Image Acquisitionand Proces

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Setting Up 29 FIGURE 2.17 An inexpensive webcam with a USB interface. 2.2.3 USB SYSTEMS Although very inexpensive, universal serial bus (USB) cameras often sufÞce when your image acquisition needs are simple (Figure 2.17). USB cameras are small, easily connected to your PC, and do not require a frame grabber card. There are currently two USB standards: the original version is able to handle data rates of 1.5 Mbps, whereas the new version (USB 2.0) can transmit at 12 Mbps. USB 2.0 can theoretically reach transfer rates of 480 Mbps; however, there is a wide variation in edge rates. With typical line loads, full speed devices usually fall in the 12 to 25 ns range, and low-speed devices typically range 110 to 225 ns. The USB standard allows for a limit of 127 devices to be connected simultaneously, although devices often reserve large ranges of the available bandwidth, so this limit is rarely practically achievable. If a large number of devices are required, you can install extra USB interfaces in your PC, effectively providing independent USB buses and raising the number of devices that will function simultaneously. USB cable lengths are limited to 5 m due to a cable delay speciÞcation of 26 ns that allows for signal reßections to settle in the cable before any subsequent bits are transmitted. Both USB and IEEE 1394 are serial buses; IEEE 1394 can move more data in a given amount of time, but is considerably more expensive than USB. Applications that are best suited for FireWire are high-bandwidth applications (e.g., external disk drives and high-quality video streams), whereas USB Þlls the middle- and lowbandwidth application niche (including Webcams, audio, scanners and printers). National Instruments does not provide native support for USB cameras, although several LabVIEW drivers are available. One such solution is Peter Parente’s Webcam Library, which is featured in Chapter 3. More information regarding the USB standards and applicable hardware can be found at the USB Implementers Forum Web site (http://www.usb.org).
30 <strong>Image</strong> Acquisition <strong>Proces</strong>sing with LabVIEW 2.3 FROM OBJECT TO CAMERA Often the physical constraints of the object you are monitoring or existing hardware around it will control the placement of your camera, lens system and lighting. Before you buy any hardware, you should consider several factors. 2.3.1 RESOLUTION The resolution of a camera system refers to the smallest feature of the object that it can distinguish. A common example used to demonstrate resolution is a barcode, shown in Figure 2.18. As you can see, a barcode is made from black and white bars FIGURE 2.18 Barcode. of varying width. If the resolution of your acquisition system is too coarse to resolve between the smallest black and white bars, then useful data will be lost, and you will not be able to resolve the barcode. One common solution is to use the highest resolution system available, and then you will never lose useful data. However, there are several reasons not to choose this option: it requires more processing power and memory, is much slower and is much more expensive. The tricky part is to determine a suitable compromise, and therefore most efÞcient and economical resolution. As a general rule, your smallest useful object feature should be at least 2 pixels. If so, you can be assured of never missing it due to its image falling on an inactive part (sections between each of the CCD array elements) of your detector. Although that sounds simple, you need to consider the lens system that you are using to focus your image on the detector array. For example, using a 2¥ zoom system will effectively increase the resolution of your image, but decrease its Þeld of vision. To determine the sensor resolution, you will require to achieve a smallest feature of 2 pixels, you can use the following equation: È FieldOfView Sensor Resolution 1 D = 2 Ê ˆù Í Ë SmallestFeature ¯ú The sensor resolution is a dimensionless quantity; use the same units for both the Þeld of view and smallest feature to achieve a useful answer. For example, when reading a barcode 35 mm long, with a narrow bar of 0.75 mm, through a lens system of 1¥ magniÞcation, the sensor resolution would need to be at least: È Sensor Resolution 1 2 35 D = Ê ˆù Í 94pixels Ë 075 . ¯ú =
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
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Page 38 and 39: 2 Setting Up You should never under
Page 40 and 41: Setting Up 17 FIGURE 2.3 (a) Interl
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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
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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
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100 Image Acquisition Processing wi
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6 Morphology When applied to vision
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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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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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