Master Thesis - Hochschule Bonn-Rhein-Sieg

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5. Algorithms Master Thesis Björn Ostermann page 60 of 126 pointing away from the camera. The x and y value are not used in the depiction of the workspace but only in the later steps in path planning, to obtain a three dimensional representation of the environment. There are several problems in the depiction of this data for a human observer. The problem in depicting the intensity data is that in many cases the reflectional properties of nearly all objects present in the image are similar. This leads to data in which many pixels have similar greyscale values. With the distance data, the task is to convert the distance data to colour values, to be able to create an image in which the distances are visualized. Therefore for a human to be able to visually process the information acquired from the camera, the intensity image needs to be enhanced in its brightness and the distance image needs to be translated into colour. This master thesis applies the two algorithms that proved to be the best for this task in earlier parts of the project [78]. The earlier project [78] ascertained that the enhancement of the intensity image can be achieved by a scaling algorithm or histogram equalization. While the scaling algorithm is faster in its execution, the histogram equalization delivers better and more stable results, as shown in chapter 5.1.1. Since the enhancement of the intensity data is not necessary in the later process involving the robot, the duration of the algorithm does not need to be taken into account. Additionally the longer duration of the histogram equalization proved to be only a minor factor in the duration of the complete camera control thread loop (see chapter 4.3). Thus this algorithm was chosen for the project, because of its better output. It was also ascertained in the earlier project [78] that to translate the acquired distance, a colour bar is to be used that is also used in the depiction of heat images and stress images. This depiction proved to be easy to understand since people are used to it. 5.1.1 Histogram equalization – improving the grey scale values The problem in depicting the intensity data is that in many cases the reflectional properties of nearly all objects present in an image are similar. This leads to data in which many pixels have similar greyscale values. As long as all intensity values present in an image are within a short range, a simple scaling algorithm of those intensity values, described in Equation 3 and Figure 38a and b, can enhance the image sufficiently. The problem of this algorithm is the possibility of more than one cluster of intensity values. This is mainly created by the presence of highly reflective material, as shown in Figure 38c and d. In this case, the image’s intensity values are left nearly unaltered by the scaling algorithm.
5. Algorithms Master Thesis Björn Ostermann page 61 of 126 used _ range � max_brightness � min_brightness complete _ range scaling _ factor � used _ range new_ brightness � scaling _ factor � Equation 3: Scaling algorithm a) b) Number of Pixels Offset Used Range Complete Range Illumination c) d) Number of Pixels Offset Used Range Complete Range Illumination �brightness � min_brightness� Figure 38: Scaling algorithm a) to b) with a small used range and c) to d) with a large used range Number of Pixels Number of Pixels Illumination Illumination This pixel distribution of more than one information cluster is not a special, but a normal case, especially in industrial environments where lowly reflecting materials like cloth meet highly reflective materials like metals. In the case of the used workspace the mounting of the camera, visible in the camera’s image, has a high reflectivity, whereas the floor has a very low one. Thus clusters at both ends of the intensity range are always present. This problem can be avoided by using the histogram equalization, which is described in Figure 39, starting with the real distribution in Figure 39a and ending with the resulting distribution in Figure 39e. In the real distribution, there are two regions visible in which the majority of the pixels’ values are located.
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Master Thesis ”Industrial jointed
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Statutory Declaration Master Thesis
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Contents Master Thesis Björn Oster
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Master Thesis Björn Ostermann page
Page 9 and 10: 1. Introduction Master Thesis Björ
Page 11 and 12: 2. Overview on human-robot Master T
Page 19 and 20: 3. Hard- and software Master Thesis
Page 33 and 34: 4. Overall evading concept Master T
Page 59: 5. Algorithms Master Thesis Björn
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Page 101 and 102: 6. Evaluating the safety Master The
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6. Evaluating the safety Master The
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6. Evaluating the safety Master The
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7. Conclusion and Outlook Master Th
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7. Conclusion and Outlook Master Th
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8. Bibliography Master Thesis Björ
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8. Bibliography Master Thesis Björ
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9. List of Figures Master Thesis Bj
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9. List of Figures Master Thesis Bj
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Master Thesis - Hochschule Bonn-Rhein-Sieg

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