National Instruments IMAQ Vision for Measurement Studio - Manual (Page 22)

Chapter 2 Getting Measurement-Ready Images
© National Instruments Corporation 2-9 IMAQ Vision for LabWindows/CVI User Manual
this range in processing functions, such as determining a threshold range
during blob analysis.
If the image quality does not meet your needs, try to improve the imaging
conditions to get the desired image quality. You may need to re-evaluate
and modify each component of your imaging setup: lighting equipment
and setup, lens tuning, camera operation mode, and acquisition board
parameters. If you reach the best possible conditions with your setup but
the image quality still does not meet your needs, try to improve the image
quality using the image processing techniques described in the Improve an
Image
section.
Use imaqLineProfile() to get the pixel distribution along a line in the
image, or use imaqROIProfile() to get the pixel distribution along a
one-dimensional path in the image. By looking at the pixel distribution, you
can determine if the image quality is high enough to provide you with sharp
edges at object boundaries. Also, you can determine if the image is noisy,
and identify the characteristics of the noise.
If the image quality meets your needs, use the pixel distribution information
to determine some parameters of the inspection functions you want to use.
For example, use the information from the line profile to determine the
strength of the edge at the boundary of an object. You can input this
information into
imaqEdgeTool() to find the edges of objects along the
line.
Improve an Image
Using the information you gathered from analyzing your image, you may
want to improve the quality of your image for inspection. You can improve
your image with lookup tables, filters, grayscale morphology, and Fast
Fourier transforms.
Lookup Tables
Apply lookup table (LUT) transformations to highlight image details in
areas containing significant information at the expense of other areas.
A LUT transformation converts input grayscale values in the source image
into other grayscale values in the transformed image. IMAQ Vision
provides four functions that directly or indirectly apply lookup tables to
images:
• imaqMathTransform() —Converts the pixel values of an image by
replacing them with values from a predefined lookup table. IMAQ
Vision has seven predefined lookup tables based on mathematical