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240608s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2024.3410537
|2 doi
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|a pubmed25n1243.xml
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|a (DE-627)NLM373312423
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|a (NLM)38843060
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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100 |
1 |
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|a Marino, Emanuele
|e verfasserin
|4 aut
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1 |
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|a A novel AR recoloring technique to enhance operator performance on inspection tasks in Industry 4.0 environments
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|c 2024
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 25.06.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Over the past few years, the manufacturing industry has increasingly embraced Augmented Reality (AR) for inspecting real products, yet faces challenges in visualization modalities. In fact, AR content presentation significantly impacts user performance, especially when virtual object colors lack real-world context. Additionally, the lack of studies in this area compounds uncertainty about visualization effects on user performance in inspection tasks. This study introduces a novel AR recoloring technique to enhance user performance during industrial assembly inspection tasks. This technique automatically recolors virtual components based on their physical counterparts, improving distinctiveness. Experimental comparisons with AR experts and representative users, using objective and subjective metrics, demonstrate the proposed AR recoloring technique enhances task performance and reduces mental burden during inspection activities. This innovative approach outperforms established methods like CAD and random modes, showcasing its potential for advancing AR applications in manufacturing, particularly in the inspection of products
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|a Journal Article
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700 |
1 |
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|a Barbieri, Loris
|e verfasserin
|4 aut
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700 |
1 |
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|a Bruno, Fabio
|e verfasserin
|4 aut
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700 |
1 |
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|a Muzzupappa, Maurizio
|e verfasserin
|4 aut
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773 |
0 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g PP(2024) vom: 06. Juni
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnas
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773 |
1 |
8 |
|g volume:PP
|g year:2024
|g day:06
|g month:06
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|u http://dx.doi.org/10.1109/TVCG.2024.3410537
|3 Volltext
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|a AR
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|d PP
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