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How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison
Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. It is the way to perform the extraction by checking pixels by pixel using a classification approach. It gives us more accurate and fine details from the data we need for further ev...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986028/ https://www.ncbi.nlm.nih.gov/pubmed/35411201 http://dx.doi.org/10.1007/s11042-022-12821-3 |
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author | Sehar, Uroosa Naseem, Muhammad Luqman |
author_facet | Sehar, Uroosa Naseem, Muhammad Luqman |
author_sort | Sehar, Uroosa |
collection | PubMed |
description | Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. It is the way to perform the extraction by checking pixels by pixel using a classification approach. It gives us more accurate and fine details from the data we need for further evaluation. Formerly, we had a few techniques based on some unsupervised learning perspectives or some conventional ways to do some image processing tasks. With the advent of time, techniques are improving, and we now have more improved and efficient methods for segmentation. Image segmentation is slightly simpler than semantic segmentation because of the technical perspective as semantic segmentation is pixels based. After that, the detected part based on the label will be masked and refer to the masked objects based on the classes we have defined with a relevant class name and the designated color. In this paper, we have reviewed almost all the supervised and unsupervised learning algorithms from scratch to advanced and more efficient algorithms that have been done for semantic segmentation. As far as deep learning is concerned, we have many techniques already developed until now. We have studied around 120 papers in this research area. We have concluded how deep learning is helping in solving the critical issues of semantic segmentation and gives us more efficient results. We have reviewed and comprehensively studied different surveys on semantic segmentation, specifically using deep learning. |
format | Online Article Text |
id | pubmed-8986028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-89860282022-04-07 How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison Sehar, Uroosa Naseem, Muhammad Luqman Multimed Tools Appl Article Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. It is the way to perform the extraction by checking pixels by pixel using a classification approach. It gives us more accurate and fine details from the data we need for further evaluation. Formerly, we had a few techniques based on some unsupervised learning perspectives or some conventional ways to do some image processing tasks. With the advent of time, techniques are improving, and we now have more improved and efficient methods for segmentation. Image segmentation is slightly simpler than semantic segmentation because of the technical perspective as semantic segmentation is pixels based. After that, the detected part based on the label will be masked and refer to the masked objects based on the classes we have defined with a relevant class name and the designated color. In this paper, we have reviewed almost all the supervised and unsupervised learning algorithms from scratch to advanced and more efficient algorithms that have been done for semantic segmentation. As far as deep learning is concerned, we have many techniques already developed until now. We have studied around 120 papers in this research area. We have concluded how deep learning is helping in solving the critical issues of semantic segmentation and gives us more efficient results. We have reviewed and comprehensively studied different surveys on semantic segmentation, specifically using deep learning. Springer US 2022-04-06 2022 /pmc/articles/PMC8986028/ /pubmed/35411201 http://dx.doi.org/10.1007/s11042-022-12821-3 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Sehar, Uroosa Naseem, Muhammad Luqman How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison |
title | How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison |
title_full | How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison |
title_fullStr | How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison |
title_full_unstemmed | How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison |
title_short | How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison |
title_sort | how deep learning is empowering semantic segmentation: traditional and deep learning techniques for semantic segmentation: a comparison |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986028/ https://www.ncbi.nlm.nih.gov/pubmed/35411201 http://dx.doi.org/10.1007/s11042-022-12821-3 |
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