Sigma LBA-300, LBA-710, LBA-714PC, LBA-712, LBA-708, LBA-500PC, LBA-700, LBA-400 manual Convolution

6.27 Convolution

Convolution algorithms in the LBA-PC may take on a number of forms, some of which might not fit the exact description that is to follow. 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.

Figure 58

The tables below give the convolution coefficients (K values) for some of the included low-pass spatial filters.

Operator’s Manual 146 LBA-PC