Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis
Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The co...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5541134 |
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| author | R. Nandhini Abirami P. M. Durai Raj Vincent Kathiravan Srinivasan Usman Tariq Chuan-Yu Chang |
| author_facet | R. Nandhini Abirami P. M. Durai Raj Vincent Kathiravan Srinivasan Usman Tariq Chuan-Yu Chang |
| author_sort | R. Nandhini Abirami |
| collection | DOAJ |
| description | Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides. |
| format | Article |
| id | doaj-art-8a232e84a2324a4d85dcf8002256211a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8a232e84a2324a4d85dcf8002256211a2025-08-20T03:37:49ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55411345541134Deep CNN and Deep GAN in Computational Visual Perception-Driven Image AnalysisR. Nandhini Abirami0P. M. Durai Raj Vincent1Kathiravan Srinivasan2Usman Tariq3Chuan-Yu Chang4School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, TaiwanComputational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides.http://dx.doi.org/10.1155/2021/5541134 |
| spellingShingle | R. Nandhini Abirami P. M. Durai Raj Vincent Kathiravan Srinivasan Usman Tariq Chuan-Yu Chang Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis Complexity |
| title | Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis |
| title_full | Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis |
| title_fullStr | Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis |
| title_full_unstemmed | Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis |
| title_short | Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis |
| title_sort | deep cnn and deep gan in computational visual perception driven image analysis |
| url | http://dx.doi.org/10.1155/2021/5541134 |
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