Detection and modeling of alcohol intoxication dynamic from IR images based on clustering driven by ABC algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242728" target="_blank" >RIV/61989100:27240/19:10242728 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-28377-3_32" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-28377-3_32</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-28377-3_32" target="_blank" >10.1007/978-3-030-28377-3_32</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection and modeling of alcohol intoxication dynamic from IR images based on clustering driven by ABC algorithm

Popis výsledku v původním jazyce

Alcohol detection is a challenging issue due to many aspects, especially to security reasons. Conventional measuring systems usually utilize a direct contact with the human body to obtain on spot alcohol level estimation. Nevertheless, it is well known that there are several side effects including the facial temperature distribution for alcohol detection. Since the facial temperature map is observable from the infrared (IR) records, we have performed a set of experimental measurements allowing for dynamical tracking of time-dependent effect of the alcohol intoxication. In this paper, we have proposed the clustering multiregional segmentation driven by the genetic optimization, particularly the Artificial Bee Colony (ABC) algorithm for the facial IR segmentation. The genetic optimization determines an optimal distribution of the initial cluster&apos;s centroids, which represent the main part of a proper clustering. Based on the segmentation procedure, we have proposed a dynamical model allowing for prediction of time-dependent alcohol intoxication features. (C) Springer Nature Switzerland AG 2019.

Název v anglickém jazyce

Detection and modeling of alcohol intoxication dynamic from IR images based on clustering driven by ABC algorithm

Popis výsledku anglicky

Alcohol detection is a challenging issue due to many aspects, especially to security reasons. Conventional measuring systems usually utilize a direct contact with the human body to obtain on spot alcohol level estimation. Nevertheless, it is well known that there are several side effects including the facial temperature distribution for alcohol detection. Since the facial temperature map is observable from the infrared (IR) records, we have performed a set of experimental measurements allowing for dynamical tracking of time-dependent effect of the alcohol intoxication. In this paper, we have proposed the clustering multiregional segmentation driven by the genetic optimization, particularly the Artificial Bee Colony (ABC) algorithm for the facial IR segmentation. The genetic optimization determines an optimal distribution of the initial cluster&apos;s centroids, which represent the main part of a proper clustering. Based on the segmentation procedure, we have proposed a dynamical model allowing for prediction of time-dependent alcohol intoxication features. (C) Springer Nature Switzerland AG 2019.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 11683

ISBN

978-3-030-28376-6

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

10

Strana od-do

393-402

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Hendaye

Datum konání akce

4. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—