Multimodal Point Distribution Model for Anthropological
Landmark Detection

D 2019

Multimodal Point Distribution Model for Anthropological Landmark Detection

FERKOVÁ, Zuzana a Petr MATULA

Základní údaje

Originální název

Multimodal Point Distribution Model for Anthropological Landmark Detection

Autoři

FERKOVÁ, Zuzana (703 Slovensko, domácí) a Petr MATULA (203 Česká republika, domácí)

Vydání

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

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/19:00110475

Organizační jednotka

Fakulta informatiky

ISBN

978-1-5386-6249-6

ISSN

DOI

https://doi.org/10.1109/ICIP.2019.8803252

UT WoS

000521828603020

EID Scopus

2-s2.0-85076807329

Klíčová slova anglicky

Facial landmark detection; point distribution model; FIDENTIS; HCI

Štítky

core_B, Facial landmark detection, FIDENTIS, firank_A, HCI, point distribution model

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 3. 5. 2020 12:41, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

MUNI/A/1018/2018, interní kód MU

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

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VIII., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

MUNI/A/1040/2018, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 19 (Akronym: SKOMU)

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 19, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

FERKOVÁ, Zuzana a Petr MATULA. Multimodal Point Distribution Model for Anthropological Landmark Detection. Online. In 26th IEEE International Conference on Image Processing (ICIP2019). Taipei, Taiwan: Springer, 2019, s. 2986-2990. ISBN 978-1-5386-6249-6. Dostupné z: https://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 = {https://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  - CONF
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 a 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, s.~2986-2990. ISBN~978-1-5386-6249-6. Dostupné z: https://doi.org/10.1109/ICIP.2019.8803252.

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