ArticleJournal of imaging2020
Extension of the Thermographic Signal Reconstruction Technique for an Automated Segmentation and Depth Estimation of Subsurface Defects.
Article in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Multi-representation thermal features for enhanced defect analysis in pulse thermography.Scientific reports · 2026Article
- In-Situ Pixel-wise Emissivity Measurement Using a Multispectral Infrared Camera.Journal of imaging · 2023Article
- Three-Dimensional Printed Subsurface Defect Detection by Active Thermography Data-Processing Algorithm.3D printing and additive manufacturing · 2023Article
- Multispectral High Temperature Thermography.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
With increased use of light-weight materials with low factors of safety, non-destructive testing becomes increasingly important. Thanks to the advancement of infrared camera technology, pulse thermography is a cost efficient way to detect subsurface defects non-destructively. However, currently available evaluation algorithms have either a high computational cost or show poor performance if any geometry other than the most simple kind is surveyed. We present an extension of the thermographic signal reconstruction technique which can automatically segment and image defects from sound areas, while also estimating the defect depth, all with low computational cost. We verified our algorithm using real world measurements and compare our results to standard active thermography algorithms with similar computational complexity. We found that our algorithm can detect defects more accurately, especially when more complex geometries are examined.
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Registered trials
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