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Image analysis for 3D micro-features: A new hybrid measurement method
- Source :
- Precision engineering 48 (2017): 123–132. doi:10.1016/j.precisioneng.2016.11.012, info:cnr-pdr/source/autori:Percoco G., Modica F., Fanelli S./titolo:Image analysis for 3D micro-features: A new hybrid measurement method/doi:10.1016%2Fj.precisioneng.2016.11.012/rivista:Precision engineering/anno:2017/pagina_da:123/pagina_a:132/intervallo_pagine:123–132/volume:48
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- Measuring 3D micro-features is a challenging task that is usually performed using high-cost systems generally based on technologies with narrow working ranges and very accurate control of the sensor positions. Well-known image analysis methods, such as Photogrammetry, would likely lower the costs of 3D inspection of micro-features and add texture information to the 3D models; however, the behaviour of Photogrammetry is strongly affected by the scaling method because it retrieves a model that must be scaled after its computation. In this paper, an experimental study of the validity of a hybrid 3D image processing method for measuring micro-features is presented. This method exploits the Depth-from-Focus method to retrieve the correct scale for a photogrammetric model. The measurement of properly-designed and manufactured specimens was performed by following and adapting the German guideline VDI/VDE 2634, Part 3 to validate the method using calibrated specimens. The proposed system has been demonstrated to be very promising can achieve an error of less than 10 μm.
- Subjects :
- 0209 industrial biotechnology
Engineering
Scale (ratio)
Computation
3d model
02 engineering and technology
01 natural sciences
Image (mathematics)
010309 optics
WireEDM
020901 industrial engineering & automation
Micro-features
0103 physical sciences
Computer vision
Scaling
Measurement
Measurement method
business.industry
General Engineering
Depth from focus
International standards
Photogrammetry
Scale
Task (computing)
Depth from Focus
Artificial intelligence
business
Subjects
Details
- ISSN :
- 01416359
- Volume :
- 48
- Database :
- OpenAIRE
- Journal :
- Precision Engineering
- Accession number :
- edsair.doi.dedup.....e18225d1f5d83a50aa0acb9715156eb2
- Full Text :
- https://doi.org/10.1016/j.precisioneng.2016.11.012