Feature Point Positioning and Modeling Approach for Human Body Based on Improved ASM

Authors

Xinjuan Zhu, School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China;
Xiaoya Xiong, School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China;

Abstract

Abstract: Traditional Procrustes normalization needs many iterations which will spend a lot of time. Here training samples alignment was set only after once translation, rotation and scaling operations by marking anchor point and using average body shape as the initialization rules model. Traditional ASM algorithm leads to a long computing time and is easily to make the feature points matching error for gray model’s similarity. It was improved by using every feature points as a center point, training gray model though its rounded rectangular gray distribution, and searching target points within its 24 neighborhood points. Experimental results show that the key feature point positioning method for Human body based on this improved ASM reduces the number of iterations, shortens the running time, and improves the positioning accuracy.

Recommended Citation

Zhu, Xinjuan and Xiong, Xiaoya (2020) "Feature Point Positioning and Modeling Approach for Human Body Based on Improved ASM," Journal of System Simulation: Vol. 27: Iss. 2, Article 10.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss2/10

First Page

286

Revised Date

2014-05-11

DOI Link

https://doi.org/

Last Page

294

CLC

TP391.9

Recommended Citation

Zhu Xinjuan, Xiong Xiaoya. Feature Point Positioning and Modeling Approach for Human Body Based on Improved ASM[J]. Journal of System Simulation, 2015, 27(2): 286-294.