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.

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.

An Image Mosaic Method Based on SIFT Feature Matching

2012 ◽  
Vol 433-440 ◽  
pp. 5420-5424 ◽  
Author(s):  
Li Jing Cao ◽  
Ming Lv
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Image Mosaic ◽  
Sift Feature ◽  
Mosaic Image ◽  
Blending Method ◽  
Orientation Scale ◽  
Sift Features ◽  
Human Eyes ◽  
Arbitrary Matching

This paper concerns the problem of image mosaic. An image matching method based on SIFT features and an image blending method of improved Hat function are proposed in the paper. SIFT feature is local feature and keeps invariant to scale zoom, rotation and illumination. It is also insensitive to noise, view point changing and so on. Because of this our method is insensitive to orientation, scale and illumination of input images, so it’s possible to accomplish image mosaic between arbitrary matching images and the Hat function blending algorithm with global intensity revise makes the mosaic image accepted by human eyes.

Reference Point-Based SIFT Feature Matching

2014 ◽  
Vol 543-547 ◽  
pp. 2670-2673
Author(s):  
Lei Cao ◽  
Di Liao ◽  
Bin Dang Xue
Keyword(s):  
Reference Point ◽  
Image Matching ◽  
Euclidean Distance ◽  
Feature Matching ◽  
Experimental Results ◽  
Feature Descriptor ◽  
Matching Algorithm ◽  
Sift Feature ◽  
Sift Features ◽  
Suitable Reference

Aiming to solve the high computational and time consuming problem in SIFT feature matching, this paper presents an improved SIFT feature matching algorithm based on reference point. The algorithm starts from selecting a suitable reference point in the feature descriptor space when SIFT features are extracted. In the feature matching stage, this paper uses the Euclidean distance between descriptor vectors of the feature point to be matched and the reference point to make a fast filtration which removes most of the features that could not be matched. For the remaining SIFT features, Best-bin-first (BBF) algrithm is utilized to obtain precise matches. Experimental results demonstrate that the proposed matching algorithm achieves good effectiveness in image matching, and takes only about 60 percent of the time that the traditional matching algorithm takes.

An Image Registration Algorithm Based on Improved SIFT Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3411-3415 ◽  
Author(s):  
Yin Wen Dong ◽  
Luan Wan ◽  
Zhao Ming Shi ◽  
Ming Lei Zhu
Keyword(s):  
Image Registration ◽  
Nearest Neighbor ◽  
Interpolation Method ◽  
Space Structure ◽  
Cruise Missile ◽  
Feature Descriptor ◽  
Sift Algorithm ◽  
Sift Feature ◽  
Registration Algorithm ◽  
Image Registration Algorithm

Aiming at there are long matching time and many wrong matching in the traditional SIFT algorithm, An image registration algorithm based on improved SIFT feature is put forward. First of all, through setting the number of extreme points in the feature point detection, feature points is found according to the DOG space structure from coarse to fine, and the improved SIFT feature descriptor generation algorithm is used. The preliminary matched point pairs are obtained by the nearest neighbor matching criterion, and the bilateral matching method is used for screening the preliminary matched point. Then, the second matching will be done by the similar measurement method based on mahalanobis distance, and RANSAC algorithm is used to calculate the affine transform model. Finally, the transformed image is resampled and interpolated through the bilinear interpolation method. Experimental results show that the algorithm can realize image registration effectively. Image registration technique is an important research content in computer vision and image processing in the, which are widely used in vehicle matching navigation and positioning, cruise missile terminal guidance, target tracking and recognition, image mosaic[1-6]. SIFT algorithm[3-5]can achieve image registration when there are translation, rotation, affine transformation between two images, even for images took by arbitrary angles. And SIFT feature is the milestone of local feature study. But there are long matching time and many wrong matching in the traditional SIFT algorithm, it is difficult to meet the requirement of fast image registration. This paper puts forward an image registration algorithm based on improved SIFT feature, which is robust for image rotation, affine and scale change, and is better than traditional SIFT algorithm.

scholarly journals Robust Image Matching Algorithm Using SIFT on Multiple Layered Strategies

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Yong Chen ◽  
Lei Shang ◽  
Eric Hu
Keyword(s):  
Image Matching ◽  
Geometric Feature ◽  
Data Sets ◽  
Feature Points ◽  
Matching Method ◽  
Scale Invariant ◽  
Data Set ◽  
Sift Algorithm ◽  
Delta Rule ◽  
Scale Invariant Feature

As for the unsatisfactory accuracy caused by SIFT (scale-invariant feature transform) in complicated image matching, a novel matching method on multiple layered strategies is proposed in this paper. Firstly, the coarse data sets are filtered by Euclidean distance. Next, geometric feature consistency constraint is adopted to refine the corresponding feature points, discarding the points with uncoordinated slope values. Thirdly, scale and orientation clustering constraint method is proposed to precisely choose the matching points. The scale and orientation differences are employed as the elements ofk-means clustering in the method. Thus, two sets of feature points and the refined data set are obtained. Finally, 3 * delta rule of the refined data set is used to search all the remaining points. Our multiple layered strategies make full use of feature constraint rules to improve the matching accuracy of SIFT algorithm. The proposed matching method is compared to the traditional SIFT descriptor in various tests. The experimental results show that the proposed method outperforms the traditional SIFT algorithm with respect to correction ratio and repeatability.

