DocumentCode
553955
Title
A method for customizing 3D virtual human body models based on Multi-class Support Vector Machine
Author
Sun Yongjian ; Li Renwang ; Wan Changjiang ; Zhang Xiumei
Author_Institution
Dept. of Ind. Eng., Zhejiang Sci-Tech Univ., Hangzhou, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
344
Lastpage
347
Abstract
How to generate a personalized 3D virtual body model conveniently and quickly is playing an increasingly important role in computer animation, virtual reality, entertainment, e-commerce and many other areas. Some related researchers just simply adjust human characteristic parameters to generate body model using existing 3D body model. In this article, in order to generate the personalized 3D virtual body model quickly, an approach based on Fuzzy Support Vector Machines (FSVM) is suggested. This constructs a classification model of the personalized body characteristic parameter. The one-versus-one (OVO) method based on the binary tree is used to handle a multiclass problem by breaking it into various two-class problems. Application of the method shows that the method of FSVM has the characteristics of less calculation and less error in the allowed range than the classical neural network.
Keywords
fuzzy set theory; pattern classification; solid modelling; support vector machines; trees (mathematics); virtual reality; binary tree; classification; computer animation; entertainment; fuzzy support vector machines; one-versus-one method; personalized 3D virtual human body; virtual reality; Biological system modeling; Computational modeling; Humans; Kernel; Solid modeling; Support vector machines; Three dimensional displays; 3D human model; Customization; Human-centric products; Model customizing; Multi-class support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022036
Filename
6022036
Link To Document