Research output per year
Research output per year
Project Details
Description
Product aesthetics is a main quality parameter to be assessed in Danish manufacturing. Today this is mainly done manually and often the decision lies with one specific individual.
This project first of all aims at a better understanding of this process across different products and companies. Secondly, the project aims at automating/digitalizing the process, with the twofold goal of saving labor and making aesthetic quality less subjective and more quantifiable.
Manufacturing Academy of Denmark (MADE) - MADE Digital - Work Package 9
https://www.made.dk/digital/sensor-technology-and-production-data/
This project first of all aims at a better understanding of this process across different products and companies. Secondly, the project aims at automating/digitalizing the process, with the twofold goal of saving labor and making aesthetic quality less subjective and more quantifiable.
Manufacturing Academy of Denmark (MADE) - MADE Digital - Work Package 9
https://www.made.dk/digital/sensor-technology-and-production-data/
| Short title | Aesthetic Quality Control |
|---|---|
| Status | Finished |
| Effective start/end date | 01/03/2017 → 01/08/2020 |
Collaborative partners
- Bang & Olufsen A/S
- LEGO Systems AS
- VOLA
- Danish Technological Institute
- JLI Vision
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Defect or Design? Leveraging the Angle of Opportunity for detecting Scratches on Brushed Aluminium Surfaces
Hansen, A. J., Moeslund, T. B. & Knoche, H., 20 Jul 2021, In: IEEE Access. 9, p. 99526-99538 13 p., 9468644.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile24 Downloads (Pure) -
Machine Vision for Aesthetic Quality Control of Reflective Surfaces
Hansen, A. J., Philipsen, M. P., Knoche, H. & Moeslund, T. B., 2021, Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) . Hassanien, A. E., Haqiq, A., Tonellato, P. J., Bellatreche, L., Goundar, S., Azar, A. T., Sabir, E. & Bouzidi, D. (eds.). Springer, Vol. 1377. p. 389-401 13 p. (Advances in Intelligent Systems and Computing).Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
Open AccessFile85 Downloads (Pure) -
Fantastic plastic? An Image-based Test Method to Detect Aesthetic Defects in Batches Based on Reference Samples
Hansen, A. J., Knoche, H. & Moeslund, T. B., 1 Sept 2020, In: Polymer Testing. 89, 106585.Research output: Contribution to journal › Journal article › Research › peer-review
File2 Citations (Scopus)120 Downloads (Pure)