Chapter 2
Getting Measurement-Ready Images
IMAQ Vision for LabWindows/CVI User Manual
2-12
ni.com
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Closing—Removes dark pixels isolated in bright regions and smooths
boundaries.
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Proper-opening—Removes bright pixels isolated in dark regions and
smooths the inner contours of particles.
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Proper-closing—Removes dark pixels isolated in bright regions and
smooths the inner contours of particles.
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Auto-median—Generates simpler particles that have fewer details.
FFT
Use the Fast Fourier Transform (FFT) to convert an image into its
frequency domain. In an image, details and sharp edges are associated
with mid to high spatial frequencies because they introduce significant
gray-level variations over short distances. Gradually varying patterns are
associated with low spatial frequencies.
An image can have extraneous noise, such as periodic stripes, introduced
during the digitization process. In the frequency domain, the periodic
pattern is reduced to a limited set of high spatial frequencies. Also, the
imaging setup may produce non-uniform lighting of the field of view,
which produces an image with a light drift superimposed on the
information you want to analyze. In the frequency domain, the light drift
appears as a limited set of low frequencies around the average intensity of
the image, the DC component.
You can use algorithms working in the frequency domain to isolate and
remove these unwanted frequencies from your image. Complete the
following steps to obtain an image in which the unwanted pattern has
disappeared but the overall features remain.
1.
Use
imaqFFT()
to convert an image from the spatial domain to the
frequency domain. This function computes the FFT of the image and
results in a complex image representing the frequency information of
your image.
2.
Improve your image in the frequency domain with a lowpass or
highpass frequency filter. Specify which type of filter to use with
imaqAttenuate()
or
imaqTruncate()
. Lowpass filters smooth
noise, details, textures, and sharp edges in an image. Highpass filters
emphasize details, textures, and sharp edges in images, but they also
emphasize noise.
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Lowpass attenuation—The amount of attenuation is directly
proportional to the frequency information. At low frequencies,
there is little attenuation. As the frequencies increase, the
