EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 15 (2024) Int. J. Simul. Multidisci. Des. Optim., 15 (2024) 10 References
Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 15, 2024
Article Number 10
Number of page(s) 13
DOI https://doi.org/10.1051/smdo/2024006
Published online 24 May 2024
  1. J. Fang, Z. Feng, B. Cai, DrawnNet: offline hand-drawn diagram recognition based on the keypoint prediction of aggregating geometric characteristics, Entropy 24, 425 (2022) [CrossRef] [MathSciNet] [Google Scholar]
  2. S. Zhang, Research on energy-saving packaging design based on artificial intelligence, Energy Rep. 8, 480–489 (2022) [CrossRef] [Google Scholar]
  3. M. Hudon, S. Lutz, R. Pagés, A. Smolic, Augmenting hand-drawn art with global illumination effects through surface inflation, in Proceedings of the 16th ACM SIGGRAPH European Conference on Visual Media Production (2019) pp. 1–9. [Google Scholar]
  4. C. Bou-Mitri, M. Abdessater, H. Zgheib, Z. Akiki, Food packaging design and consumer perception of the product quality, safety, healthiness, and preference, Nutr. Food Sci. 51, 71–86 (2020) [Google Scholar]
  5. M. Ketelsen, M. Janssen, U. Hamm, Consumers’ response to environmentally-friendly food packaging − a systematic review, J. Cleaner Prod 254, 120123 (2020) [CrossRef] [Google Scholar]
  6. Y. Unal, Y.S. Taspinar, I. Cinar, R. Kursun, M. Koklu, Application of pre-trained deep convolutional neural networks for coffee beans species detection, Food Anal. Methods 15, 3232–3243 (2020) [Google Scholar]
  7. G. Simmonds, C. Spence, Food imagery and transparency in product packaging. Multisensory packaging: Designing new product experiences (2019). pp. 49–77 [CrossRef] [Google Scholar]
  8. Y. Feng, Application and Value of Hand-painted Illustration in Food Packaging Design, BCP Social Sciences and Humanities. ADCS 2022. 15, 77–81 (2022) [Google Scholar]
  9. O. Rakhmanov, Testing strength of the state-of-art image classification methods for hand drawn sketches, in 2019 15th International Conference on Electronics, Computer and Computation (ICECCO). IEEE (2019). pp. 1–3 [Google Scholar]
  10. B. Schäfer, H. Stuckenschmidt, DiagramNet: hand-drawn diagram recognition using visual arrow-relation detection, in Document Analysis and Recognition-ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5-10, 2021, Proceedings, Part I 16. Springer International Publishing (2021) pp. 614–630. [CrossRef] [Google Scholar]
  11. S. Roy, A. Bhattacharya, N. Sarkar, S. Malakar, R. Sarkar, Offline hand-drawn circuit component recognition using texture and shape-based features, Multimedia Tools Appl. 79, 31353–31373 (2020) [CrossRef] [Google Scholar]
  12. O. Rakhmanov, A novel algorithm to classify hand drawn sketches with respect to content quality, in Computational Science and Its Applications-ICCSA 2020: 20th International Conference, Cagliari, Italy, July 1-4, 2020, Proceedings, Part IV 20. Springer International Publishing (2020). pp. 179–193 [Google Scholar]
  13. H. Shaukat, S. Kun, M. Muhammad, S. Parinya, A.A.M. Abdullah, Tuning-up learning parameters for deep convolutional neural network: a case study for hand-drawn sketch images, J. Electr. Sci. Technol. 20, 305–318 (2022) [Google Scholar]
  14. J. Xin, G. Yan, Q. Song, Application of patent right and trademark right in packaging design based on computer nonlinear prediction systems for virtual reality technology, Sci. Program. 2022, 7 (2022) [Google Scholar]
  15. S. Wu, Interactive 3D Digital Art in Modern Packaging Design. Scholar Publishing Group, International Journal of Art Innovation and Development. ISSN 2789-7192 3, Issue 3: 1–18 (2022) [Google Scholar]
  16. M. Favier, F. Celhay, G. Pantin-Sohier, Is less more or a bore? Package design simplicity and brand perception: an application to Champagne, J. Retailing Consumer Serv. 46, 11–20 (2019) [CrossRef] [Google Scholar]
  17. Y. Wu, Application of autoCAD in graphic art design based on VBA, Comput. Aided Design 18, 75–86 (2021) [Google Scholar]
  18. G. Cascini, J. O’Hare, E. Dekoninck, N. Becattini, J.F. Boujut, F.B. Guefrache, I. Carli, G. Caruso, L. Giunta, F. Morosi, Exploring the use of AR technology for co-creative product and packaging design, Comput. Ind. 123, 103308 (2020) [CrossRef] [Google Scholar]
  19. S. Fortunati, L. Martiniello, D. Morea, The strategic role of the corporate social responsibility and circular economy in the cosmetic industry, Sustainability 12, 5120 (2020) [Google Scholar]
  20. T.Y. Wu, Y.J. Li, Y. Liu, Study of color emotion impact on leisure food package design, in HCI International 2017-Posters’ Extended Abstracts: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part II 19. Springer International Publishing (2017). pp. 612–619 [Google Scholar]
  21. J. Hao, S. Luo, L. Pan, Computer-aided intelligent design using deep multi-objective cooperative optimization algorithm, Future Generation Comput. Syst. 124, 49–53 (2021) [CrossRef] [Google Scholar]
  22. Z. Zhao, H. Zheng, Y. Liu, The appearance design of agricultural product packaging art style under the intelligent computer aid, Comput. Aided Des. Appl. 19, 164–173 (2021) [CrossRef] [Google Scholar]
  23. W. Yu, P. Singh, Application of CAD in product packaging design based on green concept, Comput. Aided Des. Appl. 19, 124–133 (2021) [CrossRef] [Google Scholar]
  24. G.M. Kontogeorgis, S. Jhamb, X. Liang, K. Dam-Johansen, Computer-aided design of formulated products, Curr. Opin. Colloid Interface Sci. 57, 12–18 (2022) [Google Scholar]
  25. T. Vasileiadis, A. Tzotzis, D. Tzetzis, P. Kyratsis, Combining product and packaging design for increased added value and customer satisfaction, J. Graphic Eng. Des. 10, 5–15 (2019) [CrossRef] [Google Scholar]
  26. W. Zhou, J. Jia, Training convolutional neural network for sketch recognition on large-scale dataset, Int. Arab J. Inf. Technol. 17, 82–89 (2020) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.