Image Acquisitionand Proces

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Image Processing 93 attributes. The VI has three outputs of interest: event, coordinates and other parameters, which are populated as shown in Table 5.1. TABLE 5.1 Event ROI Tool Coordinates [Array] Other Parameters [Array] None Any NULL NULL Click Cursor [0,1] xy position of the click [0,1,2] selected pixel intensity Double click Any [0,1] xy position of the click [0,1,2] selected pixel intensity Zoom [0,1] xy position of the click [2,3] image xy center position [0] new zoom factor Draw Line [0,1] xy start point [2,3] xy end point Rectangle [0,1] xy start point [2,3] xy end point Oval [0,1] bounding rectangle start point [2,3] bounding rectangle end point Polygon [0,1] bounding rectangle start point [2,3] bounding rectangle end point [4,5], [6,7], [8,9] … vertices Freehand [0,1] bounding rectangle start point [2,3] bounding rectangle end point [4,5],[6,7],[8,9]… vertices Move Any [0,1] new position of the window NULL Size Any [0] new width of the window NULL [1] new height of the window Scroll Any [0,1] new center position of image NULL [0] bounding rectangle width [1] bounding rectangle height [2] vertical segment angle [0] width [1] height [0] bounding rectangle width [1] bounding rectangle height [0] bounding rectangle width [1] bounding rectangle height [0] bounding rectangle width [1] bounding rectangle height In this instance, the user was able to use the draw vertices data returned in the coordinates array, which was subsequently built into an array of clusters (each containing the x,y pair of the pixels that were traced over). This allowed the user to trace the path of the mouse (Figure 5.15). As you can see in Figure 5.14, the coordinates array returns the start and end point of the freehand draw operation, as well as the vertices of each point along the way: 5.1.3 MANUALLY BUILDING A ROI: THE “ROI DESCRIPTOR” It is often beneÞcial to programmatically deÞne ROIs, especially if there are several objects of interest that are always in the same place within a series of
94 Image Acquisition Processing with LabVIEW FIGURE 5.14 Trace mouse cursor example. FIGURE 5.15 Trace mouse cursor example — wiring diagram. images (e.g., parts on a production line). Setting the upper left and lower right corners is not enough to fully deÞne a ROI, as it contains several other attributes. The ROI itself is carried around LabVIEW diagrams as a special cluster called the ROI Descriptor (Figure 5.16; Table 5.2). Figure 5.17 is an example of a dynamically deÞned rectangular ROI. As the user changes the Manual Rect ROI values, a new ROI Descriptor is calculated with an internal ID. The image data within the ROI is then extracted and displayed as a new
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IMAGE PROCESSING SERIES Series Edit
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Foreword The introduction of LabVIE
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FIGURE 1 Vision 6.1 FIGURE 2 Compan
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Acknowledgments My gratitude goes o
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Dedication To Don “old fella” P
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Contents Chapter 1 Image Types and
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Chapter 4 Displaying Images........
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Chapter 7 Image Analysis...........
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1 Image Types and File Management F
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Image Types and File Management 3 F
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Image Types and File Management 5 A
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Image Types and File Management 7 C
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Image Types and File Management 9 p
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Image Types and File Management 11
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Image Types and File Management 13
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2 Setting Up You should never under
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Setting Up 17 FIGURE 2.3 (a) Interl
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Setting Up 19 Linear Aray Light Sou
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Setting Up 21 FIGURE 2.7 National I
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Setting Up 23 2.1.3.2 Control and I
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Setting Up 25 FIGURE 2.11 X-Ray ima
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Setting Up 27 Apple Computer techno
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Setting Up 29 FIGURE 2.17 An inexpe
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Setting Up 31 Although this would b
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Setting Up 33 134 -115 \ Contrast =
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Setting Up 35 resolution printer wi
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Setting Up 37 C A B C Traditional T
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Setting Up 39 Camera Light Source O
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Setting Up 41 Camera Light Source (
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Morphology 143 I Closed { } = Erode
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Morphology 145 FIGURE 6.3 Simple mo
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Morphology 147 FIGURE 6.5 Custom st
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Morphology 149 FIGURE 6.8 Particle
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Morphology 151 FIGURE 6.12 Fill hol
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Morphology 153 FIGURE 6.17 Danielss
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Morphology 155 Detected circles wit
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Morphology 157 6.5 CASE STUDY: FIND
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Morphology 159 6.6 USER SOLUTION: D
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Morphology 161 ULTRASOUND MACHINE (
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Morphology 163 FIGURE 6.31 The Edit
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166 Image Acquisition Processing wi
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8 Machine Vision A generally accept
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Machine Vision 219 TABLE 8.1 “IMA
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Machine Vision 221 TABLE 8.3 Input
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Machine Vision 223 packages, includ
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Machine Vision 225 good sense to ad
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Machine Vision 227 TABLE 8.4 “IMA
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Glossary 1-Bit Image An image compr
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Glossary 231 binary display device
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Glossary 233 spatial Þlters, each
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Glossary 235 Resolution There are t
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Image Acquisitionand Proces

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