Attention-Aware DAE for Automated Solar Coronal Loop Segmentation
| dc.contributor.author | Dhaubhadel, Prabal Man | |
| dc.contributor.author | Lee, Jong Kwan | |
| dc.contributor.author | Tian, Qing | |
| dc.contributor.editor | Skala, Václav | |
| dc.date.accessioned | 2024-07-25T19:43:17Z | |
| dc.date.available | 2024-07-25T19:43:17Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract-translated | This paper introduces an enhanced Denosing Autoencoder (DAE) model, incorporating a novel attention mecha nism, for the segmentation of solar coronal loops. This work is based on DAE framework to address the segmenta tion challenges posed by intricate structures of coronal loops which also appear with other solar features and image noises. Specifically, we introduce Encoding-Aware Decoding Attention (EADA) to all decoding stages of DAE, which resulted in improvement in coronal loop segmentation. Our models are validated through experiments on a synthetic image dataset of 11,000 images and a test dataset of 165 real coronal images of the NASA’s Solar Dy namics Observatory (SDO) satellite mission. Compared to the state-of-the-art coronal loop segmentation baseline, our attention-enhanced model results in better loop gap-filling and higher segmentation metrics (i.e., 3.6% increase in accuracy, 11.4% better recall and 5.6% higher precision). | en |
| dc.description.sponsorship | This research was supported in part by the David and Amy Fulton Endowed Professorship in Computer Sci ence at Bowling Green State University. This work would not have been possible without the computing resources provided by the Ohio Supercomputer Cen ter. We also thank the reviewers for their valuable com ments which improved our pape | en |
| dc.format | 10 s | cs |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 67-76. | en |
| dc.identifier.doi | https://doi.org/10.24132/CSRN.3401.8 | |
| dc.identifier.issn | 2464–4625 (online) | |
| dc.identifier.issn | 2464–4617 (print) | |
| dc.identifier.uri | http://hdl.handle.net/11025/57378 | |
| dc.language.iso | en | en |
| dc.publisher | Václav Skala - UNION Agency | en |
| dc.rights | © Václav Skala - UNION Agency | en |
| dc.rights.access | openAccess | en |
| dc.subject | odšumovací autokodéry | cs |
| dc.subject | blok pozornosti | cs |
| dc.subject | segmentace solární koronální smyčky | cs |
| dc.subject | vyplnění mezer smyčky | cs |
| dc.subject.translated | denoising autoencoders | en |
| dc.subject.translated | attention block | en |
| dc.subject.translated | Solar Coronal Loop Segmentation | en |
| dc.subject.translated | loop gap-filling | en |
| dc.title | Attention-Aware DAE for Automated Solar Coronal Loop Segmentation | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.type.status | Peer reviewed | en |
| dc.type.version | publishedVersion | en |
Files
Original bundle
1 - 1 out of 1 results
No Thumbnail Available
- Name:
- A47-2024.pdf
- Size:
- 5.37 MB
- Format:
- Adobe Portable Document Format
- Description:
- Plný text
License bundle
1 - 1 out of 1 results
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed upon to submission
- Description: