Master Thesis - Fachbereich Informatik

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4.4. TUBE LOCALIZATION 69 Global Threshold The classification into BG and TUBE is based on the general assumption that the mean intensity of objects is darker than the background. In more detail, taking the mean value of the smoothed profile Psmooth as a global reference and calculating the local mean value for each segment s(i), the classification C can be expressed as: � TUBE , mean(s(i)) ≤ Psmooth C1(s) = (4.10) BG , otherwise In image segmentation the mean value is widely used as an initial guess of a threshold separating two classes of data distinguishable via the gray level [48, 2]. There are many more sophisticated approaches for threshold selection including histogram shape analysis [57, 63, 26], entropy [54], fuzzy sets [20, 14] or cluster-based approaches [55, 46]. The different techniques are summarized and compared in several surveys [59, 47, 60]. However, in this application the threshold is used for classification and it is not intended for calculation of a binary image that segments the tubes from the background. Since processing time is strictly limited and critical in this application, it is essential to save computation time if possible. As introduced before, the actual segmentation is based on strong vertical edges in the profile, but does not include any semantic meaning of the segments. In the classification step, the mean turned out to be a reliable and fast choice to distinguish between foreground and background segments both for black and transparent tubes if there is a uniform and sufficient contrast between tubes and the background over the whole image. In this case there is no need for another threshold than the mean - saving additional operations. Insteadofcomparingtheglobalmeanwiththelocalmean,thelocalmediancouldbe observed to result in a more distinct measure for discrimination: � TUBE , median(s(i)) ≤ Psmooth C2(s) = (4.11) BG , otherwise The better performance of measure C2 originates in the characteristic of the median tobelesssensitivetooutlierscomparedtothemean[32]. Thisisimportantsincethe input data can be very unsteady due to the background texture or printing visible on transparent tubes (independent of the additional camera noise level). As mentioned before, thesmoothingoftheprofileatthefirststepalsoblursthetubeedgescausingthesegment boundaries not to be totally precise. In this case, the local mean tends to move closer to the global mean, which does not have to implicate a misclassification. The median, however, turned out to be more distinct in most cases. Figure 4.14 shows the smoothed profile of (a) a transparent and (b) a black tube respectively. The examples represents the states entering + centered and entering + centered + leaving. The segment boundaries, which correspond to the locations of the strongest peaks in the first derivative of the profile, are visualized as well as the global mean and the local median. Segments that have a median above the global mean are classified as background. Regional Threshold One drawback of the global threshold approach is that different background segments are assumed to be almost equal in image brightness, i.e. the tubebackground contrast is approximately uniform within one image. This assumption, however, does not hold if there are larger variations in background brightness (for example due to material properties or dirt on the belt). Such variations can occur between images,
70 CHAPTER 4. LENGTH MEASUREMENT APPROACH 400 350 300 250 200 150 100 50 ! 400 350 300 250 200 150 100 50 smoothed profile segment boundaries predicted tube boundaries local median global mean Background Background Tube 1 Tube 2 0 20 40 60 80 100 x 120 140 160 180 (a) Transparent/ State: entering + centered smoothed profile segment boundaries predicted tube boundaries local median global mean Background Background 0 Tube 1 Tube 2 Tube 3 0 20 40 60 80 100 x 120 140 160 180 (b) Black/ State: entering + centered + leaving Figure 4.14: Different steps and results of the profile analysis. After smoothing the profile, strong peaks in the first derivative indicate potential tube boundaries. The segments between the strongest peaks are classified into foreground and background based on the difference between the local median of each segment and the global mean. The background is assumed to be brighter on average. Neighboring segments of the same class are merged. The crosses mark the correctly predicted boundaries of the centered tube. Note the stronger contrast of black tubes.
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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
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 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 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 -
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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
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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 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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