Two-dimensional color-coded matching method for 3D moving human body shape measurement

2001 ◽  
Author(s):  
Weiyi Liu ◽  
Deyue Yu ◽  
Zhaoqi Wang ◽  
Guoguang Mu
Keyword(s):  
Human Body ◽  
Body Shape ◽  
Shape Measurement ◽  
Two Dimensional ◽  
Matching Method

Recognition of human kidney ultrasonic organ images by two-dimensional dp matching method

1986 ◽  
Vol 17 (8) ◽  
pp. 50-59 ◽  
Author(s):  
Hiromitsu Yamada ◽  
Kazuhiko Yamamoto ◽  
Masahiko Matsuura
Keyword(s):  
Human Kidney ◽  
Two Dimensional ◽  
Matching Method

Video-Based Human Motion Analysis

2011 ◽  
Vol 403-408 ◽  
pp. 2593-2597
Author(s):  
Hong Bao ◽  
Zhi Min Liu
Keyword(s):  
Motion Analysis ◽  
Human Body ◽  
Body Shape ◽  
Human Motion ◽  
Random Motion ◽  
Human Motion Analysis ◽  
Mass Center ◽  
Shape Features ◽  
Skeleton Model ◽  
Regular Motion

In the analysis of human motion, movement was divided into regular motion (such as walking and running) and random motion (such as falling down).Human skeleton model is used in this paper to do the video-based analysis. Key joints on human body were chosen to be traced instead of tracking the entire human body. Shape features like mass center trajectory were used to describe the movement, and to classify human motion. desired results achieved.

2-D Image Matching Based on Global Harmony Search Optimization

2012 ◽  
Vol 239-240 ◽  
pp. 1133-1137 ◽  
Author(s):  
Li Ming Guan ◽  
Qian Kai Yang ◽  
Jian Lin ◽  
Yi Fan Wu
Keyword(s):  
Hausdorff Distance ◽  
Image Matching ◽  
Fitness Function ◽  
Harmony Search ◽  
Two Dimensional ◽  
Edge Detector ◽  
Matching Method ◽  
Two Dimensional Image ◽  
Search Optimization ◽  
Canny Edge

A two-dimensional image matching method based on the improved Hausdorff distance and global harmony search optimization is presented in this paper. First, edges are extracted form the original images by the Canny edge detector. Then, a fitness function based on the improved Hausdorff distance is constructed. Finally the global harmony search optimization is adopted to optimize the fitness function. Experiments show that the proposed method is able to locate the object of interest globally and efficiently.

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