Practical Guide to Machine Vision Software
An Introduction with LabVIEW

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Language: English
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296 p. · 17.3x24.6 cm · Paperback
For both students and engineers in R&D, this book explains machine vision in a concise, hands-on way, using the Vision Development Module of the LabView software by National Instruments.
Following a short introduction to the basics of machine vision and the technical procedures of image acquisition, the book goes on to guide readers in the use of the various software functions of LabView's machine vision module. It covers typical machine vision tasks, including particle analysis, edge detection, pattern and shape matching, dimension measurements as well as optical character recognition, enabling readers to quickly and efficiently use these functions for their own machine vision applications. A discussion of the concepts involved in programming the Vision Development Module rounds off the book, while example problems and exercises are included for training purposes as well as to further explain the concept of machine vision.
With its step-by-step guide and clear structure, this is an essential reference for beginners and experienced researchers alike.

About the Authors ix

Preface xi

1 Basics of Machine Vision 1

1.1 Digital Images 1

1.1.1 Grayscale Image 1

1.1.2 Binary Image 2

1.1.3 Color Image 3

1.2 Components of Imaging System 5

1.2.1 Camera 6

1.2.2 Camera Bus: The Method to Connect PC and Camera 10

1.2.3 Lens 13

1.2.4 Lighting 15

2 Image Acquisition with LabVIEW 17

2.1 Acquiring Images with MAX 17

2.2 Acquiring Images Using LabVIEW 19

2.2.1 IMAQdx Functions 19

2.2.2 Image Management Functions 21

2.2.3 Block Diagram for Image Acquisition 23

2.2.4 Image Acquisition from Example 23

2.2.5 Vision Acquisition Express 26

3 Particle Analysis 33

3.1 Particle Analysis Using Vision Assistant 34

3.1.1 Image Acquisition Using Vision Assistant 35

3.1.2 Image Processing Functions 37

3.1.3 Setting a ROI (Region of Interest) 38

3.1.4 Binary Image Conversion 40

3.1.5 Morphology 43

3.1.6 Particle Analysis 44

3.2 LabVIEW Code Creation Using Vision Assistant 47

3.2.1 Block Diagram of Created LabVIEW Code 50

3.2.2 Image Type Modification 54

3.3 LabVIEW Code Modification 55

3.3.1 SubVI for Particle Analysis 55

3.4 Particle Analysis Using Vision Express 67

3.4.1 Vision Acquisition Express 67

3.4.2 Vision Assistant Express 68

3.5 Conversion of Pixels to Real-World Units 71

4 Edge Detection 75

4.1 Edge Detection via Vision Assistant 75

4.2 LabVIEW Code for Edge Detection 78

4.3 Vi for Real-time-based Edge Detection 81

4.4 The Use of Vision Assistant Express for Real-Time Edge Detection 85

5 Pattern Matching 89

5.1 Pattern Matching Using Vision Assistant 90

5.2 LabVIEW Code Creation and Modification 96

5.3 Main VI for Pattern Matching 99

5.4 Vision Assistant Express 101

6 Color Pattern Matching 105

6.1 Color Pattern Matching Using Vision Assistant Express 105

6.1.1 Vision Acquisition Express 107

6.1.2 Vision Assistant Express 108

6.1.3 Main VI 112

7 Dimension Measurement 117

7.1 Dimension Measurement Using Vision Assistant Express 117

7.1.1 Find Circular Edge Function 119

7.1.2 Clamp Function 119

7.1.3 Caliper Function 123

7.2 Vi Creation for Dimension Measurement 126

7.2.1 Vision Assistant Express VI for Dimension Measurement 126

7.2.2 ROI Array 127

7.2.3 Front Panel of Main Vi 129

7.2.4 Block Diagram of the Main Vi 130

8 Dimension Measurement Using Coordinate System 135

8.1 Measurement Based on a Reference Coordinate System Using Vision Assistant Express 135

8.1.1 Pattern Matching 137

8.1.2 Coordinate System 138

8.1.3 Dimension Measurement Using the Clamp Function 141

8.1.4 Measurement of Circle Edge 142

8.2 Conversion of Vision Assistant Express to a Standard Vi 145

9 Geometric Matching 149

9.1 Geometric Matching Using Vision Assistant Express 150

9.1.1 Geometric Matching for Circles 151

9.1.2 Geometric Matching for Ellipses 155

