3D head model classification using optimized EGI

Xin Tong; Hau San Wong; Bo Ma

doi:10.1117/12.641366

3D head model classification using optimized EGI

Xin Tong^*
, Hau San Wong
, Bo Ma

^*Corresponding author for this work

City University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

With the general availability of 3D digitizers and scanners, 3D graphical models have been used widely in a variety of applications. This has led to the development of search engines for 3D models. Especially, 3D head model classification and retrieval have received more and more attention in view of their many potential applications in criminal identifications, computer animation, movie industry and medical industry. This paper addresses the 3D head model classification problem using 2D subspace analysis methods such as 2D principal component analysis (2D PCA^[3]) and 2D fisher discriminant analysis (2DLDA^[5]). It takes advantage of the fact that the histogram is a 2D image, and we can extract the most useful information from these 2D images to get a good result accordingingly. As a result, there are two main advantages: First, we can perform less calculation to obtain the same rate of classification; second, we can reduce the dimensionality more than PCA to obtain a higher efficiency.

Original language	English
Title of host publication	Three-Dimensional Image Capture and Applications VII - Proceedings of SPIE-IS and T Electronic Imaging
DOIs	https://doi.org/10.1117/12.641366
Publication status	Published - 2006
Externally published	Yes
Event	Three-Dimensional Image Capture and Applications VII - San Jose, CA, United States Duration: 16 Jan 2006 → 17 Jan 2006

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	6056
ISSN (Print)	0277-786X

Conference

Conference	Three-Dimensional Image Capture and Applications VII
Country/Territory	United States
City	San Jose, CA
Period	16/01/06 → 17/01/06

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

2D subspace analysis
3D head model classification
EGI (Extended Gaussian Image)
LDA
PCA

Access to Document

10.1117/12.641366

Cite this

@inproceedings{7c57fdbb2cd74534a484b4f1909cb55a,

title = "3D head model classification using optimized EGI",

abstract = "With the general availability of 3D digitizers and scanners, 3D graphical models have been used widely in a variety of applications. This has led to the development of search engines for 3D models. Especially, 3D head model classification and retrieval have received more and more attention in view of their many potential applications in criminal identifications, computer animation, movie industry and medical industry. This paper addresses the 3D head model classification problem using 2D subspace analysis methods such as 2D principal component analysis (2D PCA[3]) and 2D fisher discriminant analysis (2DLDA[5]). It takes advantage of the fact that the histogram is a 2D image, and we can extract the most useful information from these 2D images to get a good result accordingingly. As a result, there are two main advantages: First, we can perform less calculation to obtain the same rate of classification; second, we can reduce the dimensionality more than PCA to obtain a higher efficiency.",

keywords = "2D subspace analysis, 3D head model classification, EGI (Extended Gaussian Image), LDA, PCA",

author = "Xin Tong and Wong, \{Hau San\} and Bo Ma",

year = "2006",

doi = "10.1117/12.641366",

language = "English",

isbn = "0819460966",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

booktitle = "Three-Dimensional Image Capture and Applications VII - Proceedings of SPIE-IS and T Electronic Imaging",

note = "Three-Dimensional Image Capture and Applications VII ; Conference date: 16-01-2006 Through 17-01-2006",

}

Tong, X, Wong, HS & Ma, B 2006, 3D head model classification using optimized EGI. in Three-Dimensional Image Capture and Applications VII - Proceedings of SPIE-IS and T Electronic Imaging., 60560M, Proceedings of SPIE - The International Society for Optical Engineering, vol. 6056, Three-Dimensional Image Capture and Applications VII, San Jose, CA, United States, 16/01/06. https://doi.org/10.1117/12.641366

3D head model classification using optimized EGI. / Tong, Xin; Wong, Hau San; Ma, Bo.
Three-Dimensional Image Capture and Applications VII - Proceedings of SPIE-IS and T Electronic Imaging. 2006. 60560M (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6056).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - 3D head model classification using optimized EGI

AU - Tong, Xin

AU - Wong, Hau San

AU - Ma, Bo

PY - 2006

Y1 - 2006

N2 - With the general availability of 3D digitizers and scanners, 3D graphical models have been used widely in a variety of applications. This has led to the development of search engines for 3D models. Especially, 3D head model classification and retrieval have received more and more attention in view of their many potential applications in criminal identifications, computer animation, movie industry and medical industry. This paper addresses the 3D head model classification problem using 2D subspace analysis methods such as 2D principal component analysis (2D PCA[3]) and 2D fisher discriminant analysis (2DLDA[5]). It takes advantage of the fact that the histogram is a 2D image, and we can extract the most useful information from these 2D images to get a good result accordingingly. As a result, there are two main advantages: First, we can perform less calculation to obtain the same rate of classification; second, we can reduce the dimensionality more than PCA to obtain a higher efficiency.

AB - With the general availability of 3D digitizers and scanners, 3D graphical models have been used widely in a variety of applications. This has led to the development of search engines for 3D models. Especially, 3D head model classification and retrieval have received more and more attention in view of their many potential applications in criminal identifications, computer animation, movie industry and medical industry. This paper addresses the 3D head model classification problem using 2D subspace analysis methods such as 2D principal component analysis (2D PCA[3]) and 2D fisher discriminant analysis (2DLDA[5]). It takes advantage of the fact that the histogram is a 2D image, and we can extract the most useful information from these 2D images to get a good result accordingingly. As a result, there are two main advantages: First, we can perform less calculation to obtain the same rate of classification; second, we can reduce the dimensionality more than PCA to obtain a higher efficiency.

KW - 2D subspace analysis

KW - 3D head model classification

KW - EGI (Extended Gaussian Image)

KW - LDA

KW - PCA

UR - https://www.scopus.com/pages/publications/33645501026

U2 - 10.1117/12.641366

DO - 10.1117/12.641366

M3 - Conference contribution

AN - SCOPUS:33645501026

SN - 0819460966

SN - 9780819460967

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Three-Dimensional Image Capture and Applications VII - Proceedings of SPIE-IS and T Electronic Imaging

T2 - Three-Dimensional Image Capture and Applications VII

Y2 - 16 January 2006 through 17 January 2006

ER -

3D head model classification using optimized EGI

Abstract

Publication series

Conference

UN SDGs

Keywords

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