Master Thesis - Fachbereich Informatik

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4.5. MEASURING POINT DETECTION 77 The results of the transparent tubes are crucial for the selection of an appropriate edge detection approach used in this application, since due to the strong contrast the detection of the black tube boundaries is uncritical with all tested methods. For both tube types the edge detection results differ in detected orientation, edge elongation (i.e. how precise an edge can be localized), or edge representation (signed/unsigned values, floating point/binary, etc.). Canny Edge Detector The Canny edge detector results in a skeletonized one pixel wide response that precisely describes edges of arbitrary orientation. In this application the main drawback of Canny’s approach is the importance of the threshold choice. As can be seen in Table 4.2, different parameter sets yield very different results. If the upper hysteresis threshold used as starting point for edge linking is low (e.g. 100) combined with a lower second threshold (e.g. 50), too many background edges are detected as well. A larger upper threshold (e.g. > 200) reduces the number of detected edge pixels, but also eliminates parts of the tube edge. It is possible that it breaks up into parts. If the distance between upper and lower threshold is large, it is likely that background and tube edges are merged. In any case a threshold set working fine with one image can lead to very poor results in another. The result of the Canny edge detector is a binary image where non-edge pixels have a value of zero and edge pixels a value of one (or 255 in 8bit gray level images). Binary contour algorithms can be applied to analyze chains of connected edge pixels. As can be seen in the test images, depending on how many edge pixels survived the thresholding, such analysis can be very complex and time-consuming. Gaps within edges belonging to the tube boundary make this search even more complicated. Sobel The Sobel operator approximates a Gaussian smoothing combined with differentiation. It can be applied with respect to x- andy- direction. Accordingly to the filter direction, vertical or horizontal edges are enhanced. Since the tube boundaries have a vertical orientation, the SOBELX operator is an adequate choice in this application. Edges are located at local extrema, i.e. local minima at bright-dark edges and local maxima for dark-bright edges with respect to the gradient direction. A drawback is that also the background pattern is dominantly vertical oriented, thus, background edges are also detected. The intensity of an edge is related to the image contrast. Assuming a certain contrast between tubes and background, a large amount of background clutter could be removed by thresholding leaving only tube edges and edges due to high-contrast dirt particles. However, this would lead to a similar approach like the Canny edge detector with the drawbacks stated before. Laplace The implementation used to test the Laplacian calculates the second-order derivative in x- andy-direction using the Sobel operator and sums the results. The output is an image of signed floating point values. Edges are located at the zero crossings between strong peaks. The Laplacian is an anisotropic operator, thus, edges off all orientations are detected equally. One drawback of this method is the sensitivity to noise. In the resulting response there are many zero crossings. Compared to first-order derivatives, the edge criterion is more complex. A pixel is an edge pixel if the closest neighbor in the direction of the gradient is a local maximum while the opposite neighbor is a local minimum and both
78 CHAPTER 4. LENGTH MEASUREMENT APPROACH neighbors must meet a certain threshold. However, the zero crossing can be computed with subpixel accuracy. Steerable Filters The idea with filters that are steerable for example in scale and orientation is to design a filter that performs best for a particular edge detection task (See Section 2.3.3). In this application the goal is to find a filter that extracts the tube edges with maximum precision, while background edges and dirt are suppressed. The steerable filter approach allows for testing a large range of different edge detection kernels. Experiments with systematically varied parameter sets of first-derivative Gaussian filters following the approach of Freeman and Adelson [25] are applied to the test images. Some of the results are visualized in Figure 4.2. As can be seen, the background clutter can not be eliminated even with larger kernel sizes while the tube edges get blurred. No parameter setting for a Gaussian derivative kernel has been found that performs significantly better as a tube edge detector than the computational less expensive Sobel operator. All tested methods beside the Canny edge detector can be seen more as edge enhancer than as real edge detectors. This means, the results do not fulfill the second and third criterion for good edge detection (See Section 2.3.1). Further processing of the edge responses such as nonmaximum suppression is necessary. An alternative is a template based edge localization step which is introduced in the next section. 4.5.2. Template Based Edge Localization It is important to state that even precisely detected edges (including Canny’s approach) still have no semantical meaning. In all tested methods there have been false positives, i.e. edges belonging to the background, dirt, or noise. Hence, model knowledge has to be applied to the detected edges to ensure whether an edge really corresponds to a tube’s boundary or not. In this application, the highly constrained conditions reduce the number of expected situations to a small, well defined minimum. The edges belonging to the tube boundaries of interest are always approximately vertical. Due to perspective the tube boundary appears straight or slightly curved in a convex fashion under back light, depending on the position of the tube with respect to the optical ray of the camera. The more the tube boundary is displaced from the camera center the larger is the curvature. At this stage it is of interest to locate a tube’s boundaries within the two local ROIs (left and right respectively). Strong changes in image intensity in x direction (vertical edges) have been enhanced using the SOBELX operator. The goal is not only to find the strongest peaks in the edge image, but also the strongest connected ridge along such peaks that most likely corresponds to the tube boundary. This task can be performed by template matching (See Section 2.4). If the feature to be detected can be modeled by a template, the response of the crosscorrelation with this template computes a match probability within a given search region. The idea is to design a template that models the response of the edge enhancer and correlate this template with the local ROI. The position where the correlation has its maximum provides close information on the tube boundary location. Therefore, it is
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Fachbereich Informatik Department o
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Acknowledgments First of all, I wou
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Contents Acknowledgments iii Abstra
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Contents ix 5.3.7. TubeLength .....
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List of Tables 1.1. Rangeoftubetype
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List of Figures 2.1. AccuracyandPre
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1. Introduction Heat shrinkable tub
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1.2. PROBLEM STATEMENT 3 hazards, r
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1.4. RELATED WORK 5 Length [mm] Tol
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1.5. THESIS OUTLINE 7 The vision pa
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2. Technical Background 2.1. Visual
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2.1. VISUAL MEASUREMENTS 11 (a) Fig
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2.1. VISUAL MEASUREMENTS 13 Figure
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2.1. VISUAL MEASUREMENTS 15 � xd
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2.2. ILLUMINATION 17 tubes alternat
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2.2. ILLUMINATION 19 effect of comp
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2.3. EDGE DETECTION 21 i 2 i 1 0 (a
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2.3. EDGE DETECTION 23 N × 1 kerne
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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 52 and 53: 3.2. CAMERA SETUP 37 Figure 3.4: Co
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
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Page 104 and 105: 4.6. MEASURING 89 side of the tube,
Page 106 and 107: 4.6. MEASURING 91 fcor(x) =−(c1x
Page 108 and 109: 4.7. TEACH-IN 93 inthemeasuringplan
Page 110 and 111: 4.7. TEACH-IN 95 The pair of a pixe
Page 112 and 113: 5. Results and Evaluation 5.1. Expe
Page 114 and 115: 5.1. EXPERIMENTAL DESIGN 99 between
Page 116 and 117: 5.1. EXPERIMENTAL DESIGN 101 where
Page 118 and 119: 5.1. EXPERIMENTAL DESIGN 103 Ground
Page 120 and 121: 5.2. TEST SCENARIOS 105 enough, the
Page 122 and 123: 5.3. EXPERIMENTAL RESULTS 107 The t
Page 124 and 125: 5.3. EXPERIMENTAL RESULTS 109 Detec
Page 126 and 127: 5.3. EXPERIMENTAL RESULTS 111 Lengt
Page 128 and 129: 5.3. EXPERIMENTAL RESULTS 113 GTD [
Page 130 and 131: 5.3. EXPERIMENTAL RESULTS 115 gray
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Page 140 and 141: 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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