Master Thesis - Fachbereich Informatik

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2.2. ILLUMINATION 19 effect of completely smooth objects appearing dark in the image, since the light rays are not reflected toward the camera, while unevenness leads to brighter image intensities. Due to this effect, directional lighting is also denoted as dark field illumination in the literature [18] (See Figure 2.5(c)). Directional lighting mostly qualifies for surface inspection tasks that consider the surface structure revealing irregularities or bumpiness. Polarized light In combination with a polarizing filter in front of the camera lens, incident lighting with polarized light can be used to avoid specular reflections. Such reflections preserve the polarization of a light ray, thus, with the right choice of filter, only scattered light rays can pass the filter and reach the camera. A maximal filter effect can be reached if the polarization of the light source and the filter are perpendicular to each other. Polarized light is often combined with a ring light setup to avoid both shadows and reflections. Structured lighting Structured lighting is used to obtain three-dimensional information of objects. A certain pattern of light (e.g. crisp lines, grids or cycles [18]) is projected onto the object. Based on deflections of this known pattern in the image, one can infer the object’s three-dimensional characteristics. For example, in [58], a 3D scanner using structured lighting is presented that integrates a real-time range scanning pipeline. In machine vision applications, structured lighting can be used for dimensional measuring tasks were the contrast between object and background is poor. Axial illumination In this type of illumination setup (see Figure 2.5(d)), also denoted as coaxial illumination in the literature, the light rays are directed to run along the optical axis of the camera [18]. This is achieved using an angled beam splitter or half-silvered mirror in combination with a diffuse light source. The beam of light has usually the same size as the camera’s field of view. The main application of axial illumination systems is to illuminate highly reflective, shiny materials such as plastic, metal or other specular materials, or for example to inspect the inside of bore holes. Axial illumination is typically used for inspection of small objects such as electrical connectors or coins. One potential problem with most incident lighting methods are shadows. Although the shadow contrast can be lowered using several light sources at different positions around the object (e.g. ring lights) or axial illumination setups, objects with sharp corners or concavities might have regions that can not be illuminated and therefore especially regions close to the object’s boundaries appear darker in the image. Thus, dark objects on a bright background may appear enlarged [16]. The effect of shadows is less significant for bright objects on dark background. In applications that require totally shadow-free conditions for highly accurate measurements of object contours, another lighting setup called back lighting can be used, as introduced in the following. 2.2.3. Back lighting The setup were the object is placed between the light source and the camera, compared to incident lighting, is denoted as back light illumination. In this arrangement, the light enters the camera directly leading to bright intensity values at non-occluded regions. The object, on the other hand, casts a shadow on the image plane, thus, leading to darker intensity values. Non-translucent materials result in a very strong, shadow-free contrast,
20 CHAPTER 2. TECHNICAL BACKGROUND which makes back lighting interesting for dimensional measuring tasks. Furthermore, surface structures or textures can be suppressed. If the only light source is placed below the object, there will be no shadows around. Back lighting can also be used for localization of wholes and cracks, or for measuring translucency. In combination with polarized light, back lighting can also be adapted to enhance the contrast of transparent materials which are difficult to detect in an image at other lighting setups. In a typical scenario, polarized light entering the camera directly is filtered out by an adequate polarization filter in front of the camera lens, while the polarization of the light is changed when passing through the object. Thus, in opposition to back lighting without polarization, background regions appear dark in the image while (translucent) objects result in brighter intensities. Figure 3.9 in Section 3.3 visualizes the effect of back lighting in combination with a polarization filter. 2.3. Edge Detection An edge can be defined as particularly sharp change in (image) brightness [24], or more mathematically speaking a strong discontinuity of the spatial image gray level function [36]. Beside edges due to object boundaries, there are much more causes for edges in images such as shadows, reflectance, texture or depth. Thus, simply extracting edges in images is no general indicator for object boundaries. To yield a semantical meaning, edge information can be combined with other features including shape, color, texture, or motion. Model knowledge about expected properties can be useful to group these low-level features to objects. In real images there are many changes in brightness (or color), but with respect to a certain application it may be of interest to extract only the strongest edges or edges of a certain orientation. Thus, information such as edge strength and orientation have to be taken into account to link the results of the filter response. Furthermore, in real images there is also a certain amount of noise in the data which has to be handled carefully. 2.3.1. Edge Models Edges can be modeled according to their intensity profiles [65]. considered in this thesis are shown in Figure 2.6. The two edge models The ideal step edge is the basis for most theoretic approaches. It can be defined as: � i1 Eideal(x) = i2 ,x
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
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Page 32 and 33: 2.2. ILLUMINATION 17 tubes alternat
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Page 40 and 41: 2.3. EDGE DETECTION 25 These operat
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Page 44 and 45: 2.4. TEMPLATE MATCHING 29 accuracy
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Page 48 and 49: 3.2. CAMERA SETUP 33 3.2. Camera se
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Page 52 and 53: 3.2. CAMERA SETUP 37 Figure 3.4: Co
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Page 60 and 61: 3.3. ILLUMINATION 45 (a) (b) (c) Fi
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Page 68 and 69: 4.2. MODEL KNOWLEDGE AND ASSUMPTION
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Page 76 and 77: 4.3. CAMERA CALIBRATION 61 Figure 4
Page 78 and 79: 4.3. CAMERA CALIBRATION 63 is compu
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Page 82 and 83: 4.4. TUBE LOCALIZATION 67 � 1 Psm
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4.4. TUBE LOCALIZATION 69 Global Th
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4.4. TUBE LOCALIZATION 71 but also
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4.4. TUBE LOCALIZATION 73 Global Re
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4.5. MEASURING POINT DETECTION 75 f
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4.5. MEASURING POINT DETECTION 77 T
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4.5. MEASURING POINT DETECTION 79 -
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4.5. MEASURING POINT DETECTION 81 0
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4.5. MEASURING POINT DETECTION 83 o
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4.5. MEASURING POINT DETECTION 85 (
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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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