yang Liu
Keyword(s):  
Feature Extraction ◽  
Image Matching ◽  
Search Algorithm ◽  
Matching Method ◽  
Sift Feature

scholarly journals SIFT Feature Extraction Algorithm for Image in DCT Domain

2013 ◽  
Vol 347-350 ◽  
pp. 2963-2967 ◽  
Author(s):  
Zhen Wu ◽  
Zhe Xu ◽  
Rui Nian Zhang ◽  
Shao Mei Li
Keyword(s):  
Feature Extraction ◽  
Image Matching ◽  
Coefficient Matrix ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Sift Feature ◽  
Dct Domain ◽  
Extraction Algorithm ◽  
Dct Coefficients ◽  
The Relationship

Image feature extraction is an important technology in image matching and retrieval. For the problem of high computational complexity of spatial domain image feature extraction using the SIFT algorithm, and by studying the relationship between DCT coefficient matrix and image, the paper designed the DCT coefficients reduced matrix of image and proposed the algorithm of SIFT feature extraction in DCT domain reduced image. Experiments showed that with the low loss of accuracy in image matching and retrieval, the method proposed can significantly improve the computational efficiency of feature extraction.

An Image Matching Method Based on SIFT Feature

2012 ◽  
Vol 170-173 ◽  
pp. 2855-2859
Author(s):  
Zhao Ming Shi ◽  
Bo Ying Geng ◽  
Zhong Hong Wu ◽  
Yin Wen Dong
Keyword(s):  
Mahalanobis Distance ◽  
Image Matching ◽  
Nearest Neighbor ◽  
High Accuracy ◽  
Similarity Measurement ◽  
Matching Method ◽  
Sift Algorithm ◽  
Sift Feature ◽  
Nearest Neighbor Rule ◽  
Sift Features

Aiming at problems about repeat matching and wrong matching appeared when traditional SIFT algorithm was used in image matching, an image matching method based on SIFT feature was put forward. Firstly, SIFT features were extracted by traditional SIFT algorithm and candidate matching point pairs were obtained by the nearest neighbor rule. Secondly, lateral matching method was used to remove repeat matched dot-pairs. Finally, Mahalanobis distance as a similarity measurement was used to remove wrong matched dot-pairs. Experiment shows this method can achieve image matching effectively with high accuracy.

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.

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