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G-senjou No Maou - The Devil On G-String Japanese Voice Pack Crack And Patch File Download
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Abstract:Over the last few years, 3D imaging of plant geometry has become of significant importance for phenotyping and plant breeding. Several sensing techniques, like 3D reconstruction from multiple images and laser scanning, are the methods of choice in different research projects. The use of RGBcameras for 3D reconstruction requires a significant amount of post-processing, whereas in this context, laser scanning needs huge investment costs. The aim of the present study is a comparison between two current 3D imaging low-cost systems and a high precision close-up laser scanner as a reference method. As low-cost systems, the David laser scanning system and the Microsoft Kinect Device were used. The 3D measuring accuracy of both low-cost sensors was estimated based on the deviations of test specimens. Parameters extracted from the volumetric shape of sugar beet taproots, the leaves of sugar beets and the shape of wheat ears were evaluated. These parameters are compared regarding accuracy and correlation to reference measurements. The evaluation scenarios were chosen with respect to recorded plant parameters in current phenotyping projects. In the present study, low-cost 3D imaging devices have been shown to be highly reliable for the demands of plant phenotyping, with the potential to be implemented in automated application procedures, while saving acquisition costs. Our study confirms that a carefully selected low-cost sensor is able to replace an expensive laser scanner in many plant phenotyping scenarios.Keywords: low-cost sensors; 3D imaging; David laser scanning system; Microsoft Kinect; parameterization; close-up scanning
In the agricultural context, laser scanning devices were commonly used for, e.g., kinematic in-field scanning of pear trees [17], 3D modeling of the canopy of tomato plants using different points of view [18] or for the estimation of biomass in different crops [13,19]. In these applications, a very coarse measuring is sufficient. To get a more detailed view on the properties of plants, highly resolved and highly accurate laser scanners are requisite for the observation of the smallest structures [20] and deformation effects, such as wilting [21]. Thus, there is a compelling demand for low-cost 3D imaging techniques for plant phenotyping platforms. However, as stated by [22], there is still a trade-off between the efficiency of image analysis and the costs for a sensor system with adequate plant trait extraction accuracies.
David 3d Laser Scanner Crack.epub
A comparison study of different 3D low-cost laser scanners needs a reliable validation measurement. For this purpose, a commercial 3D laser triangulation system was used with a line laser scanner (Perceptron Scan Works V5, Perceptron Inc., Plymouth, MI, USA), coupled to an articulated measuring arm (Romer Infinite 2.0 (1.4 m), Hexagon Metrology Services Ltd., London UK; Figure 1C; Table 1). The measuring combination has been proven regarding its applicability for scanning the geometry of tree roots [35], grapevine and wheat [9]. The system has an accuracy of 45 μm within a 2D scanning field with a depth of 110 mm and a mean width of 105 mm. This scanning field is manually moved over the surface of the object. The single scan lines were combined automatically to a complete and almost occlusion-free 3D model of an object. The point cloud was meshed using Geomagic Studio 12 (Raindrop Geomagic Inc, Morrisville, NC, USA).
A number of publications [7,8,9] have examined the combination of using laser scanners and images, however these projects only used images to colour the point cloud rather than for 3D reconstruction. Compared to the use of images, the use of laser scanners requires more equipment and software. Specific training in the operation of the scanners and a more careful planning of the capturing process is required, due to the complexity of the set up time and safety issues related to using a laser. A number of researchers have used MeshLab software to align the different scans taken from the different parts of the object of interest [7,8,9]. As with the photographic 3D reconstruction software solutions, MeshLab does not provide any statistical values to indicate the precision or accuracy of the alignment of the point clouds. Furthermore, accuracy checks are often not performed in this type of work.
Terrestrial laser scanners (TLS) capture 3D information directly, and are used for a variety of applications [10]. TLS measures distances through the use of a laser ranging system (based on time of flight using either pulsed or phase-based measurements), and combines them with the angular measurement to produce three dimensional information in the form of points, generating a point cloud over a field of view. The type of scanning technology has an impact on the accuracy, maximum range and speed of acquisition. Traditionally, pulse systems operate over greater ranges, whereas phased-based systems have increased accuracy and capture speeds.
