|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM219884056 |
| 003 |
DE-627 |
| 005 |
20231224044022.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2013 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2012.2210234
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0733.xml
|
| 035 |
|
|
|a (DE-627)NLM219884056
|
| 035 |
|
|
|a (NLM)22851258
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Chen, Jie
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Automatic dynamic texture segmentation using local descriptors and optical flow
|
| 264 |
|
1 |
|c 2013
|
| 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 03.06.2013
|
| 500 |
|
|
|a Date Revised 27.12.2012
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a A dynamic texture (DT) is an extension of the texture to the temporal domain. How to segment a DT is a challenging problem. In this paper, we address the problem of segmenting a DT into disjoint regions. A DT might be different from its spatial mode (i.e., appearance) and/or temporal mode (i.e., motion field). To this end, we develop a framework based on the appearance and motion modes. For the appearance mode, we use a new local spatial texture descriptor to describe the spatial mode of the DT; for the motion mode, we use the optical flow and the local temporal texture descriptor to represent the temporal variations of the DT. In addition, for the optical flow, we use the histogram of oriented optical flow (HOOF) to organize them. To compute the distance between two HOOFs, we develop a simple effective and efficient distance measure based on Weber's law. Furthermore, we also address the problem of threshold selection by proposing a method for determining thresholds for the segmentation method by an offline supervised statistical learning. The experimental results show that our method provides very good segmentation results compared to the state-of-the-art methods in segmenting regions that differ in their dynamics
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Zhao, Guoying
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Salo, Mikko
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Rahtu, Esa
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Pietikäinen, Matti
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 22(2013), 1 vom: 15. Jan., Seite 326-39
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:22
|g year:2013
|g number:1
|g day:15
|g month:01
|g pages:326-39
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2012.2210234
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 22
|j 2013
|e 1
|b 15
|c 01
|h 326-39
|