Hierarchical fast mean-shift segmentation in depth images
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Hierarchical fast mean-shift segmentation in depth images
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
12
Pages from-to
441-452
Publisher name
Springer Verlag
Place of publication
Cham
Event location
Lecce
Event date
Oct 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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