# Sampling Method

The ViDi tools do not sample the input images uniformly (although the image sampling does cover the entire image extent). During training, the tools use a special technique to selectively sample at a higher rate those parts of the images that are determined to be more likely to contribute additional information to the network.

Because the network training is performed using both information within the sample region and contextual information from around the sample region, the tool can be strongly influenced by samples collected at the edge of an image. If you are using a view within an image, then the context information for samples collected at the edge of the view will use pixels from outside of the view for context data. If the sample is at the edge of the image itself, the tool will generate synthetic pixels for use as context. You can control the specific method used for this through the use of masks, borders, and sample channels..

The tools also allow you to provide masks for use during sampling. This allows you to explicitly exclude parts of the image from training, although even masked regions are considered as context. (You can use this property as an alternative method for handling image boundaries: By masking the border of an image, you can prevent the tool from collecting image samples that require the generation of synthetic pixels for context.)

Finally, if you are using color images (or any image with multiple planes or channels), you can explicitly specify which channels are sampled. Using multiple channels has a minor impact on training and processing time, but it can allow the tools to work more accurately in cases where color provides important information in the image.