Master Thesis - Fachbereich Informatik

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3.2. CAMERA SETUP 37 Figure 3.4: Color information of transparent tubes in HSV color space. Rows include from top to bottom: Color input image, hue channel, saturation channel, value (brightness) channel, and in the bottom row the computed gray scale image using Eq. 3.2. Although all images are taken from the same sequence, tubes and background can have very different color. The position of the scan lines corresponds to the same real world location. It can be seen that both edges are ramp edges (see Section 2.3.1). The slope of the edge profile, however, is larger for the gray level camera, i.e. the edge can be located more precise. This is an important advantage with respect to accurate measuring. Therefore, if color has no other significant advantage over gray scale images, a gray scale camera should be preferred in this application. One can think of using color information to segment the transparent tubes from the background, since they appear yellowish or reddish while the conveyor belt should be white. For black tubes, color has obviously no significant benefit, hence, it is adequate to concentrate on the transparent tubes in this context. The idea is to use color as a measure to distinguish between transparent tubes and the background, since here the gray scale contrast is lower compared to black tubes. However, as can be seen in Figure 3.4, in real images of transparent heat shrink tubes on a conveyor, the color of the conveyor belt can appear quite different. The test images have been taken from a sequence of tubes on a moving conveyor. The images have been illuminated via a back light setup, which will be introduced in Section 3.3. It can be observed that some regions of the same conveyor belt look yellowish, while others appear blueish in the image (see left column in Figure 3.4). There are several color models beside the R-G-B model. Humans intuitively perceive and describe color experiences in terms of hue (chromatic color), saturation (absence of white) and brightness [27]. A corresponding color model is the H-S-V model, where H stands for hue, S for saturation, and V for (brightness) value respectively. More detailed information on color models can be found for example in [35]. In the hue domain, a yellowish transparent tube differs from a blueish background significantly (left column in Figure 3.4). If the background is also yellowish, the difference
38 CHAPTER 3. HARDWARE CONFIGURATION between tube and background decreases (center column). Strong discontinuities in background color (as in the right column) could be wrongly classified as a tube. The saturation domain is also a quite unstable feature. If the background contains a lot of white, it is more desaturated than the object (like in the center column) and yields a quite strong contrast. The example in the left column, however, shows that the difference in saturation does not always have to be that clear. The brightness channel (fourth row) is very close to the computed gray level image using Equation 3.2 (bottom row). Thus, it equals approximately what a gray level camera would see. In this experiment it has been shown color can be a very unstable feature. With respect to precise length measurements it definitely turns out that there are a lot of artifacts at the tube edges in the H and S color channel respectively. In the brightness channel, edges appear much more sharp. The little artifacts in this channel are due to the camera noise, motion blur effects, or not perfectly adjusted camera focus. Since the brightness channel is closely related to the gray value image converted from R-G-B values using Equation 3.2 one could replace the brightness channel by this image. As can be seen in Figure 3.4, the bottom row yields even a better contrast between object and background. With the observations made before, one can conclude that a gray level camera is best suited in this particular application. It yields the best edge quality, which is important for precise measurements, and both black and transparent tubes are imaged with a sufficient contrast between object and background making it possible to locate a tube in the image without using color information. Hence, the Marlin F-046B camera has been selected for this prototype. It yields the best compromise in image quality, resolution, and speed. 3.2.2. Camera Positioning The camera is placed at fix position and viewing angle above the measuring area of the conveyor (see Figure 3.1(b)). In a calibration step, it is adjusted to position the image plane parallel to the surface of the conveyor with the optical center above the center of the measuring area, thus, minimizing the perspective effects at this area. The exact calibration procedure will be explained in Section 4.3.2. The moving direction of the conveyor (and therefore of the tube segments) is horizontal in the image. The distance between camera and conveyor depends on the optical system, i.e. the lens, that is used and on the tube size to be inspected. In Section 4.2.2, the basic assumptions and constraints regarding the image content with respect to the image processing are presented. This includes the assumption that only one tube can be seen totally in an image at one time. Correspondingly, the cameras field of view has to be adapted to satisfy this criterion for different tube lengths. Placing the camera above the conveyor has the additional advantage of not extending the dimensions of the production line, since space in a production hall is limited and therefore expensive. 3.2.3. Lens Selection Parameters such as object size, sensor size of the camera, camera distance, and accuracy requirements determine the right optical system (objective) for a particular application. In the following, the term lens will be used synonymously to the term optical system or objective, although an objective is actually more than just a single lens (iris, case, mount,
Page 1 and 2: Fachbereich Informatik Department o
Page 4: Acknowledgments First of all, I wou
Page 8 and 9: Contents Acknowledgments iii Abstra
Page 10 and 11: Contents ix 5.3.7. TubeLength .....
