scholarly journals On plane-based camera calibration: A general algorithm, singularities, applications

2003 ◽  
Author(s):  
P.F. Sturm ◽  
S.J. Maybank
Keyword(s):  
Camera Calibration ◽  
General Algorithm

Fish-eye camera calibration using horizontal and vertical laser planes projected from a laser level

2019 ◽  
Vol 2019 (9) ◽  
pp. 374-1-374-6
Author(s):  
Yen-Chou Tai ◽  
Yu-Hsiang Chiu ◽  
Yi-Yu Hsieh ◽  
Yong-Sheng Chen ◽  
Jen-Hui Chuang
Keyword(s):  
Camera Calibration ◽  
Laser Level ◽  
Fish Eye

Detecting Asymmetric Cropping Based on Camera Calibration

2013 ◽  
Vol 34 (10) ◽  
pp. 2409-2414 ◽  
Author(s):  
Xian-zhe Meng ◽  
Shao-zhang Niu ◽  
Xiao-mei Wu ◽  
Ye-zhou Li
Keyword(s):  
Camera Calibration

scholarly journals Two-Dimensional Sampling-Recovery Algorithm of a Realization of Gaussian Processes on the Input and Output of Linear Systems

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1079
Author(s):  
Vladimir Kazakov ◽  
Mauro A. Enciso ◽  
Francisco Mendoza
Keyword(s):  
Gaussian Processes ◽  
Research Method ◽  
General Algorithm ◽  
Two Dimensional ◽  
Recovery Algorithm ◽  
Conditional Mean ◽  
Quality Of Recovery ◽  
Input And Output ◽  
Statistical Relationships

Based on the application of the conditional mean rule, a sampling-recovery algorithm is studied for a Gaussian two-dimensional process. The components of such a process are the input and output processes of an arbitrary linear system, which are characterized by their statistical relationships. Realizations are sampled in both processes, and the number and location of samples in the general case are arbitrary for each component. As a result, general expressions are found that determine the optimal structure of the recovery devices, as well as evaluate the quality of recovery of each component of the two-dimensional process. The main feature of the obtained algorithm is that the realizations of both components or one of them is recovered based on two sets of samples related to the input and output processes. This means that the recovery involves not only its own samples of the restored realization, but also the samples of the realization of another component, statistically related to the first one. This type of general algorithm is characterized by a significantly improved recovery quality, as evidenced by the results of six non-trivial examples with different versions of the algorithms. The research method used and the proposed general algorithm for the reconstruction of multidimensional Gaussian processes have not been discussed in the literature.

scholarly journals Two Independent Light Dilution Corrections for the SO2 Camera Retrieve Comparable Emission Rates at Masaya Volcano, Nicaragua

2021 ◽  
Vol 13 (5) ◽  
pp. 935
Author(s):  
Matthew Varnam ◽  
Mike Burton ◽  
Ben Esse ◽  
Giuseppe Salerno ◽  
Ryunosuke Kazahaya ◽  
...  
Keyword(s):  
Light Scattering ◽  
Optical Depth ◽  
Camera Calibration ◽  
Wind Direction ◽  
Emission Rate ◽  
Emission Rates ◽  
Depth Images ◽  
Masaya Volcano ◽  
Rapid Changes ◽  
Volcanic Emission

SO2 cameras are able to measure rapid changes in volcanic emission rate but require accurate calibrations and corrections to convert optical depth images into slant column densities. We conducted a test at Masaya volcano of two SO2 camera calibration approaches, calibration cells and co-located spectrometer, and corrected both calibrations for light dilution, a process caused by light scattering between the plume and camera. We demonstrate an advancement on the image-based correction that allows the retrieval of the scattering efficiency across a 2D area of an SO2 camera image. When appropriately corrected for the dilution, we show that our two calibration approaches produce final calculated emission rates that agree with simultaneously measured traverse flux data and each other but highlight that the observed distribution of gas within the image is different. We demonstrate that traverses and SO2 camera techniques, when used together, generate better plume speed estimates for traverses and improved knowledge of wind direction for the camera, producing more reliable emission rates. We suggest combining traverses and the SO2 camera should be adopted where possible.

scholarly journals Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1091
Author(s):  
Izaak Van Crombrugge ◽  
Rudi Penne ◽  
Steve Vanlanduit
Keyword(s):  
Camera Calibration ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Extrinsic Calibration ◽  
Practical Procedure ◽  
Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

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