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 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.

A SIFT Feature Matching Algorithm Based on Semi-Variance Function

2013 ◽  
Vol 647 ◽  
pp. 896-900 ◽  
Author(s):  
Feng Tian ◽  
Yu Bo Yan
Keyword(s):  
High Dimension ◽  
Image Matching ◽  
Feature Matching ◽  
Variance Function ◽  
Feature Point ◽  
Matching Algorithm ◽  
Sift Feature ◽  
Matching Efficiency

For solving the low matching efficiency problem due to high dimension of eigenvector in SIFT, a SIFT feature matching algorithm based on semi-variance function is proposed. For each feature point in image SIFT feature point zone, m beams are generated by using the position of the feature point as center and the orientation of the feature point as start direction. The image semi-variance function value of each beam, which is treated as SIFT value of eigenvector descriptor, is used in the algorithm aiming at reducing the dimension of eigenvector and improving image matching efficiency. The experiment result shows that the matching rate of this algorithm is higher, the matching time of this algorithm is less.

An Image Matching Algorithm Based on SCCH Feature Descriptor

2011 ◽  
Vol 33 (9) ◽  
pp. 2152-2157 ◽  
Author(s):  
Yong-he Tang ◽  
Huan-zhang Lu ◽  
Mou-fa Hu
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Matching Algorithm

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.

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.

scholarly journals SIFT Feature Matching Algorithm with Local Shape Context

2013 ◽  
Vol 5 (20) ◽  
pp. 4810-4815
Author(s):  
Gu Lichuan ◽  
Qiao Yulong ◽  
Cao Mengru ◽  
Guo Qingyan
Keyword(s):  
Feature Matching ◽  
Shape Context ◽  
Local Shape ◽  
Matching Algorithm ◽  
Sift Feature

Traffic Sign Recognition Based on SIFT Features

2010 ◽  
Vol 121-122 ◽  
pp. 596-599 ◽  
Author(s):  
Ni An Cai ◽  
Wen Zhao Liang ◽  
Shao Qiu Xu ◽  
Fang Zhen Li
Keyword(s):  
Traditional Method ◽  
Euclidean Distance ◽  
Experimental Results ◽  
Traffic Sign Recognition ◽  
Recognition Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Good Ability ◽  
Sift Features

A recognition method for traffic signs based on an SIFT features is proposed to solve the problems of distortion and occlusion. SIFT features are first extracted from traffic signs and matched by using the Euclidean distance. Then the recognition is implemented based on the similarity. Experimental results show that the proposed method, superior to traditional method, can excellently recognize traffic signs with the transformation of scale, rotation, and distortion and has a good ability of anti-noise and anti-occlusion.

An Improved SIFT Matching Algorithm

2012 ◽  
Vol 239-240 ◽  
pp. 1232-1237 ◽  
Author(s):  
Can Ding ◽  
Chang Wen Qu ◽  
Feng Su
Keyword(s):  
High Dimension ◽  
Gradient Method ◽  
Feature Matching ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
New Feature ◽  
Scale Invariant Feature

The high dimension and complexity of feature descriptor of Scale Invariant Feature Transform (SIFT), not only occupy the memory spaces, but also influence the speed of feature matching. We adopt the statistic feature point’s neighbor gradient method, the local statistic area is constructed by 8 concentric square ring feature of points-centered, compute gradient of these pixels, and statistic gradient accumulated value of 8 directions, and then descending sort them, at last normalize them. The new feature descriptor descend dimension of feature from 128 to 64, the proposed method can improve matching speed and keep matching precision at the same time.

Geometric-Edge Random Graph Model for Image Representation

2012 ◽  
pp. 42-57
Author(s):  
Jiang Bo ◽  
Tang Jing ◽  
Luo Bin
Keyword(s):  
Random Graph ◽  
Image Matching ◽  
Graph Matching ◽  
Image Representation ◽  
Graph Model ◽  
Experimental Results ◽  
Structural Variations ◽  
Matching Algorithm ◽  
Random Graph Model ◽  
Dot Product

This chapter presents a random graph model for image representation. The first contribution the authors propose is a Geometric-Edge (G-E) Random Graph Model for image representation. The second contribution is that of casting image matching into G-E Random Graph matching by using the random dot product graph based matching algorithm. Experimental results show that the proposed G-E Random Graph model and matching algorithm are effective and robust to structural variations.

SIFT feature matching algorithm of multi-source remote image

2013 ◽  
Vol 21 (8) ◽  
pp. 2146-2153 ◽  
Author(s):  
刘志文 LIU Zhi-wen ◽  
刘定生 LIU Ding-sheng ◽  
刘鹏 LIU Peng
Keyword(s):  
Feature Matching ◽  
Matching Algorithm ◽  
Sift Feature
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