An Effective Image Matching Algorithm Based on Rotation Independence

2013 ◽  
Vol 347-350 ◽  
pp. 3685-3690
Author(s):  
Xian Ying Huang ◽  
Wei Wei Chen
Keyword(s):  
Image Matching ◽  
Irregular Shape ◽  
Matching Accuracy ◽  
Similarity Comparison ◽  
Matching Algorithm ◽  
Irregular Shapes ◽  
Irregular Region ◽  
Histogram Intersection ◽  
Intersection Method ◽  
Traditional Image

Traditional image matching algorithms has poor accuracy in image comparing, such as histogram intersection method. A new image matching algorithm based on the similarity comparison of irregular shape is presented in this paper, which divides the image into a number of irregular regions according to different colors, and extracts the boundary points of the irregular region to compose an irregular shape. The direction and distance is used to comparing the two irregular shapes if the rotation of the image is not considered, otherwise circular list is used to ignore the image rotation. It can be used widely. If two irregular shapes are similar, the two images are considered similar. Experiment proves that this method can effectively improve the image matching accuracy.

Research of Stereo Matching Based on HALCON

2013 ◽  
Vol 415 ◽  
pp. 361-364
Author(s):  
Hui Yu Xiang ◽  
Zhe Li ◽  
Jia Jun Huang ◽  
Baoan Han
Keyword(s):  
Image Matching ◽  
Stereo Matching ◽  
Vision System ◽  
Difficult Problem ◽  
Matching Method ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Matching Window

Binocular stereo matching is a hot and difficult problem in machine vision. In this paper, based on the matching method of Halcon which is visual software perform image matching. First, performing binocular stereo vision system calibration, based on the calibration results acquired the epipolar standard geometric structure. Then, image matching researched under this structure. At last, using ncc matching algorithm, through comparing the different parameters matching window obtain ideal match results. Experiments prove that this method not only can effectively shorten matching time, but also can achieve higher matching accuracy.

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.

Modeling of image matching accuracy with image metrics based on least squares matching algorithm

2010 ◽  
Author(s):  
Xi-yang Zhi ◽  
Wei Zhang ◽  
Fan-jiao Tan ◽  
Qing-yu Hou ◽  
Yi-ming Cao
Keyword(s):  
Least Squares ◽  
Image Matching ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Image Metrics

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

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 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.

scholarly journals PC_FFIW A Robust Image Matching Algorithm

2012 ◽  
Vol 49 (21) ◽  
pp. 20-24
Author(s):  
Behloul Ali ◽  
Aksa Abla
Keyword(s):  
Image Matching ◽  
Matching Algorithm ◽  
Robust Image
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