|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM280911297 |
| 003 |
DE-627 |
| 005 |
20231225030421.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2018 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2017.2783627
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0936.xml
|
| 035 |
|
|
|a (DE-627)NLM280911297
|
| 035 |
|
|
|a (NLM)29432098
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Gao, Bin
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging
|
| 264 |
|
1 |
|c 2018
|
| 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 30.07.2018
|
| 500 |
|
|
|a Date Revised 30.07.2018
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Li, Xiaoqing
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Woo, Wai Lok
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tian, Gui Yun
|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 27(2018), 5 vom: 01. Mai, Seite 2160-2175
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:27
|g year:2018
|g number:5
|g day:01
|g month:05
|g pages:2160-2175
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2017.2783627
|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 27
|j 2018
|e 5
|b 01
|c 05
|h 2160-2175
|