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

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.

A Real-Time Image Matching Algorithm for Binocular Stereo Measurement System

2019 ◽  
Author(s):  
Jianyu Duan ◽  
Lingyu Sun ◽  
Lijun Li ◽  
Zongmiao Dai ◽  
Zhenkai Xiong ◽  
...  
Keyword(s):  
Real Time ◽  
Measurement System ◽  
Image Matching ◽  
Great Influence ◽  
Consensus Method ◽  
Feature Descriptor ◽  
Matching Algorithm ◽  
Real Time Image ◽  
Binocular Stereo ◽  
Time Image

Abstract Binocular stereo measurement system can obtain accurate three-dimensional information from two-dimensional images. It has been widely applied in many fields such as vehicle tracking, robot navigating, automatic crane lifting, as well as other fields. The crucial step of binocular stereo measurement is image matching. For the image matching, it is a great challenge to ensure both real-time and matching accuracy simultaneously. The image matching algorithm has a great influence on the image matching time and accuracy. In this paper, a real-time image matching algorithm for binocular stereo measurement system is proposed based on Speedup Robust Features (SURF) algorithm. In the proposed algorithm, firstly, the key feature points are identified by the original SURF algorithm method. Secondly, the main direction of the key feature point is determined by intensity centroid method. Then, the feature descriptor is calculated by the BRIEF binary method so that the time of feature description can be shortened. Finally, RANSAC (Random Sample Consensus) method is adopted to remove mismatching points. The experiments results show that the proposed algorithm can shorten image matching time obviously and improve the accuracy of matching points.

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 MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0178090 ◽  
Author(s):  
Mingzhe Su ◽  
Yan Ma ◽  
Xiangfen Zhang ◽  
Yan Wang ◽  
Yuping Zhang
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Binary Representation ◽  
Invariant Feature

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 AN INTEGRATED RANSAC AND GRAPH BASED MISMATCH ELIMINATION APPROACH FOR WIDE-BASELINE IMAGE MATCHING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 297-300 ◽  
Author(s):  
M. Hasheminasab ◽  
H. Ebadi ◽  
A. Sedaghat
Keyword(s):  
Image Matching ◽  
Graph Transformation ◽  
Feature Point ◽  
Consensus Method ◽  
Feature Descriptor ◽  
Range Image ◽  
Point Sets ◽  
Short Baseline ◽  
Epipolar Constraint ◽  
Baseline Image

In this paper we propose an integrated approach in order to increase the precision of feature point matching. Many different algorithms have been developed as to optimizing the short-baseline image matching while because of illumination differences and viewpoints changes, wide-baseline image matching is so difficult to handle. Fortunately, the recent developments in the automatic extraction of local invariant features make wide-baseline image matching possible. The matching algorithms which are based on local feature similarity principle, using feature descriptor as to establish correspondence between feature point sets. To date, the most remarkable descriptor is the scale-invariant feature transform (SIFT) descriptor , which is invariant to image rotation and scale, and it remains robust across a substantial range of affine distortion, presence of noise, and changes in illumination. The epipolar constraint based on RANSAC (random sample consensus) method is a conventional model for mismatch elimination, particularly in computer vision. Because only the distance from the epipolar line is considered, there are a few false matches in the selected matching results based on epipolar geometry and RANSAC. Aguilariu et al. proposed Graph Transformation Matching (GTM) algorithm to remove outliers which has some difficulties when the mismatched points surrounded by the same local neighbor structure. In this study to overcome these limitations, which mentioned above, a new three step matching scheme is presented where the SIFT algorithm is used to obtain initial corresponding point sets. In the second step, in order to reduce the outliers, RANSAC algorithm is applied. Finally, to remove the remained mismatches, based on the adjacent K-NN graph, the GTM is implemented. Four different close range image datasets with changes in viewpoint are utilized to evaluate the performance of the proposed method and the experimental results indicate its robustness and capability.

scholarly journals COMPARISON OF SEMI AUTOMATIC DTM FROM IMAGE MATCHING WITH DTM FROM LIDAR

2016 ◽  
Vol XLI-B3 ◽  
pp. 373-380
Author(s):  
Aji Rahmayudi ◽  
Aldino Rizaldy
Keyword(s):  
Image Matching ◽  
Point Cloud ◽  
Statistical Data ◽  
Aerial Images ◽  
Mountainous Terrain ◽  
Topographic Map ◽  
Matching Algorithm ◽  
Time Calculation ◽  
Accuracy Difference ◽  
Map Scale

Nowadays DTM LIDAR was used extensively for generating contour line in Topographic Map. This method is very superior compared to traditionally stereomodel compilation from aerial images that consume large resource of human operator and very time consuming. Since the improvement of computer vision and digital image processing, it is possible to generate point cloud DSM from aerial images using image matching algorithm. It is also possible to classify point cloud DSM to DTM using the same technique with LIDAR classification and producing DTM which is comparable to DTM LIDAR. This research will study the accuracy difference of both DTMs and the result of DTM in several different condition including urban area and forest area, flat terrain and mountainous terrain, also time calculation for mass production Topographic Map. From statistical data, both methods are able to produce 1:5.000 Topographic Map scale.

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