Master Thesis - Fachbereich Informatik

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5.3. EXPERIMENTAL RESULTS 129 Task Ωtime [ms/frame] Profile Analysis 0.29 Undistortion 1.79 Edge Detection/ 14.57 Template Matching Length computation/ Tracking 0.69 Other 0.48 Total 17.82 Undistortion: 10% Profile Analysis: 2% (a) Other: 3% Length computation/ Tracking: 4% Edge detection/ Template matching: 82% (b) Figure 5.22: (a) Average processing time per frame divided into different steps of the visual inspection. (b) Corresponding pie chart. As one can see, the edge detection and template matching is the dominant operation throughout inspection.
130 CHAPTER 5. RESULTS AND EVALUATION 5.4. Discussion and Future Work The main difficulties with transparent tubes come along with the nonuniform brightness and the texture of the background. A conveyor belt which is equally translucent over the whole length could prevent many problems. The parameters controlling the detection sensitivity must cover both the brightest and the darkest region of the conveyor belt. This is always a compromise leading to poorer results on average. However, if the contrast between tubes and the background does not depend on where the tube is located on the conveyor belt, the parameters can be adjusted much more specific. The background texture of the conveyor belt used for the prototype has the drawback of regular vertical structures. If the tube edge contrast is poor, the edge response of the background may be stronger than the tube edge. Model knowledge can be used to improve the tube edge localization even under the presence of strong vertical background edges. However, there is still a certain error probability which can be drastically reduced if vertical background edges are suppressed. The best solution would be to use a conveyor belt with a canvas of horizontal structure. This would obviously simplify the detection task without requiring any computation time. If no conveyor belt can be found that provides the desired horizontal structure in combination with good translucency characteristics, one can think of suppressing the background pattern within the local ROI around a tube edge algorithmically by exploiting the regularity of the background pattern. One idea is to transform the spatial image into the frequency domain using the Fourier transform. For more information on the Fourier transform and the frequency domain it is referred to [64]. If it is possible to find characteristic frequencies belonging to the background pattern, one can remove these frequencies in the frequency domain and apply the inverse Fourier transform to the filtered spectrum. The result is a filtered spatial image with reduced background structure. The filter must be designed carefully to preserve the tube edges. In a first experiment, test images of both a conveyor with and without a tube have been acquired and transformed into the frequency domain. Figure 5.23(a) and (b) show an example of the spectrum of an image with background only and with transparent tubes in the image respectively. The spatial domain of (b) can be seen in (d). One eye-catching consistency in the spectra are the bright spots. If one removes these spots in the spectrum of an image indicated by the black regions in (c) and applies the inverse Fourier transform to this filtered spectrum, the result is an image with a significantly reduced background pattern. The actual tube edges, however, are quite well preserved. In this case the spectrum has been filtered by hand and only coarse. Much more work has to be spent in designing more sophisticated and reliable filters that perform well for a large number of images without removing or blurring any relevant edges. Removing a frequency from the spectrum does always influence the whole image. The filter in the example produces new structure at the tube regions, especially around the printings. In addition, the darker stripe in the background on the right of the input image is still present in the filtered version, since it does not belong to the regular pattern of the background. Although in this example the dark stripe is not critical it might be in other situations. This shows the limits of this approach. Any deviations from the regular background pattern are difficult to suppress in the frequency domain. If the conveyor belt is changed, the texture of the belt might by completely different. In this case the filter has to be adapted. An automated filter adaptation and background learning is non trivial.
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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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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
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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) (
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Page 140 and 141: 5.3. EXPERIMENTAL RESULTS 125 Total
Page 142 and 143: 5.3. EXPERIMENTAL RESULTS 127 Color
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.
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Master Thesis - Fachbereich Informatik

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