Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Cailong Deng"'
Publikováno v:
IEEE Access, Vol 11, Pp 106877-106897 (2023)
Due to the inherent limitations of matching algorithms and the complexities associated with image contents, mismatches are inevitable and can have detrimental effects on downstream tasks in computer vision and remote sensing. Researchers have publish
Externí odkaz:
https://doaj.org/article/115e02e710614eb79567c623572e8c0c
Publikováno v:
IEEE Access, Vol 11, Pp 57117-57136 (2023)
Most learning-based methods require labelling the training data, which is time-consuming and gives rise to wrong labels. To address the labelling issues thoroughly, we propose an unsupervised learning framework to remove mismatches by maximizing the
Externí odkaz:
https://doaj.org/article/fac627cc3ef54c7cb76bc4466e4de9be
Publikováno v:
IEEE Access, Vol 9, Pp 147634-147648 (2021)
Mismatching removal is at the core yet still a challenging problem in the photogrammetry and computer vision field. In this paper, we propose a coordinate embedding network (named CE-Net). We consider the mismatching problem as a graph node classific
Externí odkaz:
https://doaj.org/article/e241b86d2f1c4e0d8ecbfd0c4ff4b61b
Publikováno v:
Sensors, Vol 22, Iss 16, p 6110 (2022)
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 s
Externí odkaz:
https://doaj.org/article/606b5d4447c446909ee20ab790f9b6d0
Publikováno v:
Sensors, Vol 20, Iss 24, p 7050 (2020)
Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based
Externí odkaz:
https://doaj.org/article/bbdfd6226d514396bf87747391b94a55
Publikováno v:
Sensors, Vol 20, Iss 13, p 3712 (2020)
Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunde
Externí odkaz:
https://doaj.org/article/845a35208c554cba9500204b84a5da80
Publikováno v:
Sensors, Vol 20, Iss 7050, p 7050 (2020)
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based
Publikováno v:
Sensors (Basel, Switzerland)
Sensors, Vol 20, Iss 3712, p 3712 (2020)
Sensors
Volume 20
Issue 13
Sensors, Vol 20, Iss 3712, p 3712 (2020)
Sensors
Volume 20
Issue 13
Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunde