Global Convolutional Neural Networks with Self-Attention for Fisheye Image Rectification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Byunghyun | - |
dc.contributor.author | Lee, Dohyun | - |
dc.contributor.author | Min, Kyeongyuk | - |
dc.contributor.author | Chong, Jongwha | - |
dc.contributor.author | Joe, Inwhee | - |
dc.date.accessioned | 2023-01-25T10:07:35Z | - |
dc.date.available | 2023-01-25T10:07:35Z | - |
dc.date.created | 2023-01-05 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182220 | - |
dc.description.abstract | Fisheye images are attracting attention in computer vision such as autonomous vehicles and virtual reality because of the wide field of view (WFoV). However, fisheye images have geometric distortions caused by the refractive index of the lens. Conventional fisheye rectification methods require multiple images to calculate distortion coefficients and lens intrinsic parameters. This means that if the fisheye lens is changed, the same operation will have to be repeated. On the other hand, by using deep learning, images with different distortion coefficients can be rectified. Also, with end-to-end learning, no feature engineering is required. To improve the performance of fisheye image rectification, we propose global convolutional neural networks with self-attention to rectify the fisheye images. The proposed method employs dilated convolutional neural networks (D-CNNs) to enlarge receptive fields, and self-attention to extract the most important features of input images. In this way, the proposed method can extract global features from input images. To better train and evaluate the proposed method, we generate fisheye images from the Place2 dataset with Cartesian and polar coordinates, and label them with original images (ground-truth). we also schedule the learning rate with cosine annealing and use an integrated loss function. The experimental results show that the proposed method achieves an excellent performance in both qualitative and quantitative evaluations. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Global Convolutional Neural Networks with Self-Attention for Fisheye Image Rectification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Joe, Inwhee | - |
dc.identifier.doi | 10.1109/ACCESS.2022.3228297 | - |
dc.identifier.scopusid | 2-s2.0-85144761047 | - |
dc.identifier.wosid | 000902050800001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.10, pp.129580 - 129587 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 10 | - |
dc.citation.startPage | 129580 | - |
dc.citation.endPage | 129587 | - |
dc.type.rims | ART | - |
dc.type.docType | Article in Press | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | Bandpass filters | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Refractive index | - |
dc.subject.keywordPlus | Virtual reality | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Dilated convolution | - |
dc.subject.keywordPlus | Features extraction | - |
dc.subject.keywordPlus | Fisheye image rectification | - |
dc.subject.keywordPlus | Fisheye images | - |
dc.subject.keywordPlus | Image rectification | - |
dc.subject.keywordPlus | Information filter | - |
dc.subject.keywordPlus | Self-attention | - |
dc.subject.keywordAuthor | Fisheye image rectification | - |
dc.subject.keywordAuthor | dilated convolution | - |
dc.subject.keywordAuthor | self-attention | - |
dc.subject.keywordAuthor | deep learning | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9980359 | - |
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