Share Email Print

Proceedings Paper

The research of the application of the computer vision on the non-contacted body measurement based on the improved ant colony algorithm in the body type clustering
Author(s): Qun Zhan; Nanxiang Zhao
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In the 2D non-contacted body measurement, the transform model which converts the human body 2D girth data to the 3D girth data is required. However, the integrate model is hardly to be obtained for the different human body type categories determine the different model parameter. So, the work of human body type accuracy classification based on the measure data is very important. The canonical transformation method is used to strengthen the similar of data features of the same type and broaden the diversity of the data features of the different type. The "accumulating dead bodies" ant colony algorithm is improved in the paper in the way of employing the road information densities to help the ant to select the probable path lead to site of the accumulating dead bodies when it moves the data. By the way, the randomness and blindness of the ants' walking are eliminated, and the speed of the algorithm convergence is improved. For avoiding the unevenness of the data unit visited times in the algorithm, the access mechanism of the union data is employed, which avoid the algorithm to get into the local foul trap. The clustering validity function is selected to verify the clustering result of the human measure data. The experiment results indicate the affectivity and efficiency of the human body clustering work based on the improved ant colony algorithm. Basing the sorting result, the accuracy 3D body data transforming model can be founded, which should improve the accuracy of the non-contacted body measurement.

Paper Details

Date Published: 18 August 2011
PDF: 7 pages
Proc. SPIE 8194, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, 81941T (18 August 2011); doi: 10.1117/12.900312
Show Author Affiliations
Qun Zhan, Anhui Agriculture Univ. (China)
Nanxiang Zhao, Hefei Electronic Engineering Institute (China)

Published in SPIE Proceedings Vol. 8194:
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications
Makoto Ikeda; Nanjian Wu; Guangjun Zhang; Kecong Ai, Editor(s)

© SPIE. Terms of Use
Back to Top