ULMR: An Unsupervised Learning Framework for Mismatch Removal

Due to radiometric and geometric distortions between images, mismatches are inevitable. Thus, a mismatch removal process is required for improving matching accuracy. Although deep learning methods have been proved to outperform handcraft methods in specific scenarios, including image identification...

Descripción completa

Detalles Bibliográficos
Autores principales: Deng, Cailong, Chen, Shiyu, Zhang, Yong, Zhang, Qixin, Chen, Feiyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9413738/
https://www.ncbi.nlm.nih.gov/pubmed/36015871
http://dx.doi.org/10.3390/s22166110

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9413738/
https://www.ncbi.nlm.nih.gov/pubmed/36015871
http://dx.doi.org/10.3390/s22166110

Ejemplares similares