Multimodal Point Distribution Model for Anthropological
Landmark Detection

D 2019

Multimodal Point Distribution Model for Anthropological Landmark Detection

FERKOVÁ, Zuzana and Petr MATULA

Basic information

Original name

Multimodal Point Distribution Model for Anthropological Landmark Detection

Authors

FERKOVÁ, Zuzana (703 Slovakia, belonging to the institution) and Petr MATULA (203 Czech Republic, belonging to the institution)

Edition

Taipei, Taiwan, 26th IEEE International Conference on Image Processing (ICIP2019), p. 2986-2990, 5 pp. 2019

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/19:00110475

Organization unit

Faculty of Informatics

ISBN

978-1-5386-6249-6

ISSN

DOI

http://dx.doi.org/10.1109/ICIP.2019.8803252

UT WoS

000521828603020

Keywords in English

Facial landmark detection; point distribution model; FIDENTIS; HCI

Abstract

V originále

While current landmark detection algorithms offer a good approximation of the landmark locations, they are often unsuitable for the use in biological research. We present multimodal landmark detection approach, based on Point distribution model that detects a larger number of anthropologically relevant landmarks than the current landmark detection algorithms. At the same time we show that improving detection accuracy of initial vertices, using image information, to which the Point distribution model is fitted, increases both the overall accuracy and the stability of the detected landmarks. We show results on data from the public FIDENTIS Database, created for the anthropological research, and compare them to the state-of-the-art landmark detection algorithms that are based on statistical shape models.

Links

MUNI/A/1018/2018, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VIII.

Investor: Masaryk University, Category A

MUNI/A/1040/2018, interní kód MU

Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 19 (Acronym: SKOMU)

Investor: Masaryk University, Category A

Citovat

FERKOVÁ, Zuzana and Petr MATULA. Multimodal Point Distribution Model for Anthropological Landmark Detection. Online. In 26th IEEE International Conference on Image Processing (ICIP2019). Taipei, Taiwan: Springer, 2019, p. 2986-2990. ISBN 978-1-5386-6249-6. Available from: https://dx.doi.org/10.1109/ICIP.2019.8803252.

@inproceedings{1550005,
   author = {Ferková, Zuzana and Matula, Petr},
   address = {Taipei, Taiwan},
   booktitle = {26th IEEE International Conference on Image Processing (ICIP2019)},
   doi = {http://dx.doi.org/10.1109/ICIP.2019.8803252},
   keywords = {Facial landmark detection; point distribution model; FIDENTIS; HCI},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Taipei, Taiwan},
   isbn = {978-1-5386-6249-6},
   pages = {2986-2990},
   publisher = {Springer},
   title = {Multimodal Point Distribution Model for Anthropological Landmark Detection},
   year = {2019}
}

TY  - JOUR
ID  - 1550005
AU  - Ferková, Zuzana - Matula, Petr
PY  - 2019
TI  - Multimodal Point Distribution Model for Anthropological Landmark Detection
PB  - Springer
CY  - Taipei, Taiwan
SN  - 9781538662496
KW  - Facial landmark detection
KW  - point distribution model
KW  - FIDENTIS
KW  - HCI
N2  - While current landmark detection algorithms offer a good approximation of the landmark locations, they are often unsuitable for the use in biological research. We present multimodal landmark detection approach, based on Point distribution model that detects a larger number of anthropologically relevant landmarks than the current landmark detection algorithms. At the same time we show that improving detection accuracy of initial vertices, using image information, to which the Point distribution model is fitted, increases both the overall accuracy and the stability of the detected landmarks. We show results on data from the public FIDENTIS Database, created for the anthropological research, and compare them to the state-of-the-art landmark detection algorithms that are based on statistical shape models.
ER  -

FERKOVÁ, Zuzana and Petr MATULA. Multimodal Point Distribution Model for Anthropological Landmark Detection. Online. In \textit{26th IEEE International Conference on Image Processing (ICIP2019)}. Taipei, Taiwan: Springer, 2019, p.~2986-2990. ISBN~978-1-5386-6249-6. Available from: https://dx.doi.org/10.1109/ICIP.2019.8803252.

Detailed Information on Publication Record