Reconstructing atmospheric cloud particles from multiple fisheye cameras

Kátai-Urbán, Gábor; Eichhardt, Ivan; Otte, Vilem; Megyesi, Zoltán; Bixel, Paul

doi:10.1016/j.solener.2018.06.050

Cited by 3 publications

(14 citation statements)

References 22 publications

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“…This question is briey addressed in section 5. Katai-Urban et al (2018) model the challenges of camera-based cloud height derivations and address the question of the optimal camera distance for cloud heights between 1000 m and 2000 m. They nd that for the applied approaches, using a camera resolution of 5184×3456 pixels, cloud height deviations decrease up to a camera distance of 200 m. Beyond a camera distance of 200 m, little improvements are found. Without further explanations, optimal camera distances between 2000 m and 10000 m for cloud heights between 1000 m and 5000 m are postulated in Katai-Urban et al (2016), also stating that such large distance would "show too much geometric and photometric distortion, which makes the matching of cloud pixels unfeasible" (Katai-Urban et al (2016)).…”

Section: Introductionmentioning

confidence: 99%

Determination of the optimal camera distance for cloud height measurements with two all-sky imagers

Kuhn¹,

Nouri²,

Wilbert³

et al. 2019

Solar Energy

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All-sky imager based systems can be used to measure a number of cloud properties. Congurations consisting of two all-sky imagers can be used to derive cloud heights for weather stations, aviation and nowcasting of solar irradiance. One key question for such systems is the optimal distance between the all-sky imagers. This problem has not been studied conclusively in the literature. To the best of our knowledge, no previous in-eld study of the optimal camera distance was performed. Also, comprehensive modeling is lacking.Here, we address this question with an in-eld study on 93 days using 7 camera distances between 494 m and 2562 m and one specic cloud height estimation approach. We model the ndings and draw conclusions for various congurations with dierent algorithmic methods and camera hardware.The camera distance is found to have a major impact on the accuracy of cloud height determinations.For the used 3 megapixel cameras, cloud heights up to 12000 m and the used algorithmic approaches, an optimal camera distance of approximately 1500 m is determined. Optimal camera distances can be reduced to less than 1000 m if higher camera resolutions (e.g. 6 megapixel) are deployed. A step-by-step guide to determine the optimal camera distance is provided.

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Section: Introductionmentioning

confidence: 99%

Determination of the optimal camera distance for cloud height measurements with two all-sky imagers

Kuhn¹,

Nouri²,

Wilbert³

et al. 2019

Solar Energy

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“…Potential applications are among atmospheric cloud observation or short-term solar irradiance forecasting, or autonomous driving. My related publications: [16,18,3,5].…”

Section: Main Contributionsmentioning

confidence: 99%

“…I hereby certify that the results presented in this dissertation are my own results. During my research I cooperated with my advisor Dmitry Chetverikov [10,13,14,15,2], Levente Hajder [16,17,18,19,5], Dániel Baráth [6,1,12], Zoltán Pusztai [5], József Molnár [4], Gábor Kátai-Urbán and Zoltán Megyesi [3].…”

Section: Authorshipmentioning

confidence: 99%

“…Passive sensors such as cameras and other light sensors and optical elements, like the Total Sky Imagers (TSIs) [42], are substantially more cost-effective. In this section, the effectiveness of a trinocular camera system [3] is emphasized over a stereo camera pair [89] with respect to reliability and precision of both 3D position and motion reconstructions.…”

Section: Calibration Of Multi-fisheye Systemsmentioning

confidence: 99%

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Affine Correspondences and Challenges in Multi-Sensor Systems

