|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM254291104 |
| 003 |
DE-627 |
| 005 |
20231224172047.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2016 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TVCG.2015.2467436
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0847.xml
|
| 035 |
|
|
|a (DE-627)NLM254291104
|
| 035 |
|
|
|a (NLM)26529731
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Dutta, Soumya
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis
|
| 264 |
|
1 |
|c 2016
|
| 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 05.02.2016
|
| 500 |
|
|
|a Date Revised 04.11.2015
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Effective analysis of features in time-varying data is essential in numerous scientific applications. Feature extraction and tracking are two important tasks scientists rely upon to get insights about the dynamic nature of the large scale time-varying data. However, often the complexity of the scientific phenomena only allows scientists to vaguely define their feature of interest. Furthermore, such features can have varying motion patterns and dynamic evolution over time. As a result, automatic extraction and tracking of features becomes a non-trivial task. In this work, we investigate these issues and propose a distribution driven approach which allows us to construct novel algorithms for reliable feature extraction and tracking with high confidence in the absence of accurate feature definition. We exploit two key properties of an object, motion and similarity to the target feature, and fuse the information gained from them to generate a robust feature-aware classification field at every time step. Tracking of features is done using such classified fields which enhances the accuracy and robustness of the proposed algorithm. The efficacy of our method is demonstrated by successfully applying it on several scientific data sets containing a wide range of dynamic time-varying features
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 700 |
1 |
|
|a Shen, Han-Wei
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 22(2016), 1 vom: 24. Jan., Seite 837-46
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnns
|
| 773 |
1 |
8 |
|g volume:22
|g year:2016
|g number:1
|g day:24
|g month:01
|g pages:837-46
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TVCG.2015.2467436
|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 2016
|e 1
|b 24
|c 01
|h 837-46
|