Page 12 and 13: List of Tables 1.1. Rangeoftubetype
Page 14 and 15: List of Figures 2.1. AccuracyandPre
Page 16 and 17: 1. Introduction Heat shrinkable tub
Page 18 and 19: 1.2. PROBLEM STATEMENT 3 hazards, r
Page 20 and 21: 1.4. RELATED WORK 5 Length [mm] Tol
Page 22 and 23: 1.5. THESIS OUTLINE 7 The vision pa
Page 24 and 25: 2. Technical Background 2.1. Visual
Page 26 and 27: 2.1. VISUAL MEASUREMENTS 11 (a) Fig
Page 28 and 29: 2.1. VISUAL MEASUREMENTS 13 Figure
Page 30 and 31: 2.1. VISUAL MEASUREMENTS 15 � xd
Page 32 and 33: 2.2. ILLUMINATION 17 tubes alternat
Page 34 and 35: 2.2. ILLUMINATION 19 effect of comp
Page 36 and 37: 2.3. EDGE DETECTION 21 i 2 i 1 0 (a
Page 38 and 39: 2.3. EDGE DETECTION 23 N × 1 kerne
Page 40 and 41: 2.3. EDGE DETECTION 25 These operat
Page 42 and 43: 2.3. EDGE DETECTION 27 (a) 0 ◦ (b
Page 44 and 45: 2.4. TEMPLATE MATCHING 29 accuracy
Page 46 and 47: 3. Hardware Configuration This chap
Page 48 and 49: 3.2. CAMERA SETUP 33 3.2. Camera se
Page 50 and 51: 3.2. CAMERA SETUP 35 eachcolorchann
Page 54 and 55: 3.2. CAMERA SETUP 39 adjusting scre
Page 56 and 57: 3.2. CAMERA SETUP 41 further distan
Page 58 and 59: 3.3. ILLUMINATION 43 f V 12mm 129mm
Page 60 and 61: 3.3. ILLUMINATION 45 (a) (b) (c) Fi
Page 62 and 63: 3.3. ILLUMINATION 47 (a) (b) Figure
Page 64 and 65: 3.4. BLOW OUT MECHANISM 49 A light
Page 66 and 67: 4. Length Measurement Approach Whil
Page 68 and 69: 4.2. MODEL KNOWLEDGE AND ASSUMPTION
Page 74 and 75: 4.3. CAMERA CALIBRATION 59 4.2.7. B
Page 76 and 77: 4.3. CAMERA CALIBRATION 61 Figure 4
Page 78 and 79: 4.3. CAMERA CALIBRATION 63 is compu
Page 80 and 81: 4.4. TUBE LOCALIZATION 65 (a) (b) F
Page 82 and 83: 4.4. TUBE LOCALIZATION 67 � 1 Psm
Page 84 and 85: 4.4. TUBE LOCALIZATION 69 Global Th
Page 86 and 87: 4.4. TUBE LOCALIZATION 71 but also
Page 88 and 89: 4.4. TUBE LOCALIZATION 73 Global Re
Page 90 and 91: 4.5. MEASURING POINT DETECTION 75 f
Page 92 and 93: 4.5. MEASURING POINT DETECTION 77 T
Page 94 and 95: 4.5. MEASURING POINT DETECTION 79 -
Page 96 and 97: 4.5. MEASURING POINT DETECTION 81 0
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4.5. MEASURING POINT DETECTION 87 w
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4.6. MEASURING 89 side of the tube,
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4.6. MEASURING 91 fcor(x) =−(c1x
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4.7. TEACH-IN 93 inthemeasuringplan
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4.7. TEACH-IN 95 The pair of a pixe
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5. Results and Evaluation 5.1. Expe
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5.1. EXPERIMENTAL DESIGN 99 between
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5.1. EXPERIMENTAL DESIGN 101 where
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5.1. EXPERIMENTAL DESIGN 103 Ground
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5.2. TEST SCENARIOS 105 enough, the
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5.3. EXPERIMENTAL RESULTS 107 The t
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5.3. EXPERIMENTAL RESULTS 109 Detec
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5.3. EXPERIMENTAL RESULTS 111 Lengt
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5.3. EXPERIMENTAL RESULTS 113 GTD [
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5.3. EXPERIMENTAL RESULTS 115 gray
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5.3. EXPERIMENTAL RESULTS 119 (a) (
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5.3. EXPERIMENTAL RESULTS 121 (a) (
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5.3. EXPERIMENTAL RESULTS 125 Total
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5.3. EXPERIMENTAL RESULTS 127 Color
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5.3. EXPERIMENTAL RESULTS 129 Task
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5.4. DISCUSSION AND FUTURE WORK 131
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6. Conclusion In this thesis a func
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Appendix 135
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138 APPENDIX A. PROFILE ANALYSIS IM
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140 APPENDIX A. PROFILE ANALYSIS IM
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142 APPENDIX B. HARDWARE COMPONENTS
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144 APPENDIX B. HARDWARE COMPONENTS
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146 Bibliography [18] C. Demant, B.
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148 Bibliography [51] K.K. Pingle.
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Master Thesis - Fachbereich Informatik

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