National Instruments NI Vision NI 17xx Benutzerhandbuch
Chapter 3
NI Smart Camera Image Sensor
© National Instruments Corporation
3-5
Figure 3-4 shows what happens when gain is applied to a signal.
Figure 3-4. Effect of Gain on the Video Signal
In Figure 3-4a, low gain has been applied to the signal. The pixel values in
the image are grouped close together. In Figure 3-4b, medium gain has
been applied to the signal; there are now more notable differences in pixel
value within the image. In Figure 3-4c, high gain has been applied to the
signal; at high gain, mid-range and bright portions of the image are now
both represented as white, the highest pixel value. In Figure 3-4c, several
bright areas of the image have been clipped to the maximum pixel value,
and you can no longer distinguish subtle shading in the brightest areas of
the image.
the image are grouped close together. In Figure 3-4b, medium gain has
been applied to the signal; there are now more notable differences in pixel
value within the image. In Figure 3-4c, high gain has been applied to the
signal; at high gain, mid-range and bright portions of the image are now
both represented as white, the highest pixel value. In Figure 3-4c, several
bright areas of the image have been clipped to the maximum pixel value,
and you can no longer distinguish subtle shading in the brightest areas of
the image.
Gain can be useful when there is not enough available light and you need
to increase the brightness of your images. However, increasing gain
multiplies both the signal and noise. When possible, it is preferable to add
additional lighting.
to increase the brightness of your images. However, increasing gain
multiplies both the signal and noise. When possible, it is preferable to add
additional lighting.
Hardware Binarization
The NI Smart Camera supports binarization and inverse binarization of
acquired images. Binarization and inverse binarization segment an image
into two regions—a particle region and a background region. Use
binarization and inverse binarization to isolate objects of interest in an
image.
acquired images. Binarization and inverse binarization segment an image
into two regions—a particle region and a background region. Use
binarization and inverse binarization to isolate objects of interest in an
image.
To separate objects under consideration from the background, select a pixel
value range. This pixel value range is known as the gray-level interval, or
the threshold interval. When enabled, binarization sets all image pixels that
fall within the threshold interval to the image white value and sets all other
value range. This pixel value range is known as the gray-level interval, or
the threshold interval. When enabled, binarization sets all image pixels that
fall within the threshold interval to the image white value and sets all other
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Low Gain
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Medium Gain
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High Gain
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