An efficient rotation-invariance remote image matching algorithm based on feature points matching

2005 ◽  
Author(s):  
Xu Jianbin ◽  
Hong Wen ◽  
Wu Yirong
Keyword(s):  
Image Matching ◽  
Rotation Invariance ◽  
Feature Points ◽  
Matching Algorithm ◽  
Feature Points Matching

The Fast Image Matching Algorithm for Building Photogrammetry

2012 ◽  
Vol 152-154 ◽  
pp. 1723-1728
Author(s):  
Mao Li Fu ◽  
Can Zhao ◽  
Jun Ting Cheng
Keyword(s):  
Image Matching ◽  
Three Dimensional ◽  
Extraction Process ◽  
Accurate Information ◽  
Feature Points ◽  
Matching Algorithm ◽  
Feature Points Matching ◽  
Reference Information ◽  
Harris Corner ◽  
Dimensional Reconstruction

SIFT is the most common algorithm for the image local feature points matching. The excellency of it is not only good spatial scale invariance, but also more accurate and faster than other algorithm. However, the SIFT feature points do not reflect the geometric features of objects, so, when dealing with the building images, these points are not available in most cases, and the extraction process is complicated. Therefore, this paper presents a new algorithm that combines the Harris corner detector and SIFT operator. This new algorithm not only can enhance the efficiency of image matching, and make accurate information on the building corner, but also provide good reference information for modeling. Experiments show that the extract feature points of this algorithm can be applied to the three-dimensional reconstruction of large buildings.

An Image Matching Algorithm Based on SIFT and Invariability of Feature Points Set

2011 ◽  
Vol 121-126 ◽  
pp. 701-704
Author(s):  
Xue Tong Wang ◽  
Yao Xu ◽  
Feng Gao ◽  
Jing Yi Bai
Keyword(s):  
Image Matching ◽  
Singular Value ◽  
Feature Points ◽  
Matching Algorithm ◽  
Candidate Feature

Feature points can be used to match images. Candidate feature points are extracted through SIFT firstly. Then feature points are selected from candidate points through singular value decomposing. Distance between feature points sets is computed According to theory of invariability of feature points set, images are matched if the distance is less than a threshold. Experiment showed that this algorithm is available.

scholarly journals SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 810
Author(s):  
Meng Yu ◽  
Dong Zhang ◽  
Dah-Jye Lee ◽  
Alok Desai
Keyword(s):  
Image Matching ◽  
Rotation Invariance ◽  
Polar Coordinate System ◽  
Feature Descriptor ◽  
Feature Points ◽  
Rotation Invariant ◽  
Image Scaling ◽  
Polar Coordinate ◽  
Computer Vision Applications ◽  
Accurate Matching

Feature description has an important role in image matching and is widely used for a variety of computer vision applications. As an efficient synthetic basis feature descriptor, SYnthetic BAsis (SYBA) requires low computational complexity and provides accurate matching results. However, the number of matched feature points generated by SYBA suffers from large image scaling and rotation variations. In this paper, we improve SYBA’s scale and rotation invariance by adding an efficient pre-processing operation. The proposed algorithm, SR-SYBA, represents the scale of the feature region with the location of maximum gradient response along the radial direction in Log-polar coordinate system. Based on this scale representation, it normalizes all feature regions to the same reference scale to provide scale invariance. The orientation of the feature region is represented as the orientation of the vector from the center of the feature region to its intensity centroid. Based on this orientation representation, all feature regions are rotated to the same reference orientation to provide rotation invariance. The original SYBA descriptor is then applied to the scale and orientation normalized feature regions for description and matching. Experiment results show that SR-SYBA greatly improves SYBA for image matching applications with scaling and rotation variations. SR-SYBA obtains comparable or better performance in terms of matching rate compared to the mainstream algorithms while still maintains its advantages of using much less storage and simpler computations. SR-SYBA is applied to a vision-based measurement application to demonstrate its performance for image matching.

The Application of SIFT Algorithm in Blind Road Environmental Image Matching

2012 ◽  
Vol 155-156 ◽  
pp. 1137-1141
Author(s):  
Shuo Shi ◽  
Ming Yu ◽  
Cui Hong Xue ◽  
Ying Zhou
Keyword(s):  
Feature Extraction ◽  
Image Matching ◽  
Feature Points ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Extraction Algorithm ◽  
Environmental Image ◽  
Scale Invariant Feature

Image Matching is a key technology in the intelligent navigation system for the blind, which is based on the computer video. The images of moving blind people, collected at real time, have variety of changes in light, rotation, scaling, etc. Against this feature, we propose a practical matching algorithm, which is based on the SIFT (Scale Invariant, Feature Transform). That is the image matching algorithm. We focus on the algorithm of the SIFT feature extraction and matching, and obtain the feature points of the image through feature extraction algorithm. We verify the effect of the algorithm by selecting practical images with rotation, scaling and different light. The result is that this method can get better matches for the blind road environmental image.

scholarly journals A fast RFID image matching algorithm for multi-tag identification and distribution optimization

2021 ◽  
Vol 15 ◽  
pp. 174830262110126
Author(s):  
Ke Zhang ◽  
Xiaolei Yu ◽  
Lin Li ◽  
Zhenlu Liu ◽  
Shanhao Zhou ◽  
...  
Keyword(s):  
Real Time ◽  
Image Matching ◽  
Positioning System ◽  
Feature Points ◽  
High Complexity ◽  
Matching Accuracy ◽  
Tag Identification ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
Matching Algorithm

We propose an improved image matching algorithm that combines the minimum feature value algorithm to extract feature points and the direction gradient histogram to calculate the description vector. This algorithm is oriented to RFID multi-tag identification and distribution optimization in the actual scenario, and the traditional SURF algorithm has the problems of low matching accuracy and high complexity in multi-tag matching. This algorithm effectively improves the positioning accuracy of the RFID multi-tag positioning system. The experimental results show that the matching success rate of the improved algorithm in this paper is 87.4%, which is 50% higher than the SURF algorithm. Not only the matching accuracy is greatly improved, but the running speed is also increased by 48%. The algorithm in this paper has high matching accuracy and real-time performance.It provides an effective way for RFID multi-tag real-time fast matching and precise positioning.

An Image Matching Algorithm Based on SUSAN-SIFT Algorithm

2013 ◽  
Vol 325-326 ◽  
pp. 1588-1592 ◽  
Author(s):  
Hui Qing Zhang ◽  
Lu Guang Cao
Keyword(s):  
Experimental Verification ◽  
Image Matching ◽  
Feature Points ◽  
Principal Curvatures ◽  
Fast Calculation ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Low Contrast ◽  
Time Performance ◽  
Gaussian Convolution

For the purpose of improving the real-time performance of the SIFT algorithm, an image matching algorithm based on SUSAN-SIFT algorithm is proposed in this paper. Use SUSAN algorithm in the detecting feature points part of the SIFT algorithm, avoiding the time-consuming down-sampling and Gaussian convolution of the algorithm, and remove the feature points of the unstable and low-contrast by the methods of interpolated estimate and the principal curvatures, and then use the SIFT algorithm to achieve the parts of describing the feature points and images matching. experimental verification: the SUSAN-SIFT algorithm has more fast calculation speed than SIFT algorithm ensuring the accuracy at the same time.

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