Image Acquisitionand Proces

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Image Processing 133 Unlike a smoothing Þlter, the PUI weighting is greater than 1 and also greater than any of the neighboring pixels. This leads to a stronger inclination to the original PUI value, which in turn promotes a much weaker smoothing effect. As you would expect, a larger kernel grid invites the inßuence of pixels farther away, increasing the blurring effect: 1 2 4 2 1 2 4 8 4 2 4 8 16 8 4 2 4 8 4 2 1 2 1 2 1 5.8.2.2 Gradient Often one of the most interesting (and fun to play with!) Þlter families is the gradient. This Þlter is particularly useful when attempting to map intensity variations along a speciÞc axis of the image. An example of a 45û gradient Þlter is: 0 –1 –1 1 0 –1 1 1 0 This Þlter example seemingly “sweeps” through the image from the top right corner, highlighting variations in intensity, in the same manner as a Þrst-order derivative. If you were to imagine this sweeping, as the Þlter detects an increase in intensity, it is marked lighter and decreases are marked darker (Figure 5.65). Although I have described this Þlter as having a “direction,” all noncumulative Þlters are purely directionless — the new image is built from artifacts of the source image only and therefore mathematically Þltering the pixels in differing directions (or in a random order) has no effect. Another useful feature of the gradient Þlter is your ability to increase the apparent thickness of the edges detected. As you increase the size of your kernel, the thickness also increases (Figure 5.66). As you can see from the 7 ¥ 7 kernel example, the image has become quite distorted as the intensity gradients are more highlighted from the background, so the value of increasing it to this level is questionable (depending on the software application’s purpose). In the previous example, the 5 ¥ 5 kernel looks to be a good choice and the effect of the Þlter is even more apparent when the negative of the image is taken (Figure 5.67).
134 Image Acquisition Processing with LabVIEW FIGURE 5.65 (a) Original shrub leaves image. (b) 45û Gradient Þltered image. (c) 125û Gradient Þltered image. FIGURE 5.66 (a) 3 ¥ 3 45û Gradient Þltered image. (b) 5 ¥ 5 45û Gradient Þltered image. (c) 7 ¥ 7 45û Gradient Þltered image.
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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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Setting Up 43 FIGURE 2.37 (a) An ob
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Setting Up 45 FIGURE 2.41 (a) PCB p
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48 Image Acquisition Processing wit
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184 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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