A Robust Method for Camera Calibration in Noisy Settings Based on Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8360
- @InProceedings{Casado:2024:SMC,
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author = "Ricardo S. Casado and Mario L. Tronco and
Emerson C. Pedrino",
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title = "A Robust Method for Camera Calibration in Noisy
Settings Based on Genetic Programming",
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booktitle = "2024 IEEE International Conference on Systems, Man,
and Cybernetics (SMC)",
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year = "2024",
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pages = "2461--2467",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Training,
Accuracy, Three-dimensional displays, Databases, Noise,
Cameras, Mathematical models, Calibration, Noise
measurement",
-
DOI = "
doi:10.1109/SMC54092.2024.10831486",
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abstract = "In this article, we introduce a novel camera
cali-bration method using genetic programming, to
calibrate cam-eras in noisy environments. Traditional
calibration methods, such as those of Tsai and Zhang,
extensively use the pinhole camera model, and are less
accurate in the presence of noise. In this work, high
precision calibration is achieved using pseudo linear
genetic programming. Instead of the pinhole camera
model, pseudo linear genetic programming generates
mathematical functions which allow for far greater
precision in the calibration process than classical
methods, regardless of the environmental conditions.
The method has several challenges such as
identification of suitable calibration functions and
creation of an extensive training database. However,
the method provides advantages in terms of better
results quality and practicality, as it eliminates the
necessity of the intrinsic camera parameters. The
results illustrate that this methodology is far
superior in comparison to the current state-of-the-art
technique, Zhang's widely used method, with a 20x
improvement in calibration accuracy.",
-
notes = "Also known as \cite{10831486}",
- }
Genetic Programming entries for
Ricardo S Casado
Mario Luiz Tronco
Emerson Carlos Pedrino
Citations