Image Acquisitionand Proces

Recommendations

Info

Image Analysis 177 FIGURE 7.14 Front panel of the PIN alignment inspection software. FIGURE 7.15 A portion of the image processing wiring diagram. image for defective pins in the Þeld of view and displays the results on the front panel in the Results box. The number of bad and good pins is also updated with every image processed. Shift-invariant pattern matching is used to Þnd the pins and the surrounding areas with two bright spots. Using thresholding and morphology, the bright spots are removed and the coordinates of the pin center are measured and checked for alignment within the limits of the inspection criteria. The overall result of the algorithm is a robust detection routine that eliminates false positives in most lighting conditions (Figure 7.15). As can be seen from the wiring diagram, an ideal pattern search is implemented using the IMAQ Match Pattern function from the Vision Toolkit.
178 Image Acquisition Processing with LabVIEW 7.3 MATHEMATICAL MANIPULATION OF IMAGES If you are not able to Þnd a VI within the Vision Toolkit that will perform the function that you need, and deÞning a custom Þlter (see Chapter 5) is not appropriate, then breaking an image into a two-dimensional array of intensity values and performing a custom mathematical process on them may be the only option available. 7.3.1 IMAGE TO TWO-DIMENSIONAL ARRAY Converting an image to an array is quite simple using IMAQ ImageToArray (Figure 7.16), which parses the image, and returns a two-dimensional array of 8-bit integers that represent the intensity levels across the image (Figure 7.17). IMAQ ImageToArray also has an optional rectangle input, so the user can deÞne a rectangular ROI of intensities to be returned. FIGURE 7.16 Convert grayscale image to two-dimensional array. 7.3.2 IMAGE PORTIONS Rather than returning a two-dimensional array of a complete image or a rectangular ROI, IMAQ GetPixelValue, IMAQ GetRowCol and IMAQ GetPixelLine are useful when you need to extract particular predeÞned portions of an image. In the example shown in Figure 7.18, the user has selected a pixel in the middle of the image, and IMAQ GetPixelValue has returned the intensity value of that pixel. IMAQ GetRowCol has also been used to return intensity contours of the horizontal row and vertical column that intersect at the PUI, and IMAQ GetPixelLine returns the intensity values along an arbitrary line from the top left corner of the image to the bottom right (Figure 7.19). IMAQ SetPixelValue, IMAQ SetRowCol and IMAQ SetPixelLine accept new intensity value inputs, allowing you to set the intensity values of image portions (Figure 7.20). As you can see in Figure 7.21, the user can set the new intensity
Page 3:
IMAGE PROCESSING SERIES Series Edit
Page 6:
Foreword The introduction of LabVIE
Page 9 and 10:
FIGURE 1 Vision 6.1 FIGURE 2 Compan
Page 12 and 13:
Acknowledgments My gratitude goes o
Page 14:
Dedication To Don “old fella” P
Page 18 and 19:
Contents Chapter 1 Image Types and
Page 20 and 21:
Chapter 4 Displaying Images........
Page 22 and 23:
Chapter 7 Image Analysis...........
Page 24 and 25:
1 Image Types and File Management F
Page 26 and 27:
Image Types and File Management 3 F
Page 28 and 29:
Image Types and File Management 5 A
Page 30 and 31:
Image Types and File Management 7 C
Page 32 and 33:
Image Types and File Management 9 p
Page 34 and 35:
Image Types and File Management 11
Page 36 and 37:
Image Types and File Management 13
Page 38 and 39:
2 Setting Up You should never under
Page 40 and 41:
Setting Up 17 FIGURE 2.3 (a) Interl
Page 42 and 43:
Setting Up 19 Linear Aray Light Sou
Page 44 and 45:
Setting Up 21 FIGURE 2.7 National I
Page 46 and 47:
Setting Up 23 2.1.3.2 Control and I
Page 48 and 49:
Setting Up 25 FIGURE 2.11 X-Ray ima
Page 50 and 51:
Setting Up 27 Apple Computer techno
Page 52 and 53:
Setting Up 29 FIGURE 2.17 An inexpe
Page 54 and 55:
Setting Up 31 Although this would b
Page 56 and 57:
Setting Up 33 134 -115 \ Contrast =
Page 58 and 59:
Setting Up 35 resolution printer wi
Page 60 and 61:
Setting Up 37 C A B C Traditional T
Page 62 and 63:
Setting Up 39 Camera Light Source O
Page 64 and 65:
Setting Up 41 Camera Light Source (
Page 66 and 67:
Setting Up 43 FIGURE 2.37 (a) An ob
Page 68:
Setting Up 45 FIGURE 2.41 (a) PCB p
Page 71 and 72:
48 Image Acquisition Processing wit
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
100 Image Acquisition Processing wi
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150: 126 Image Acquisition Processing wi
Page 164 and 165: 6 Morphology When applied to vision
Page 166 and 167: Morphology 143 I Closed { } = Erode
Page 168 and 169: Morphology 145 FIGURE 6.3 Simple mo
Page 170 and 171: Morphology 147 FIGURE 6.5 Custom st
Page 172 and 173: Morphology 149 FIGURE 6.8 Particle
Page 174 and 175: Morphology 151 FIGURE 6.12 Fill hol
Page 176 and 177: Morphology 153 FIGURE 6.17 Danielss
Page 178 and 179: Morphology 155 Detected circles wit
Page 180 and 181: Morphology 157 6.5 CASE STUDY: FIND
Page 182 and 183: Morphology 159 6.6 USER SOLUTION: D
Page 184 and 185: Morphology 161 ULTRASOUND MACHINE (
Page 186: Morphology 163 FIGURE 6.31 The Edit
Page 199: 176 Image Acquisition Processing wi
Page 240 and 241: 8 Machine Vision A generally accept
Page 242 and 243: Machine Vision 219 TABLE 8.1 “IMA
Page 244 and 245: Machine Vision 221 TABLE 8.3 Input
Page 246 and 247: Machine Vision 223 packages, includ
Page 248 and 249: Machine Vision 225 good sense to ad
Page 250 and 251:
Machine Vision 227 TABLE 8.4 “IMA
Page 252 and 253:
Glossary 1-Bit Image An image compr
Page 254 and 255:
Glossary 231 binary display device
Page 256 and 257:
Glossary 233 spatial Þlters, each
Page 258:
Glossary 235 Resolution There are t
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
Page 267:
show all

Image Acquisitionand Proces

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

Delete template?

Save as template?