| Paper Abstract and Keywords |
| Presentation |
2008-03-11 13:00
[Tutorial Lecture]
*
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| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Mean shift is a popular robust framework for statistical data analysis using kernel density estimation, originally proposed by Fukunaga and Hostetler in 70's. Recently, due to the work by Cheng and Comaniciu, this method has been re-discovered and successfully applied to a wide range of vision applications. This article provides a comprehensive overview of the basic theory and applications of mean shift, highlighting its practical and theoretical advantages, recent theoretical extensions, as well as vision applications such as image segmentation and object tracking. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Mean shift / Kernel density estimation / Mode seeking / Clustering / Segmentation / Tracking / / |
| Reference Info. |
IEICE Tech. Rep., vol. 107, no. 539, PRMU2007-308, pp. 401-414, March 2008. |
| Paper # |
IE2007-324, PRMU2007-308 |
| Conference Information |
| Committee |
PRMU IE |
| Conference Date |
2008-03-10 - 2008-03-11 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
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| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
JAIST |
| Paper Information |
| Registration To |
PRMU |
| Conference Code |
2008-03-PRMU-IE |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
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| Sub Title (in English) |
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| Keyword(1) |
Mean shift |
| Keyword(2) |
Kernel density estimation |
| Keyword(3) |
Mode seeking |
| Keyword(4) |
Clustering |
| Keyword(5) |
Segmentation |
| Keyword(6) |
Tracking |
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| Speaker |
1 |
| Date Time |
2008-03-11 13:00:00 |
| Presentation Time |
90 |
| Registration for |
PRMU |
| Paper # |
IEICE-IE2007-324,IEICE-PRMU2007-308 |
| Volume (vol) |
IEICE-107 |
| Number (no) |
IEICE-IE-538,IEICE-PRMU-539 |
| Page |
pp.401-414 |
| #Pages |
IEICE-14 |
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