|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM184452597 |
| 003 |
DE-627 |
| 005 |
20231223170649.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231223s2008 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2008.2006662
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0615.xml
|
| 035 |
|
|
|a (DE-627)NLM184452597
|
| 035 |
|
|
|a (NLM)19004716
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Shen, Xilin
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Detection and segmentation of concealed objects in terahertz images
|
| 264 |
|
1 |
|c 2008
|
| 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 14.01.2009
|
| 500 |
|
|
|a Date Revised 13.11.2008
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a Terahertz imaging makes it possible to acquire images of objects concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject. The goal of this work is to automatically detect and segment concealed objects in broadband 0.1-1 THz images. Due to the inherent physical properties of passive terahertz imaging and associated hardware, images have poor contrast and low signal to noise ratio. Standard segmentation algorithms are unable to segment or detect concealed objects. Our approach relies on two stages. First, we remove the noise from the image using the anisotropic diffusion algorithm. We then detect the boundaries of the concealed objects. We use a mixture of Gaussian densities to model the distribution of the temperature inside the image. We then evolve curves along the isocontours of the image to identify the concealed objects. We have compared our approach with two state-of-the-art segmentation methods. Both methods fail to identify the concealed objects, while our method accurately detected the objects. In addition, our approach was more accurate than a state-of-the-art supervised image segmentation algorithm that required that the concealed objects be already identified. Our approach is completely unsupervised and could work in real-time on dedicated hardware
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 700 |
1 |
|
|a Dietlein, Charles R
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Grossman, Erich
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Popovic, Zoya
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Meyer, François G
|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 17(2008), 12 vom: 01. Dez., Seite 2465-75
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:17
|g year:2008
|g number:12
|g day:01
|g month:12
|g pages:2465-75
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2008.2006662
|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 17
|j 2008
|e 12
|b 01
|c 12
|h 2465-75
|