| Title: | Unsupervised SIFT features-to-Image Translation using CycleGAN |
| Authors: | Mackowiak, Sławomir Brudz, Patryk Ciesielski, Mikołaj Wawrzyniak, Maciej |
| Citation: | WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 217-226. |
| Issue Date: | 2021 |
| Publisher: | Václav Skala - UNION Agency |
| Document type: | conferenceObject konferenční příspěvek |
| URI: | http://hdl.handle.net/11025/45027 |
| ISBN: | 978-80-86943-34-3 |
| ISSN: | 2464-4617 2464–4625(CD/DVD) |
| Keywords: | SIFT;funkce;klíčové body;CycleGAN |
| Keywords in different language: | SIFT;features;keypoints;CycleGAN |
| Abstract in different language: | The generation of video content from a small set of data representing the features of objects has very promising application prospects. This is particularly important in the context of the work of the MPEG Video Coding for Machine group, where various efforts are being undertaken related to efficient image coding for machines and humans. The representation of feature points well understood by machines in a video form, which is easy to understand by humans, is an important current challenge. This paper presents results on the ability to generate images from a set of SIFT feature points without descriptors using the generative adversarial network CycleGAN. The impact of the SIFT keypoint representation method on the learning quality of the network is presented. The results and a subjective evaluation of the generated images are presented. |
| Rights: | © Václav Skala - UNION Agency |
| Appears in Collections: | WSCG 2021: Full Papers Proceedings |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/45027Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.