Image Acquisitionand Proces

Recommendations

Info

Image Types and File Management 3 FIGURE 1.2 Image data represented as a surface plot. Increasing the bit depth of the image to 16 bits also increases the amount of memory required to store the image in a raw format: MemoryRequired = 1024 ¥ 768 ¥ 16 = 12582912Bits = 1572864Bytes = 1.536MBytes You should therefore select an image depth that corresponds to the next-highest level above the level required, e.g., if you need to use a 7-bit image, select 8 bits, but not 16 bits. Images with bit depths that do not correspond to those listed in the previous table (e.g., 1, 2 and 4-bit images) are converted to the next-highest acceptable bit depths when they are initially loaded. 1.1.2 COLOR Color images are represented using either the Red-Green-Blue (RGB) or Hue- Saturation-Luminance (HSL) models. The Vision Toolkit accepts 32-bit color images that use either of these models, as four 8-bit channels: Color Model Pixel Depth Channel Intensity Extremities RGB a Red Green Blue 0 to 255 HSL a Hue Saturation Luminance 0 to 255 The alpha (a) component describes the opacity of an image, with zero representing a clear pixel and 255 representing a fully opaque pixel. This enables an image to be rendered over another image, with some of the underlying image showing through. When combining images, alpha-based pixel formats have several advantages over color-keyed formats, including support for shapes with soft or
4 Image Acquisition Processing with LabVIEW antialiased edges, and the ability to paste the image over a background with the foreground information seemingly blending in. Generally, this form of transparency is of little use to the industrial vision system user, so the Vision Toolkit ignores all forms of a information. The size of color images follows the same relationship as grayscale images, so a 1024 ¥ 768 24-bit color image (which equates to a 32-bit image including 8 bits for the alpha channel) would require the following amount of memory: MemoryRequired = 1024 ¥ 768 ¥ 32 = 25165824 Bits = 3145728 Bytes = 3.072 MBytes 1.1.3 COMPLEX Complex images derive their name from the fact that their representation includes real and complex components. Complex image pixels are stored as 64-bit ßoatingpoint numbers, which are constructed with 32-bit real and 32-bit imaginary parts. Pixel Depth Channel Intensity Extremities Real (4 bytes) Imaginary (4 bytes) –2147483648 to 2147483647 A complex image contains frequency information representing a grayscale image, and therefore can be useful when you need to apply frequency domain processes to the image data. Complex images are created by performing a fast Fourier transform (FFT) on a grayscale image, and can be converted back to their original state by applying an inverse FFT. Magnitude and phase relationships can be easily extracted from complex images. 1.2 FILE TYPES Some of the earliest image Þle types consisted of ASCII text-delimited strings, with each delimiter separating relative pixel intensities. Consider the following example: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 125 125 125 125 125 0 0 0 0 0 125 25 25 25 25 25 125 0 0 0 125 25 25 255 25 255 25 25 125 0 0 125 25 25 25 25 25 25 25 125 0 0 125 25 25 25 255 25 25 25 125 0 0 125 25 255 25 25 25 255 25 125 0 0 125 5 25 255 255 255 25 25 125 0 0 0 125 25 25 25 25 25 125 0 0 0 0 0 125 125 125 125 125 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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 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 77 and 78:
54 Image Acquisition Processing wit
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:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
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 189 and 190:
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
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

Create successful ePaper yourself

Delete template?

Save as template?