Multimodal Point Distribution Model for Anthropological Landmark Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00110475" target="_blank" >RIV/00216224:14330/19:00110475 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICIP.2019.8803252" target="_blank" >http://dx.doi.org/10.1109/ICIP.2019.8803252</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICIP.2019.8803252" target="_blank" >10.1109/ICIP.2019.8803252</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multimodal Point Distribution Model for Anthropological Landmark Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multimodal Point Distribution Model for Anthropological Landmark Detection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

26th IEEE International Conference on Image Processing (ICIP2019)

ISBN

9781538662496

ISSN

1522-4880

e-ISSN

2381-8549

Počet stran výsledku

5

Strana od-do

2986-2990

Název nakladatele

Springer

Místo vydání

Taipei, Taiwan

Místo konání akce

Taipei, Taiwan

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000521828603020