Publication: Visually Salient Region Detection in Omnidirectional Images Using Wavelet Textural Feature Map
| creativeworkseries.issn | 2079-3537 | |
| dc.contributor.author | Manisha Mane | |
| dc.contributor.author | Anand Bhaskar | |
| dc.date.accessioned | 2024-11-15T12:22:05Z | |
| dc.date.available | 2024-11-15T12:22:05Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Salient object detection is a crucial aspect of computer vision that involves identifying the most prominent area in a 2D image. However, predicting salient regions in omnidirectional images can be challenging due to their circular field of view.To facilitate saliency detection, pre and post-processing are required, which involves converting the image into an equirectangular projection (ERP). In this study, we propose a detailed approach to saliency detection for omnidirectional images using the wavelet domain. Our proposed model utilizes a 2-D wavelet transform to decompose and reconstruct images in the CIELAB space.The texture channel map is then calculated, followed by the feature map, where salient regions are marked using Gaussian filtering and entropy. Our experimental results demonstrate that this method is highly effective for detecting salient objects in omnidirectional images. | |
| dc.identifier.doi | 10.26583/sv.16.3.03 | |
| dc.identifier.uri | https://openrepository.mephi.ru/handle/123456789/16211 | |
| dc.identifier.uri | http://sv-journal.org/2024-3/03/ | |
| dc.publisher | НИЯУ МИФИ | |
| dc.subject | 2-D wavelet transform | |
| dc.subject | Visual saliency | |
| dc.subject | Saliency model | |
| dc.subject | Salient object map | |
| dc.subject | Saliency detection | |
| dc.subject | Omnidirectional image | |
| dc.subject | ERP | |
| dc.title | Visually Salient Region Detection in Omnidirectional Images Using Wavelet Textural Feature Map | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| relation.isJournalIssueOfPublication | 82afd5bd-dbb8-49b4-9638-2a008780a33e | |
| relation.isJournalIssueOfPublication.latestForDiscovery | 82afd5bd-dbb8-49b4-9638-2a008780a33e | |
| relation.isJournalOfPublication | 95b5bb8c-faac-4680-a70f-5adf56268bdc |