National Instruments IMAQ Vision for Measurement Studio - Manual (Page 23)

Chapter 2 Getting Measurement-Ready Images
IMAQ Vision for LabWindows/CVI User Manual 2-10 ni.com
transformations. For more information about these lookup tables, see
Chapter 5, Image Processing, in the IMAQ Vision Concepts Manual.
• imaqLookup() —Converts the pixel values of an image by replacing
them with values from a user-defined lookup table.
• imaqEqualize() —Distributes the grayscale values evenly within a
given grayscale range. Use IMAQ Equalize to increase the contrast in
images containing few grayscale values.
• imaqInverse() —Inverts the pixel intensities of an image to
compute the negative of the image. For example, use imaqInverse()
before applying an automatic threshold to your image if the
background pixels are brighter than the object pixels.
Filters
Filter your image when you need to improve the sharpness of transitions in
the image or increase the overall signal-to-noise ratio of the image. You can
choose either a lowpass or highpass filter depending on your needs.
Lowpass filters remove insignificant details by smoothing the image,
removing sharp details, and smoothing the edges between the objects
and the background. You can use
imaqLowpass() or define your own
lowpass filter with imaqConvolve() or imaqNthOrderFilter() .
Highpass filters emphasize details, such as edges, object boundaries, or
cracks. These details represent sharp transitions in intensity value. You can
define your own highpass filter with
imaqConvolve() or
imaqNthOrderFilter() , or you can use a predefined highpass filter with
imaqEdgeFilter() or imaqCannyEdgeFilter() . The
imaqEdgeFilter() function allows you to find edges in an image using
predefined edge detection kernels, such as the Sobel, Prewitt, and Roberts
kernels.
Convolution Filter
The imaqConvolve() function allows you to use a predefined set of
lowpass and highpass filters. Each filter is defined by a kernel of
coefficients. Use
imaqGetKernel() to retrieve predefined kernels. If the
predefined kernels do not meet your needs, define your own custom filter
using a 2D array of floating point numbers.