EGO-VC – Evolutionary GPU-Optimization of Visual Correspondences for Image Alignment
| dc.contributor.author | Chang, Thomas Vincent | |
| dc.contributor.author | Hartmann, Kay | |
| dc.contributor.author | Kuth, Bastian | |
| dc.contributor.author | Seibt, Simon | |
| dc.contributor.author | von Rymon Lipinski, Bartosz | |
| dc.contributor.editor | Skala, Václav | |
| dc.date.accessioned | 2025-07-30T09:15:16Z | |
| dc.date.available | 2025-07-30T09:15:16Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract-translated | Many computer vision applications, such as panoramic stitching, image morphing, and image registration depend on precise feature correspondences. While current machine-learning-based methods excel at detecting precise feature matches, they do not necessarily guarantee visual alignment. This work presents an evolutionary, mesh-based optimization framework that enhances alignment quality and improves visual coherence as a postprocessing step for any feature-matching algorithm: The approach employs the well-known enhanced correlation coefficient (ECC) as a visual error metric, efficiently computed in parallel using the rasterization capabilities of modern graphics hardware. Given any state-of-the-art feature matcher, the proposed method constructs a Delaunay feature point mesh and evolutionarily refines the ECC alignment of each generated triangle. This results in a more precise registration beyond the accuracy of initial feature matches. Extensive evaluation across multiple standard image datasets confirms the proposed method’s effectiveness, yielding ECC improvements up to 18.3% and ensuring high visual quality in downstream applications, particularly in challenging image areas with occlusions. For morphing-based applications, this leads to sharper, smoother transitions between image pairs, minimizing visual artifacts. | en |
| dc.description.sponsorship | This work is funded by the Federal Ministry of Education and Research (BMBF Germany, No. 01IS23007B) | |
| dc.format | 8 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | http://www.doi.org/10.24132/CSRN.2025-8 | |
| dc.identifier.issn | 2464-4617 (Print) | |
| dc.identifier.issn | 2464-4625 (online) | |
| dc.identifier.uri | http://hdl.handle.net/11025/62214 | |
| dc.language.iso | en | en |
| dc.publisher | Vaclav Skala - UNION Agency | en |
| dc.rights | © Vaclav Skala - UNION Agency | en |
| dc.rights.access | openAccess | en |
| dc.subject | počítačové vidění | cs |
| dc.subject | zarovnání obrazu | cs |
| dc.subject | ECC | cs |
| dc.subject | obrazové korespondence | cs |
| dc.subject | porovnávání rysů | cs |
| dc.subject | evoluční optimalizace | cs |
| dc.subject.translated | computer vision | en |
| dc.subject.translated | image alignment | en |
| dc.subject.translated | ECC | en |
| dc.subject.translated | image correspondences | en |
| dc.subject.translated | feature matching | en |
| dc.subject.translated | evolutionary optimization | en |
| dc.title | EGO-VC – Evolutionary GPU-Optimization of Visual Correspondences for Image Alignment | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.type.status | Peer reviewed | en |
| dc.type.version | publishedVersion | en |
| local.files.count | 1 | * |
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