The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Fruit recognition from images using deep learning
Open accessAbstract
In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use such classifier.
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Chan, T. F., & Vese, L. A. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.902291 · Cited by 10,304
Optimal approximations by piecewise smooth functions and associated variational problems
Mumford, D., & Shah, J. (1989) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.3160420503 · Cited by 5,208
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
Multi-column deep neural networks for image classification
Ciresan, D., Meier, U., & Schmidhuber, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248110 · Cited by 3,720
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.06228 · Cited by 1,246
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Cited by
Machine Learning Testing: Survey, Landscapes and Horizons
Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2962027 · Cited by 897
A Review of Convolutional Neural Network Applied to Fruit Image Processing
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R. J., Fredes, C., & Valenzuela, A. (2020) · Applied Sciences · doi:10.3390/app10103443 · Cited by 406
Min, W., Jiang, S., Liu, L., Rui, Y., & Jain, R. (2020) · ACM Computing Surveys · doi:10.1145/3329168 · Cited by 341
PVGAN: a generative adversarial network for object simplification in prosthetic vision
Elnabawy, R. H., Abdennadher, S., Hellwich, O., & Eldawlatly, S. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac8acf · Cited by 3
Metadata
- Authors
- Mureşan, H., & Oltean, M.
- Venue
- Acta Universitatis Sapientiae, Informatica
- Published
- 2018-08-01
- Volume
- 10
- Issue
- 1
- Pages
- 26-42
- ISSN
- 2066-7760
- OpenAlex
- W2963912358
- Cited by
- 426
- References
- 22
- FWCI
- 23.80
- Type
- journal article
- Language
- en
- Open access
- green
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- Mureşan, H., & Oltean, M. (2018). Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica. https://doi.org/10.2478/ausi-2018-0002
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
