LBP2 User Guide Document No: 50306-001 Rev G 3/12/2020 Page 82
5.12 Convolution
Convolution algorithms in LBP2 may take on a number of forms. In the broadest
sense, convolution refers to a general-purpose algorithm that can be used in
performing a variety of area process transformations. One such general-purpose
algorithm will be described here.
For the purpose of this description, the best way to understand a convolution is to
think of it is a weighted summation process. Each pixel in an image becomes the
center element in a neighborhood of pixels. A similarly dimensioned convolution kernel
multiplies each pixel in the neighborhood. The sum of these products is then used to
replace the center pixel.
Each element of the convolution kernel is a weighting factor called a convolution
coefficient. The size and arrangement of the convolution coefficients in a convolution
kernel determine the type of area transform that will be applied to the image data.
The figure below shows a 3x3 neighborhood and convolution kernel.
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