9.2 Vi Creation for Geometric Matching 158

9.3 Shape Detection 159

10 Binary Shape Matching 165

10.1 Accessing Previously Saved Images with Vision Acquisition Express 166

10.2 Binary Shape Matching Using Vision Assistant 168

10.2.1 Binary Template Images 169

10.2.2 Binary Shape Matching 170

10.3 Overlay VI Creation for Shape Matching 172

10.4 Vi for Binary Shape Matching 173

11 OCR (Optical Character Recognition) 177

11.1 OCR Using Vision Assistant 177

11.1.1 Character Training Using Vision Assistant 177

11.1.2 Character Identification Using Vision Assistant 181

11.2 VI for OCR 185

11.2.1 VI Creation for OCR Using Vision Assistant 185

11.2.2 SubVI for OCR 185

11.2.3 Main VI 187

12 Binary Particle Classification 191

12.1 Vision Acquisition Express to Load Image Files 192

12.2 Vision Assistant Express for Classification 194

12.2.1 Train for Particle Classification 194

12.2.2 Vi Creation 199

12.3 Vi Modification 200

12.4 Overlay for Classification 204

12.5 Main VI for Classification 206

13 Contour Analysis 209

13.1 Contour Analysis 210

13.1.1 Image Acquisition Using a USB Camera 210

13.1.2 Contour Analysis Using Vision Assistant 212

13.1.3 Defect Detection Using Curvature 215

13.1.4 Defect Detection by Comparing Contours 216

13.1.5 Vi Creation 219

13.2 VIs for Contour Analysis 219

13.2.1 Main Vi 219

13.2.2 Overlay for Defects 222

13.2.3 Perspective Errors in Images 225

14 Image Calibration and Correction 227

14.1 Method for Creating an Image Correction File 227

14.1.1 Image Acquisition 228

14.1.2 New Calibration File 228

14.2 Image Correction 234

14.2.1 Image Correction Using Vision Assistant Express 234

14.2.2 VI Creation for Image Correction 237

15 Saving and Reading Images 241

15.1 Saving Image 241

15.2 Image Read from File 245

15.2.1 IMAQ Readfile 245

15.2.2 Example of Reading Image from Image Files 246

16 AVI File Write and Read 249

16.1 AVI File Creation Using Image Files 249

16.2 AVI File Creation Based on Real-Time Image Acquisition 251

16.3 Read Frame from AVI Files 252

17 Tracking 255

17.1 Tracking with the Use of Vision Assistant 255

17.2 VI Creation for Tracking Objects 259

18 LabVIEW Machine Vision Applications* 263

18.1 Semiconductor Manufacturing 263

18.2 Automobile Industry 264

18.3 Medical and Bio Applications 266

18.4 Inspection 268

18.5 Industrial Printing 269

19 Student Projects 271

Project 1: Noncontact Motion Measurement and its Analysis 271

Project 2: Intelligent Surveillance Camera 271

Project 3: Driving a LEGO NXT Car (LEGO Mindstorm) with Finger Motion 273

Project 4: Piano Keyboard Using Machine Vision 273

Index 275

Kye-Si Kwon is an associate professor at Soonchunhyang University in Korea in the department of mechanical engineering. After his PhD, obtained from KAIST, Korea, in 1999, he was a member of research staffs in companies such as Samsung and LG electronics. He joined Soonchunhyang University in 2006 where his teaching and research is centered on inkjet-related measurement methods and system developments. In 2012, he spent one year at Palo Alto Research Center (PARC) in Palo Alto, California, as a visiting researcher. He also established a start-up company and is CEO of PS. Co. Ltd (www.psolution.kr).

Steven Ready joined the Palo Alto Research Center more than two decades ago, where he designed and developed several high-accuracy inkjet printers for printed organic electronics and documents; studied the role of hydrogen in amorphous, polycrystalline, and crystalline silicon and associated applications; and contributed to the development of large-area amorphous and polycrystalline silicon arrays for optical and x-ray imaging, displays, and organic semiconductor materials and devices. Steven Ready has also made significant contributions to developing laser crystallization of silicon thin films; a fragile book scanner; control software for MOCVD reactors; and a scanning tunneling microscope. He is a member of the SPIE, MRS, and IS&T professional societies. He obtained his degree in Physics from the University of California at Santa Cruz.