The Application of Supervised Machine Learning Algorithms for Image Alignment in Multi-Channel Imaging Systems
This study presents a method for aligning the geometric parameters of images in multi-channel imaging systems based on the application of pre-processing methods, machine learning algorithms, and a calibration setup using an array of orderly markers at the nodes of an imaginary grid. According to the...
Saved in:
Main Authors: | Kyrylo Romanenko, Yevgen Oberemok, Ivan Syniavskyi, Natalia Bezugla, Pawel Komada, Mykhailo Bezuglyi |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/25/2/544 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Progressive alignment and interwoven composition network for image stitching
by: Xiaoting Fan, et al.
Published: (2024-12-01) -
An Infrared and Visible Image Alignment Method Based on Gradient Distribution Properties and Scale-Invariant Features in Electric Power Scenes
by: Lin Zhu, et al.
Published: (2025-01-01) -
Computer simulation of diffractive imaging lenses using hyperspectral images
by: S.I. Kharitonov, et al.
Published: (2023-10-01) -
Efficient Multi-Task Training with Adaptive Feature Alignment for Universal Image Segmentation
by: Yipeng Qu, et al.
Published: (2025-01-01) -
Image of a University Professor: Students’ Views and Priorities
by: M. A. Lukashenko, et al.
Published: (2019-03-01)