Master Thesis - Fachbereich Informatik

More documents

Recommendations

Info

4.7. TEACH-IN 93 inthemeasuringplaneΠM that can be represented by one pixel in the image plane. The total length in mm Ltotal of tube i canbecomputedasfollows: Ltotal(i) =ltotal(i)fpix2mm (4.27) The length Ltotal is used for the good/bad classification whether a tube meets the allowed tolerances. This can be formalized to: � GOOD if |Ltotal(i) − Ltarget|
94 CHAPTER 4. LENGTH MEASUREMENT APPROACH and background afterward. It is computed dynamically based on the regional mean of the profile and a constant factor αpeak (see Equation 4.9). Although this parameter is assumed to be constant it has to be trained once with respect to the conveyor belt used. The teach-in of this parameter is very simple and intuitive. The visual system is set to inspection mode, i.e. it is started as for standard measuring. The conveyor is empty, but moving. The operator can adjust αpeak online starting at a quite low value. This value is slightly increased as long as the system detects tubes (ghosts) where actually no tubes are. Until now this procedure has to be performed manually, but one could think of an automated version to reduce the influence of a human operator which is always a source of errors. To ensure the threshold has not become too large, several tubes are placed on the conveyor. If the system is able to successfully detect all tubes (detection does not mean the length has to be computed correctly in this context), the profile threshold factor is assumed to be trained sufficiently. If the conveyor belt is not uniformly translucent, i.e. the overall image brightness changes significantly over time, one has to assure that the system is able to detect a tube both at the brightest and at the darkest region of the belt. 4.7.3. Perspective Correction Parameters As introduced in Section 4.6.2 perspective effects in the measuring data can be reduced using a perspective correction function fcor(x). This function has two parameters c1 and c2 that have to be learned in the teach-in step from real data. One intuitive method to do this is to measure a tube at a very slow conveyor velocity. The result is a set of pixel length measurements (see Figure 4.25(a)) at almost every position in the image. Then, the parameters of a second order polynomial f(x) =c1x 2 + c2x + c3 can be computed using nonlinear least-squares (NLLS) methods. In this case, a standard Levenberg-Marquardt algorithm [53] is used. The resulting parameters c1 and c2 can be directly inserted into Equation 4.22 to compute fcor(x). For robust results this procedure can be repeated several times and the final parameter set is averaged. Alternatively one could first acquire measurements of several tubes and fit the correction function to the total data. 4.7.4. Calibration Factor The most important parameter to be trained in the teach-in step is the calibration factor that relates a length in the image to a real world length in the measuring plane ΠM. This factor has been introduced as fpix2mm. The idea is to learn the calibration factor based on correspondences between measurements and ground truth data. In an interactive process the operator places a tube of known length onto the moving conveyor. The velocity of the conveyor is set to production velocity, i.e. the velocity where the tubes will be measured later. When the tube reaches the visual field of the camera it is measured with the described approach, but at pixel level only. Once the tube has left the measuring area, the total pixel length is computed and the user is asked to enter the real world length of this tube into a dialog box. Again the input device is a standard keyboard in the prototype version of the system.
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
Page 28 and 29:
2.1. VISUAL MEASUREMENTS 13 Figure
Page 30 and 31:
2.1. VISUAL MEASUREMENTS 15 � xd
Page 32 and 33:
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 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 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 134 and 135: 5.3. EXPERIMENTAL RESULTS 119 (a) (
Page 136 and 137: 5.3. EXPERIMENTAL RESULTS 121 (a) (
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.
show all

Master Thesis - Fachbereich Informatik

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?