Video stabilization based on point feature matching technique

In this paper, an effective motion comparison method based on segmented multi-joint line graphs combined with the SIFT feature matching method is proposed. Firstly, the multi-joint 3D motion data are captured using the Kinect. Secondly, 3D motion data are normalized and distortion data are removed. Therefore, a 2D line graph can be obtained. Next, SIFT features of the 2D motion line graph are extracted. Finally, the line graphs are divided into several regions and then the comparison results can be calculated based on SIFT matching ratios between the tutor’s local line graph and the trainee’s local line graph. The experimental results show that the proposed method not only can easily deal with the several challenge problems in motion analysis, e.g., the problem of different rhythm of motions, the problem of a large amount of data, but also can provide detailed error correction cues.

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Image Point Feature Matching by Triangulation

Intelligent Automation & Soft Computing ◽

10.1080/10798587.1997.10750698 ◽

1997 ◽

Vol 3 (2) ◽

pp. 135-149

Author(s):

Joon-Woong Lee ◽

In-So Kweon

Keyword(s):

Feature Matching ◽

Image Point ◽

Point Feature

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An improved feature matching technique for stereo vision applications with the use of self-organizing map

International Journal of Precision Engineering and Manufacturing ◽

10.1007/s12541-012-0179-z ◽

2012 ◽

Vol 13 (8) ◽

pp. 1359-1368 ◽

Cited By ~ 11

Author(s):

Kajal Sharma ◽

Sung Gaun Kim ◽

Manu Pratap Singh

Keyword(s):

Stereo Vision ◽

Feature Matching ◽

Self Organizing Map ◽

Use Of Self ◽

Matching Technique ◽

Self Organizing

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Maximum-weight bipartite matching technique and its application in image feature matching

10.1117/12.233261 ◽

1996 ◽

Cited By ~ 18

Author(s):

Yong-Qing Cheng ◽

Victor Wu ◽

Robert Collins ◽

Allen R. Hanson ◽

Edward M. Riseman

Keyword(s):

Feature Matching ◽

Image Feature ◽

Maximum Weight ◽

Bipartite Matching ◽

Matching Technique

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A Feature Fast Matching Technique Using Improved Self-Organizing Map for Stereo Vision

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1299 ◽

2014 ◽

Vol 602-605 ◽

pp. 1299-1302

Author(s):

Xiang Ping Meng ◽

Lei Wang ◽

Quan De Yuan ◽

Xiu Ji

Keyword(s):

Binocular Vision ◽

Feature Matching ◽

Computation Time ◽

Visual Navigation ◽

Self Organizing Map ◽

Match Method ◽

Learning Methods ◽

Matching Technique ◽

Feature Match ◽

Self Organizing

Landmark feature match method of robot visual navigation has been studied, An unsupervised learning methods based on binocular vision has been proposed to match Landmark feature fast. Firstly, environmental Information images are obtained by binocular vision, Then SIFT feature vectors are abstracted from binocular vision images, Lastly, Self-Organizing Map is pulled in to match multidimensional feature point fast by using competitive learning methods. Experiments showed that the proposed methods on feature matching with better computation time and effect than the traditional SIFT and SURF of feature matching methods. And it can satisfied the requirement of real-time.

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