The DOI matches its Crossref record; 239 of the 239 references it cites were found in a registry or an index.
Survey on semantic segmentation using deep learning techniques
Open accessAbstract
Abstract Semantic segmentation is a challenging task in computer vision systems. A lot of methods have been developed to tackle this problem ranging from autonomous vehicles, human-computer interaction, to robotics, medical research, agriculture and so on. Many of these methods have been built using the deep learning paradigm that has shown a salient performance. For this reason, we propose to survey these methods by, first categorizing them into ten different classes according to the common concepts underlying their architectures. Second, by providing an overview of the publicly available datasets on which they have been assessed. In addition, we present the common evaluation matrix used to measure their accuracy. Moreover, we focus on some of the methods and look closely at their architectures in order to find out how they have achieved their reported performances. Finally, we conclude by discussing some of the open problems and their possible solutions.
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1 retracted
RETRACTED ARTICLE: Medical image semantic segmentation based on deep learning
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Cited by
Deep semantic segmentation of natural and medical images: a review
Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J., & Hamarneh, G. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09854-1 · Cited by 870
A Review of Deep-Learning-Based Medical Image Segmentation Methods
Liu, X., Song, L., Liu, S., & Zhang, Y. (2021) · Sustainability · doi:10.3390/su13031224 · Cited by 868
Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes
Sun, Y., Zuo, W., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2904733 · Cited by 546
Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188
Wang, Y., Luo, Q., Xie, H., Li, Q., & Sun, G. (2022) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2022.107529 · Cited by 149
Metadata
- Authors
- Lateef, F., & Ruichek, Y.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 338
- Pages
- 321-348
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2912327653
- Cited by
- 559
- References
- 239
- FWCI
- 35.08
- Type
- journal article
- Language
- en
- Open access
- bronze
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