Master Thesis - Fachbereich Informatik

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4.4. TUBE LOCALIZATION 71 but also over the whole image width or locally within a single image. The first case is uncritical as long as there is a sufficient contrast between a tube and the background. The later case, i.e. local variations in background brightness, can lead to failures of the global threshold. Figure 4.15(a) shows one characteristic situation which occurs quite often with transparent tubes. The background intensity on the left is much darker compared to the right. The global threshold fails, since the much brighter background regions on the right increase the global mean. Thus, the local median of the most left segment falls below the threshold and is therefore classified as foreground. Due to this misclassification no measuring will be performed on this frame, although it would be possible. A region based threshold can overcome this problem. The idea is to compute the classification threshold not globally, but on regional image brightness. While the local median is computed for each segment, a good classification threshold must consider at least one transition between background and foreground. Following the assumptions made in Section 4.2, two tubes can not be completely in the image at one time. Furthermore, the number of connected background regions in the image can not exceed two. If there are two connected background regions, one has to lie in the left half of the image while the other falls in the right half. Thus, one can define two regions, left and right of the image center respectively, and compute the mean for each region as analogue to the global mean. Inthefollowing,themeanoftheleftandrightsideofthe(smoothed)profileare denoted as Pleft and Pright respectively. If there is only one background region (states empty, entering, leaving, entering + leaving), splitting the image at the center has no negative effect. The left and right mean is computed either over a tube and background region, or over background only. At the very special case that the image width is exactly twice a tube’s length and the tube enters (or leaves) the scene with the right (or left) boundary exactly on the image center, the regional threshold is computed only over the tube and the classification may be either foreground or background. However, in both cases this situation can be detected as a state where a measurement is not possible and is therefore a sufficient solution. The region based classification of the segments can now be expressed as: � TUBE , median(s(i)) ≤ τregion C3(s) = BG , otherwise where τregion is defined as follows: ⎧ ⎨ Pleft ,s(i) falls into left region only τregion = Pright ⎩ max(Pleft, Pright) ,s(i) falls into right region only ,s(i) falls into both regions (4.12) (4.13) In Figure 4.15(b) one can see the difference between the global and the regional classification threshold. The regional threshold of the left half is much lower compared to the global threshold. On the other hand, since the second segment belonging to the tube intersects the center, the maximum of both regional thresholds is taken into account which lies significantly above the global threshold. Finally, all segments are classified correctly. With this threshold, the classification is less sensitive to darker background regions. Thetwomethodshavebeencomparedinthefollowingexperiment: A sequence of transparent tubes (50mm length, 8mm diameter) has been captured including 467 frames that have been manually classified as measurable, i.e. a tube is
72 CHAPTER 4. LENGTH MEASUREMENT APPROACH 250 200 150 100 50 (a) Smoothed graylevel profile Regional mean Global mean peak candidates filtered peaks local median 0 0 20 40 60 80 100 120 140 160 180 200 (b) Figure 4.15: (a) The background intensity at the left is much darker than at the right. The global mean as threshold can not compensate for such local variations as can be seen in (b). In this case, the left background region is wrongly classified as foreground, since the global threshold is larger than the local median of the corresponding segment. A region based threshold that considers the left and right image side independently can overcome this problem (see text).
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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
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 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 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 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
Page 132 and 133: 5.3. EXPERIMENTAL RESULTS 117 Lengt
Page 134 and 135: 5.3. EXPERIMENTAL RESULTS 119 (a) (
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5.3. EXPERIMENTAL RESULTS 121 (a) (
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5.3. EXPERIMENTAL RESULTS 123 Lengt
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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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