Master Thesis - Fachbereich Informatik

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Bibliography 147 [34] R. J. Hunsicker, J. Patten, A. Ledford, C Ferman, et al. Automatic vision inspection and measurement system for external screw threads. Journal of Manufacturing Systems, 1994. [35] R. W. Hunt. Measuring Colour. Ellis Horwood Ltd. Publishers, 2nd edition, 1991. [36] B. Jähne. Digital Image Processing. Springer, 6th edition, 2005. [37] B. Julez. A method of coding TV signals based on edge detection. Bell System Tech., 38(4):1001–1020, July 1959. [38] R. King. Brunelleschi’s Dome: How a Renaissance Genius Reinvented Architecture. Penguin Books, 2001. [39] R. K. Lenz and R. Y. Tsai. Calibrating a cartesian robot with eye-on-hand configuration independent of eye-to-hand relationship. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 11(9):916–928, September 1989. [40] J. Linkemann. Optics recommendation guide. http://www.baslerweb.com/. [41] E. P. Lyvers, O. R. Mitchell, M. L. Akey, and A. P. Reeves. Subpixel measurements using a moment-based edge operator. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 11(12):1293–1309, December 1989. [42] E. N. Malamas, E. G. M. Petrakis, M. E. Zervakis, L. Petit, and J. D. Legat. A survey on industrial vision systems, applications and tools. Israel Venture Capital (IVC), 21(2):171–188, February 2003. [43] M. Malassiotis and G. Strintzis. Stereo vision system for precision dimensional inspection of 3d holes. Machine Vision and Applications, 15(2):101–113, December 2003. [44] J. Malik and P. Perona. Preattentive texture discrimination with early vision mechanism. Journal of the Optical Society of America, 7(5):923–932, May 1990. [45] D. Marr and E. C. Hildreth. Theory of edge detection. Proc. Royal Soc. London, B207:187–217, 1980. [46] N. Otsu. A threshold selection method from grey-level histograms. IEEE Transactions on Systems, Man, and Cybernetics (SMC), 9(1):62–66, January 1979. [47] N. R. Pal and S. K. Pal. A review on image segmentation techniques. Pattern Recognition, 26(9):1277–1294, September 1993. [48] J.R. Parker. Algorithms for image processing and computer vision. John Wiley & Sons, Inc., 1997. [49] P. Perona. Deformable kernels for early vision. IEEE Transpaction on Pattern Analysis and Machine Intelligence, 17(5):488–499, May 1995. [50] D. T1 Pham and R. J Alcock. Automated visual inspection of wood boards: Selection of features for defect classification by a neural network. In Proc.oftheIMECH E Part E Journal of Process Mechanical Engineering, volume 213, pages 231–245. Professional Engineering Publishing, 1999.
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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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4.3. CAMERA CALIBRATION 59 4.2.7. B
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4.3. CAMERA CALIBRATION 61 Figure 4
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4.3. CAMERA CALIBRATION 63 is compu
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4.4. TUBE LOCALIZATION 65 (a) (b) F
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4.4. TUBE LOCALIZATION 67 � 1 Psm
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4.4. TUBE LOCALIZATION 69 Global Th
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4.4. TUBE LOCALIZATION 71 but also
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4.4. TUBE LOCALIZATION 73 Global Re
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4.5. MEASURING POINT DETECTION 75 f
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4.5. MEASURING POINT DETECTION 77 T
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4.5. MEASURING POINT DETECTION 79 -
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4.5. MEASURING POINT DETECTION 81 0
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4.5. MEASURING POINT DETECTION 83 o
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4.5. MEASURING POINT DETECTION 85 (
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4.5. MEASURING POINT DETECTION 87 w
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4.6. MEASURING 89 side of the tube,
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4.6. MEASURING 91 fcor(x) =−(c1x
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4.7. TEACH-IN 93 inthemeasuringplan
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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: 146 Bibliography [18] C. Demant, B.
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