6533b7d0fe1ef96bd125ba14
RESEARCH PRODUCT
HD-RTI: an adaptive multi-light imaging approach for the quality assessment of manufactured surfaces
Gaëtan Le GoïcHugues FavreliereAbir ZendaguiPierre JochumAlamin MansouriYuly CastroHermine ChatouxMarvin NuritDavid A. LewisStéphane Manigliersubject
Surface (mathematics)0209 industrial biotechnologyGeneral Computer ScienceComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyMachine visionSet (abstract data type)020901 industrial engineering & automationQuality (physics)[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingRobustness (computer science)0202 electrical engineering electronic engineering information engineeringComputer visionComputingMethodologies_COMPUTERGRAPHICSCouplingbusiness.industryQuality assessmentGeneral Engineering[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Variable (computer science)Quality inspection020201 artificial intelligence & image processingArtificial intelligenceMaterial AppearancebusinessPolynomial texture mappingdescription
International audience; Reflectance Transformation Imaging (RTI) is a technique for estimating surface local angular reflectance from a set of stereo-photometric images captured with variable lighting directions. The digitization of this information fully fits into the industry 4.0 approach and makes it possible to characterize the visual properties of a surface. The proposed method, namely HD-RTI, is based on the coupling of RTI and HDR imaging techniques. This coupling is carried out adaptively according to the response at each angle of illumination. The proposed method is applied to five industrial samples which have high local variations of reflectivity because of their heterogeneity of geometric texture and/or material. Results show that coupling HDR and RTI improves the relighting quality compared to RTI, and makes the proposed approach particularly relevant for glossy and heterogeneous surfaces. Moreover, HD-RTI enhances significantly the characterization of the local angular reflectance, which leads to more discriminating visual saliency maps, and more generally to an increase in robustness for visual quality assessment tasks.
year | journal | country | edition | language |
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2021-11-01 |