Hierarchical fast mean-shift segmentation in depth images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86100011" target="_blank" >RIV/61989100:27240/16:86100011 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-48680-2_39" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-48680-2_39</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-48680-2_39" target="_blank" >10.1007/978-3-319-48680-2_39</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical fast mean-shift segmentation in depth images

Popis výsledku v původním jazyce

Head position and head pose detection systems are very popular in recent times, especially with the rise of depth cameras like Microsoft Kinect and Intel RealSense. The goal is to recognize and segment a head in depth data. The systems could also detect the direction in which the head is pointing and we use these data to improve the gaze direction detection system and provide useful information to allow detectors to work properly. We present the Hierarchical Fast Blurring Mean Shift algorithm that is able to extract the data from depth images in real-time from above mentioned cameras. We also present some modifications for an effective reduction of the mean-shift dataset during the computation that allow us to increase the precision of the method. We use a hierarchical approach to reduce the dataset during the computation process and to improve the speed. (C) Springer International Publishing AG 2016.

Název v anglickém jazyce

Hierarchical fast mean-shift segmentation in depth images

Popis výsledku anglicky

Head position and head pose detection systems are very popular in recent times, especially with the rise of depth cameras like Microsoft Kinect and Intel RealSense. The goal is to recognize and segment a head in depth data. The systems could also detect the direction in which the head is pointing and we use these data to improve the gaze direction detection system and provide useful information to allow detectors to work properly. We present the Hierarchical Fast Blurring Mean Shift algorithm that is able to extract the data from depth images in real-time from above mentioned cameras. We also present some modifications for an effective reduction of the mean-shift dataset during the computation that allow us to increase the precision of the method. We use a hierarchical approach to reduce the dataset during the computation process and to improve the speed. (C) Springer International Publishing AG 2016.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

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 10016

ISBN

978-3-319-48679-6

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

441-452

Název nakladatele

Springer Verlag

Místo vydání

Cham

Místo konání akce

Lecce

Datum konání akce

24. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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