Implementation of Image Matching Algorithm Based on SIFT Features

2014 ◽  
Vol 602-605 ◽  
pp. 3181-3184 ◽  
Author(s):  
Mu Yi Yin ◽  
Fei Guan ◽  
Peng Ding ◽  
Zhong Feng Liu
Keyword(s):  
Image Matching ◽  
Implementation Process ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Source Codes ◽  
Invariant Feature ◽  
Viewpoint Change ◽  
Sift Features ◽  
Scale Invariant Feature

With the aim to solve the implement problem in scale invariant feature transform (SIFT) algorithm, the theory and the implementation process was analyzed in detail. The characteristics of the SIFT method were analyzed by theory, combined with the explanation of the Rob Hess SIFT source codes. The effect of the SIFT method was validated by matching two different real images. The matching result shows that the features extracted by SIFT method have excellent adaptive and accurate characteristics to image scale, viewpoint change, which are useful for the fields of image recognition, image reconstruction, etc.

scholarly journals RESEARCH PROGRESS ON FEATURE MATCHING OF SAR AND OPTICAL IMAGES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 77-82 ◽  
Author(s):  
H. Qian ◽  
J. W. Yue ◽  
M. Chen
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Optical Image ◽  
Research Progress ◽  
Phase Congruency ◽  
Matching Method ◽  
Sift Algorithm ◽  
Matching Problems ◽  
Optical Images ◽  
Edge Features

Abstract. Before obtaining information and identifying ground target from images, image matching is necessary. However, problems of strong pixel noise interference and nonlinear gray scale differences in synthetic aperture radar image still exist. Feature matching becomes a kind possible solution. To learn the research progress of SAR and optical image matching, as well as finding solutions for above matching problems, a summary for feature matching with SAR and optical image is indispensable. By listing three typical methods below, we can discuss and compare how researchers improve and innovate methods for feature matching from different angles in matching process. First method is feature matching method proposed by CHEN Min et. It uses phase congruency method to detect point features. Feature descriptors are based on gaussian-gamma-shaped edge strength maps instead of original images. This method combines both edge features and point features to reach a match target. The second one is SAR-SIFT algorithm of F. Dellinger et. This kind of method is based on improvement of sift algorithm. It proposes a SAR-Harris method and also a calculation method for features descriptors named gradient by ratio. Thirdly, it is feature matching method proposed by Yu Qiuze et. By using edge features of image and improvement of hausdorff distance for similarity measure, it applies genetic algorithm to accelerate matching search process to complete matching tasks. Those methods are implemented by using python programs, and are compared by some indexes. Experimental data used multiple sets of terrasar and optical image pairs of different resolutions. To some extent, the results demonstrate that all three kinds of feature methods can improve the matching effect between SAR and optical images. It can be easier to reach match purposes of SAR and optical images by using image edge features, while such methods are too dependent on the edge features.

scholarly journals Shared Nearest Neighbor clustering in a Locality Sensitive Hashing framework

2016 ◽  
Author(s):  
Sawsan Kanj ◽  
Thomas Brüls ◽  
Stéphane Gazut
Keyword(s):  
Nearest Neighbor ◽  
Reference Data ◽  
Sequence Data ◽  
Scale Up ◽  
Nearest Neighbors ◽  
High Accuracy ◽  
Locality Sensitive Hashing ◽  
High Dimensional ◽  
Nearest Neighbor Rule ◽  
Shared Nearest Neighbor

AbstractWe present a new algorithm to cluster high dimensional sequence data, and its application to the field of metagenomics, which aims to reconstruct individual genomes from a mixture of genomes sampled from an environ-mental site, without any prior knowledge of reference data (genomes) or the shape of clusters. Such problems typically cannot be solved directly with classical approaches seeking to estimate the density of clusters, e.g., using the shared nearest neighbors rule, due to the prohibitive size of contemporary sequence datasets. We explore here a new method based on combining the shared nearest neighbor (SNN) rule with the concept of Locality Sensitive Hashing (LSH). The proposed method, called LSH-SNN, works by randomly splitting the input data into smaller-sized subsets (buckets) and, employing the shared nearest neighbor rule on each of these buckets. Links can be created among neighbors sharing a sufficient number of elements, hence allowing clusters to be grown from linked elements. LSH-SNN can scale up to larger datasets consisting of millions of sequences, while achieving high accuracy across a variety of sample sizes and complexities.

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