A Review of Synthetic Image Data and Its Use in Computer Vision
Development of computer vision algorithms using convolutional neural networks and deep learning has necessitated ever greater amounts of annotated and labelled data to produce high performance models. Large, public data sets have been instrumental in pushing forward computer vision by providing the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/8/11/310 |
| _version_ | 1797464929677082624 |
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| author | Keith Man Javaan Chahl |
| author_facet | Keith Man Javaan Chahl |
| author_sort | Keith Man |
| collection | DOAJ |
| description | Development of computer vision algorithms using convolutional neural networks and deep learning has necessitated ever greater amounts of annotated and labelled data to produce high performance models. Large, public data sets have been instrumental in pushing forward computer vision by providing the data necessary for training. However, many computer vision applications cannot rely on general image data provided in the available public datasets to train models, instead requiring labelled image data that is not readily available in the public domain on a large scale. At the same time, acquiring such data from the real world can be difficult, costly to obtain, and manual labour intensive to label in large quantities. Because of this, synthetic image data has been pushed to the forefront as a potentially faster and cheaper alternative to collecting and annotating real data. This review provides general overview of types of synthetic image data, as categorised by synthesised output, common methods of synthesising different types of image data, existing applications and logical extensions, performance of synthetic image data in different applications and the associated difficulties in assessing data performance, and areas for further research. |
| first_indexed | 2024-03-09T18:15:10Z |
| format | Article |
| id | doaj.art-dc47c8691b99441ba613a0cee44dd241 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T18:15:10Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-dc47c8691b99441ba613a0cee44dd2412023-11-24T08:51:46ZengMDPI AGJournal of Imaging2313-433X2022-11-0181131010.3390/jimaging8110310A Review of Synthetic Image Data and Its Use in Computer VisionKeith Man0Javaan Chahl1UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaUniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaDevelopment of computer vision algorithms using convolutional neural networks and deep learning has necessitated ever greater amounts of annotated and labelled data to produce high performance models. Large, public data sets have been instrumental in pushing forward computer vision by providing the data necessary for training. However, many computer vision applications cannot rely on general image data provided in the available public datasets to train models, instead requiring labelled image data that is not readily available in the public domain on a large scale. At the same time, acquiring such data from the real world can be difficult, costly to obtain, and manual labour intensive to label in large quantities. Because of this, synthetic image data has been pushed to the forefront as a potentially faster and cheaper alternative to collecting and annotating real data. This review provides general overview of types of synthetic image data, as categorised by synthesised output, common methods of synthesising different types of image data, existing applications and logical extensions, performance of synthetic image data in different applications and the associated difficulties in assessing data performance, and areas for further research.https://www.mdpi.com/2313-433X/8/11/310computer visionimage synthesissynthetic image datasynthetic data generation |
| spellingShingle | Keith Man Javaan Chahl A Review of Synthetic Image Data and Its Use in Computer Vision Journal of Imaging computer vision image synthesis synthetic image data synthetic data generation |
| title | A Review of Synthetic Image Data and Its Use in Computer Vision |
| title_full | A Review of Synthetic Image Data and Its Use in Computer Vision |
| title_fullStr | A Review of Synthetic Image Data and Its Use in Computer Vision |
| title_full_unstemmed | A Review of Synthetic Image Data and Its Use in Computer Vision |
| title_short | A Review of Synthetic Image Data and Its Use in Computer Vision |
| title_sort | review of synthetic image data and its use in computer vision |
| topic | computer vision image synthesis synthetic image data synthetic data generation |
| url | https://www.mdpi.com/2313-433X/8/11/310 |
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