Hierarchical Blurring Mean-Shift

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F11%3A86081117" target="_blank" >RIV/61989100:27240/11:86081117 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-23687-7_21" target="_blank" >http://dx.doi.org/10.1007/978-3-642-23687-7_21</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-23687-7_21" target="_blank" >10.1007/978-3-642-23687-7_21</a>

Result language
angličtina
Original language name
Hierarchical Blurring Mean-Shift
Original language description
In recent years, various Mean-Shift methods were used for filtration and segmentation of images and other datasets. These methods achieve good segmentation results, but the computational speed is sometimes very low, especially for big images and some specific settings. In this paper, we propose an improved segmentation method that we call Hierarchical Blurring Mean-Shift. The method achieve significant reduction of computation time and minimal influence on segmentation quality. A comparison of our method with traditional Blurring Mean-Shift and Hierarchical Mean-Shift with respect to the quality of segmentation and computational time is demonstrated. Furthermore, we study the influence of parameter settings in various hierarchy depths on computationaltime and number of segments. Finally, the results promising reliable and fast image segmentation are presented.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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