Master Thesis - Fachbereich Informatik

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5.3. EXPERIMENTAL RESULTS 107 The tolerances for these lengths differ, i.e. the 30mm tubes are allowed to deviate only up to 0.5mm around the target length while 70mm tubes have a larger tolerance of 1mm. The measuring precision can be directly linked to these tolerances. Accordingly the system must measure smaller tubes with a higher precision then larger ones. In this scenario, the accuracy and precision is evaluated based on the mean and standard deviation of a sequence of tubes measured online that approximately meet the given target length. Corresponding ground truth data is available. Performance Finally, it is of interest to determine the performance of the system in terms of the average per frame processing time ΩTIME. It is investigated how the total processing time is distributed over the different stages of the inspection including radial distortion compensation, profile analysis, edge detection and template matching, as well as the total length computation and tracking. 5.3. Experimental Results In this section the experimental results of the different scenarios are presented and discussed. Further discussion as well as an outlook on future work is given in Section 5.4. 5.3.1. Noise The influence of noise on the measuring accuracy is tested on synthetic sequences. Rectangles of 200 pixels width are placed on a uniform background with a contrast of 70 gray levels between the object and the brighter background. The image size is 780 × 160, and the sequence is analyzed like a real sequence with two differences. First, the perspective correction function is disabled, since the synthetic ‘tube’ is not influenced by perspective, i.e. the width of the rectangle is constant independent of the image position. Furthermore, the dynamic selection of template curvatures based on the image position does not work as well in this scenario, since the model knowledge assumptions do not hold. Thus, in this experiment all templates are tested at each position (computation time is not critical here). Gaussian noise of standard deviation σN has been added to the ideal images, with σN ∈{5, 10, 25}. Sample images of each noise level are shown in Figure 5.4(a)-(d). The measuring results are evaluated using the root-mean-square-error between the ground truth length of 200pixels and the result of the single measurements. The results show that in the ideal (noise free) case, the pixel length is always measured correctly. Under the presence of noise, the measured length varies at subpixel level. Figure 5.4(e) shows how the measurements differ in accuracy and precision under the presence of noise. The maximum deviation from the target length occurs at the largest standard deviation (σN = 25). The RMSE results can be found in Figure 5.4(f). For sequences with only a little amount of noise (σN = 5) the RMSE is acceptable low with 0.122. If one pixel represents 0.12mm in the measuring plane, the real world error is about 1/100mm. Even under strong noise (σN = 25), which is far beyond the noise level of real images, the measuring error is 0.252pixels or 0.03mm in the example. This is still significantly below the human measuring variance.
108 CHAPTER 5. RESULTS AND EVALUATION 1 0.8 0.6 0.4 0.2 (a) σN =0 (b) σN =5 (c) σN =10 (d) σN =25 std=0 std=5 std=10 std=25 0 199 199.2 199.4 199.6 199.8 200 Length [pixel] 200.2 200.4 200.6 200.8 201 (e) (f) σN RMSE 0 0 5 0.122 10 0.158 25 0.252 Figure 5.4: Accuracy evaluation of length measurements at synthetic sequences under the influence of noise. (a)-(d) Rectangles of known size (length = 200 pixels) simulate a tube on a uniform background without perspective effects. Gaussian noise of different standard deviation σN ∈{5, 10, 25} has been added to the ideal images. (e) Gaussian distribution of the measurements. (f) Root mean square error (RMSE) for each noise level.
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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
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2.3. EDGE DETECTION 27 (a) 0 ◦ (b
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2.4. TEMPLATE MATCHING 29 accuracy
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3. Hardware Configuration This chap
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3.2. CAMERA SETUP 33 3.2. Camera se
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3.2. CAMERA SETUP 35 eachcolorchann
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3.2. CAMERA SETUP 37 Figure 3.4: Co
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3.2. CAMERA SETUP 39 adjusting scre
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3.2. CAMERA SETUP 41 further distan
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3.3. ILLUMINATION 43 f V 12mm 129mm
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3.3. ILLUMINATION 45 (a) (b) (c) Fi
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3.3. ILLUMINATION 47 (a) (b) Figure
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3.4. BLOW OUT MECHANISM 49 A light
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4. Length Measurement Approach Whil
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4.2. MODEL KNOWLEDGE AND ASSUMPTION
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4.2. MODEL KNOWLEDGE AND ASSUMPTION
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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
Page 98 and 99: 4.5. MEASURING POINT DETECTION 83 o
Page 100 and 101: 4.5. MEASURING POINT DETECTION 85 (
Page 102 and 103: 4.5. MEASURING POINT DETECTION 87 w
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 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
Page 134 and 135: 5.3. EXPERIMENTAL RESULTS 119 (a) (
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Page 140 and 141: 5.3. EXPERIMENTAL RESULTS 125 Total
Page 142 and 143: 5.3. EXPERIMENTAL RESULTS 127 Color
Page 144 and 145: 5.3. EXPERIMENTAL RESULTS 129 Task
Page 146 and 147: 5.4. DISCUSSION AND FUTURE WORK 131
Page 148 and 149: 6. Conclusion In this thesis a func
Page 150: Appendix 135
Page 153 and 154: 138 APPENDIX A. PROFILE ANALYSIS IM
Page 155 and 156: 140 APPENDIX A. PROFILE ANALYSIS IM
Page 157 and 158: 142 APPENDIX B. HARDWARE COMPONENTS
Page 159 and 160: 144 APPENDIX B. HARDWARE COMPONENTS
Page 161 and 162: 146 Bibliography [18] C. Demant, B.
Page 163 and 164: 148 Bibliography [51] K.K. Pingle.
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Master Thesis - Fachbereich Informatik

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