Feature extraction for efficient image and video segmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F16%3APU121596" target="_blank" >RIV/00216305:26230/16:PU121596 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/11086/" target="_blank" >https://www.fit.vut.cz/research/publication/11086/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/2948628.2948631" target="_blank" >10.1145/2948628.2948631</a>
Alternative languages
Result language
angličtina
Original language name
Feature extraction for efficient image and video segmentation
Original language description
The segmentation of sensory data of various domains is often crucial pre-processing step in many computer vision methods and applications. In this work, we propose a method that leverages the quantization of local features distributions for the depth and the temporal information. Three variants of the segmentation method is designed and evaluated reflecting various data domains: space (color and texture), temporal (motion) and depth domain. Each variant was tested on appropriate dataset showing the usability of designed method for applications like areal-image analysis, hand detection and moving-people detection. The pilot experiments shows the characteristics of the approach and computational costs of designed variants.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
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
Proceedings - SCCG 2016: 32nd Spring Conference on Computer Graphics
ISBN
978-1-4503-4436-4
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
75-80
Publisher name
Association for Computing Machinery
Place of publication
Smolenice
Event location
Smolenice
Event date
Apr 27, 2016
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000403659700010