Eichhardt¹

2020

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Multi-device systems of cameras and various depth sensors are widely used these days in the industry. Some can also operate well in conditions where others cannot (e.g. active laser sensors compared to cameras). Various views and sensors of different modalities reveal valuable information about the environment, which is crucial for the robust operation of, e.g., detection or decision-making algorithms. The fusion of sensor data, calibration and multiple-view geometry are critical topics discussed in this dissertation.Sensors are usually placed in a common frame of reference determined by intrinsic and other parameters describing sensor alignment. During the process of calibration, all parameters can be estimated and tuned based on correspondences between sensor views. The mainstream computer vision methods solving geometric tasks use corresponding points across views as an input. Based on the correspondences, it is possible to estimate the underlying geometry of the views (i.e., epipolar geometry). As a next step, the 3D structure of the scene can also be determined. Corresponding image points are established as the extracted centers of dominant image regions. However, the shape and orientation of the regions also contain useful information as they are related to the underlying surface. Only a minority of the computer vision community tries to utilize this inter-region relation, mostly constraining their approaches to the basic pinhole camera model. The first-order approximation of a region correspondence is called an Affine Correspondence (AC). Sensor fusion is also an important part of modern systems that observe and analyze the environment. Sensors of distinct views and different modalities (e.g., depth and color images) complement each other. A low-resolution Time-of-Flight (ToF) depth camera image can be supersampled using a high-resolution color image of the same view as guidance, while depth may provide e.g. a posterior option for refocusing the color view.This thesis provides a thorough investigation of ACs in the theoretical and algorithmic sense to obtain rapid, more robust and high-quality geometric model estimation in twoor multiple-view cases. It is demonstrated that ACs are usable in general scenarios where real-world cameras and diverse geometry complicate the task. Next, the data-level sensor fusion of high-resolution color-and lower-resolution ToF depth-cameras is investigated for single pairs of frames and also for video sequences. Finally, the calibration of multi-sensor systems is discussed, that include LiDARs and cameras (wide-angle, fisheye optics, etc.). i I would like to express my gratitude to my colleagues at Institute for Computer Science and Control (SZTAKI) and at Eötvös Loránd University (ELTE) whom I had the pleasure to work with, especially, to my advisor Dmitry Chetverikov for introducing me to image processing and computer vision, guiding me throughout my Ph.D. study and research, and all his support, knowledge and critical view on research he shared with me. I would like to tha...

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“…Nakazawa et al [ 19 ] proposed an indoor positioning method based on Visible Light Communication (VLC) that utilized a fish-eye lens camera to detect additional LED lights and, therefore, improve the positioning accuracy. Gabor et al [ 20 ] proposed a sensor system based on pre-calibrated multiple fish-eye lens cameras to accurately estimate the 3D positions and motions of clouds. Berveglieri et al [ 21 ] used a fish-eye lens camera for automated mapping and measurement of tree stems, preparatorily having performed self-calibration of the camera.…”

Section: Introductionmentioning

confidence: 99%

Proposed New AV-Type Test-Bed for Accurate and Reliable Fish-Eye Lens Camera Self-Calibration

Choi

Kim

2021

Sensors

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The fish-eye lens camera has a wide field of view that makes it effective for various applications and sensor systems. However, it incurs strong geometric distortion in the image due to compressive recording of the outer part of the image. Such distortion must be interpreted accurately through a self-calibration procedure. This paper proposes a new type of test-bed (the AV-type test-bed) that can effect a balanced distribution of image points and a low level of correlation between orientation parameters. The effectiveness of the proposed test-bed in the process of camera self-calibration was verified through the analysis of experimental results from both a simulation and real datasets. In the simulation experiments, the self-calibration procedures were performed using the proposed test-bed, four different projection models, and five different datasets. For all of the cases, the Root Mean Square residuals (RMS-residuals) of the experiments were lower than one-half pixel. The real experiments, meanwhile, were carried out using two different cameras and five different datasets. These results showed high levels of calibration accuracy (i.e., lower than the minimum value of RMS-residuals: 0.39 pixels). Based on the above analyses, we were able to verify the effectiveness of the proposed AV-type test-bed in the process of camera self-calibration.

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Reconstructing atmospheric cloud particles from multiple fisheye cameras

Cited by 3 publications

References 22 publications

Determination of the optimal camera distance for cloud height measurements with two all-sky imagers

Determination of the optimal camera distance for cloud height measurements with two all-sky imagers

Affine Correspondences and Challenges in Multi-Sensor Systems

Proposed New AV-Type Test-Bed for Accurate and Reliable Fish-Eye Lens Camera Self-Calibration

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