Master Thesis - Fachbereich Informatik

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Bibliography [1] Y.I. Abdel-Aziz and H.M. Karara. Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. Proc. of the Symposium on Close-Range Photogrammetry, pages 1–18, 1971. [2] M. B. Ahmad and T. S. Choi. Local threshold and boolean function based edge detection. IEEE Trans. on Consumer Electronics, 45(3):674–679, August 1999. [3] Allied Vision Technologies GmbH, Taschenweg 2a, D-07646 Stadtroda, Germany. AVT Marlin - Technical Manual, 7 2004. [4] A. Alper. An inside look at machine vision. Managing Automation, 2005. [5] American Society for Photogrammetry and Remote Sensing (ASPRS). Manual of Photogrammetry. Asprs Pubns, 4th edition, 1980. [6] K. Astrom and A. Heyden. Stochastic modelling and analysis of sub-pixel edge detection. In International Conference on Pattern Recognition (ICPR), pages 86–90, 1996. [7] B. Batchelor and F. Waltz. Intelligent Machine Vision. Springer, 2001. [8] A. Blake. Active Contours. Springer, 1999. [9] J. Y. Bouguet. Camera calibration toolbox for matlab. [10] I. N. Bronstein, G. Musiol, H. Mühlig, and K. A. Semendjajew. Taschenbuch der Mathematik. Harri Deutsch, 2001. [11] D. C. Brown. Decentering distortion of lenses. Photometric Engineering, 32(3):444– 462, 1966. [12] D. C. Brown. Lens distortion for close-range photogrammetry. Photometric Engineering, 37(8):855–866, 1971. [13] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 8:679–698, 1986. [14] T. Chaira and A. K. Ray. Threshold selection using fuzzy set theory. Pattern Recognition Letters (PRL), 25(8):865–874, June 2004. [15] R. W. Conners, D. E. Kline, P. A. Araman, and T.H. Drayer. Machine vision technology for the forest products industry. Computer, 30(7):43–48, 1997. [16] E. R. Davies. Machine Vision- Theory, Algorithms, Practicalities. Elsevier, 2005. [17] C. de Boor. A practical guide to splines. Springer, 1978. 145
146 Bibliography [18] C. Demant, B. Streicher-Abel, and P. Waszkewitz. Industrial Image Processing - Visual Quality Control in Manufacturing. Springer, 1999. [19] R. Deriche. Using canny’s criteria to derive a recursively implemented optimal edge detector. International Journal of Computer Vision (IJCV), 1(2):167–187, 1987. [20] S. di Zenzo, L. Cinque, and S. Levialdi. Image thresholding using fuzzy entropies. IEEE Transactions on Systems, Man, and Cybernetics (SMC-B), 28(1):15–23, February 1998. [21] O. Faugeras. Three-Dimensional Computer Vision. A Geometric Viewpoint. MIT Press, Cambridge, 1993. [22] J. Föglein. On edge gradient approximations. Pattern Recognition Letters (PRL), 1:429–434, 1983. [23] P. J. Flynn and A. K. Jain. Cad-based computer vision: From cad models to relational graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 13(2):114–132, 1991. [24] D. A. Forsyth and J. Ponce. Computer Vision - A modern approach. Pearson Education International, 2003. [25] W. T. Freeman and E. H. Adelson. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 13(9):891–906, 1991. [26] C. A Glasbey. An analysis of histogram-based thresholding algorithm. Graphical Models and Image Processing, 55(6):532–537, November 1993. [27] E. B. Goldstein. Sensation and Perception. California: Brooks/Cole Publishing Co., 1996. [28] R. C. Gonzalez and R. E. Woods. Digital Image Processing. Prentice Hall, 2nd edition, 2002. [29] E. R. Hancock and J. V. Kittler. Adaptive estimation of hysteresis thresholds. In Proc. of the IEEE Computer Vision and Pattern Recognition (CVPR), pages 196–201, 1991. [30] R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2nd edition, 2003. [31] J. Heikkila and O. Silven. A four-step camera calibration procedure with implicit image correction. In Proc. of the IEEE Computer Vision and Pattern Recognition (CVPR), pages 1106–1112, 1997. [32] R. V. Hogg and A. T. Craig. Introduction to Mathematical Statistics. Prentice Hall, 5 edition, 1994. [33] D. H. Hubel. Exploration of the primary visual cortex, 1955-1978. Nature, 299:515– 524, 1982.
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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
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
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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: 144 APPENDIX B. HARDWARE COMPONENTS
Page 163 and 164: 148 Bibliography [51] K.K. Pingle.
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Master Thesis - Fachbereich Informatik

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