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Apply 

 (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 eplacing them with values from a predefined lookup table. 
IMAQ Vision has seven predefined lookup tables based on 
mathematical transformations. For more information about these 
lookup tables, refer to Chapter 5, Image Processing, of 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 

imaqEqualize()

 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.

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.

 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()

.

 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.