Figure 2.Pyranometer circuit diagram.2.1. Radiation diffuser and pyranometer housingAs a protective element for the sensor and at the same time a solar radiation diffuser (see Figure 1), a 5 mm thick Teflon? cover has been designed and manufactured. Several thicknesses were tested for this piece (namely, 2, 3, 4 and 5 mm), although the one providing the best cosine response, with no loss incident radiation, was the 5-mm one. This piece is located just above the photodiode (see Figure 4.a). To a large extent this diffuser allows elimination of the cosine error [2,20,21]. Teflon has been used because it is a good diffuser and is also resistant to the elements and ultra-violet (UV) radiation [22,23], given its capability to diffuse transmitting lights nearly perfectly.
Moreover, the optical properties of PTFE (Teflon?) remain constant over a wide range of wavelengths, from UV up to near infrared. Within this region, the relation of its regular transmittance to diffuse transmittance is negligibly small, so light transmitted through a diffuser radiates like Lambert’s cosine law. Initially, a completely flat diffuser was designe
Camera calibration is a major issue in computer vision since it is related to many vision problems such as neurovision, remote sensing, photogrammetry, visual odometry, medical imaging, and shape from motion/silhouette/shading/stereo. Metric information within images can be supplied only by the calibrated cameras [1, 2]. The 3D computer vision problem is mathematically determined only if the optical parameters (i.e.
, parameters of intrinsic orientation) and geometrical parameters (i.e., parameters of extrinsic orientation) of the camera system are precisely known. The camera calibration methods can be classified according to the determination methods of optical and geometrical parameters of the imaging system [1]. The number of camera calibration parameters (i.e., rotation angles, translations, coordinates of principal points, scale factors, skewness between image axes, radial lens distortion coefficients, affine-image parameters, and lens-decentering parameters) depends on the mathematical model of the camera used [2].In the literature, many camera calibration methods have been introduced. A self-calibration method to estimate the optic and geometric parameters of a camera from vertical line segments of the same height is examined in [3].
Extrinsic calibration of multiple cameras is very important for 3D metric information extraction from images. Drug_discovery Computation of relative orientation parameters between multiple photo/video cameras is still one of the active research fields in the computational vision [4, 5]. Using geometric constraints within the images, such as lines and angles, enables performing 3D scene reconstruction tasks with fewer images [6].Plane-based camera calibration is an active area in computational vision because of its flexibility [7].