Whilst the scanner can only capture objects and structures in direct line of site from the laser position, scans from multiple locations can increase the coverage by being combined through a registration process. Laser scanning also captures returned intensity information, which is a function of the scanner geometry to the surface and the surface properties such as reflectance and texture in relation to the laser wave-length. Other properties can be mapped onto the points such as colour (red/green/blue), thermal, and hyper spectral data, either through an on board imaging systems or the registration of images from external sources. The generated point cloud can be processed further to produce meshed surfaces, models and/or drawings.
For the local system, targets were placed around the area that both the imagery and laser scanners could identify and capture. The co-ordinates for these targets with respect to the control points were calculated using the TLS in a similar manner to how a traditional survey would be carried out using a total station. This created a network such that the positions of these targets could be used to calculate the rotation and translation between the different setups and the different methods of capture in order to put them in a common reference system, and for the photogrammetry to introduce scale information into the models.
The system utilises an active sensor, so it is completely independent from the requirement of an external light source (including the sun and any artificial light source). Furthermore, the wavelength in which the laser operates can be outside of visible spectrum in contrast to a large number of passive sensors such as a standard consumer camera. Often scanners not only capture 3D readings of their environment, but also to information on the reflected intensity of the returned signal to classify surface features based on how strongly the surface reflects the wavelength being used. The reflected intensity information is used in other fields of research such as remote sensing to classify vegetation data [30, 31]), and has the potential to reveal structure not visible to the human eye. Some applications of this method to rock art have been attempted but are largely untested [32]. It should be noted that the reverse can also be true; surfaces that may be visually different may not appear different because they may reflect the wavelength of the scanner in an identical manner. Similarly surfaces that reflect energy in parts of the visible spectrum may not reflect energy in the scanner wavelength.
Because the TLS it is an active sensor and takes discrete measurements, an additional benefit is it can penetrate vegetation more easily than passive sensors (in the gully the scanner was able to penetrate the spinifex and foliage to capture the ground and surrounding topography). However, since the measurements are discrete, the sampling resolution means that the point cloud is sparser than other methods. The point resolution is restricted to the size of the laser beam spot size and cannot be increased by simply decreasing the distance to the object.
The same drawbacks which exist for the photogrammetry solution also are relevant for the photographic reconstruction, i.e. namely the sensitivity against light. Another major drawback is that while the data look visual attractive it is not suitable to take 3D measurements due to the lack of metric information. This means that in contrast to laser scanning and photogrammetry spatial analysis starting with basic measurements of distances to further analysis such as the roughness of surfaces is not possible. In addition, while the point cloud based on photographic reconstruction may not show so many holes such as point clouds from laser scanners or photogrammetry, it does not mean that they are not there. They are more difficult to spot in the rendering because the human eye is able to interpolate where there maybe issues present.
An important aspect of rock art recording is information on the pigmentation and texture of the art. In this case photographic imagery is best suited as it can be calibrated to correct for errors in the radiomimetic capture, and is automatically applied to the geometric information of the points and surface. The laser scanner lacks this resolution and relies on intensity data. While this allows for differentiation of the rock art to the background based on spectral reflectance strength, it does not allow for the adequate pictorial documentation and capturing (Fig. 7).
When mapping the elevation contours and some of the features, dense vegetation can cause issues with extracting the ground and terrain surface (see Fig. 10 where the grass extends approximately 1 m about the ground). Because the laser scanner can penetrate such vegetation, all the points above the lowest points in region can be removed (Fig. 11), leaving only the ground points to model the terrain. It is more difficult to remove vegetation from the photogrammetry data than from the laser scanning point clouds. The removal of vegetation allows a better analysis of the rock art location in its local setting. Based on the terrain information it is possible to assess if sites are in danger during heavy weather events, e.g. flooding, because water run-off can be assessed. 2ff7e9595c
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