|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM221202161 |
| 003 |
DE-627 |
| 005 |
20231224051255.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2012 xx |||||o 00| ||eng c |
| 028 |
5 |
2 |
|a pubmed24n0737.xml
|
| 035 |
|
|
|a (DE-627)NLM221202161
|
| 035 |
|
|
|a (NLM)22997128
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Kaâniche, Mohamed-Bécha
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Recognizing gestures by learning local motion signatures of HOG descriptors
|
| 264 |
|
1 |
|c 2012
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 29.04.2013
|
| 500 |
|
|
|a Date Revised 21.09.2012
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a We introduce a new gesture recognition framework based on learning local motion signatures (LMSs) of HOG descriptors introduced by [1]. Our main contribution is to propose a new probabilistic learning-classification scheme based on a reliable tracking of local features. After the generation of these LMSs computed on one individual by tracking Histograms of Oriented Gradient (HOG) [2] descriptor, we learn a codebook of video-words (i.e., clusters of LMSs) using k-means algorithm on a learning gesture video database. Then, the video-words are compacted to a code-book of codewords by the Maximization of Mutual Information (MMI) algorithm. At the final step, we compare the LMSs generated for a new gesture w.r.t. the learned code-book via the k-nearest neighbors (k-NN) algorithm and a novel voting strategy. Our main contribution is the handling of the N to N mapping between codewords and gesture labels within the proposed voting strategy. Experiments have been carried out on two public gesture databases: KTH [3] and IXMAS [4]. Results show that the proposed method outperforms recent state-of-the-art methods
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Brémond, François
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 34(2012), 11 vom: 01. Nov., Seite 2247-58
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:34
|g year:2012
|g number:11
|g day:01
|g month:11
|g pages:2247-58
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 34
|j 2012
|e 11
|b 01
|c 11
|h 2247-58
|