Video Stitching
| dc.contributor.author | DJAREDDIR ,Kawther | |
| dc.contributor.author | TOUIL, GHASSEN | |
| dc.date.accessioned | 2025-11-12T13:21:00Z | |
| dc.date.available | 2025-11-12T13:21:00Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This project explores video stitching—a process that merges multiple video streams into a single panoramic output. Building on image stitching techniques like feature detection, homography estimation, and blending, it addresses video-specific challenges such as temporal consistency, motion handling, and real-time processing. A Python-based application was developed using OpenCV and PyQt6, offering both simple and advanced stitching modes. Advanced methods include hybrid stitching, attention-based blending, and GAN-based inpainting. Evaluations show that while fast methods are efficient, advanced techniques produce superior visual quality, making the system suitable for applications in VR, surveillance, and mapping | |
| dc.identifier.uri | http://dspace.univ-skikda.dz:4000/handle/123456789/5394 | |
| dc.language.iso | en | |
| dc.publisher | Faculty of Science | |
| dc.title | Video Stitching | |
| dc.title.alternative | Information Systems and Software Engineering | |
| dc.type | Mémoire de Master |