[Scilab-users] data augmentation with scilab for neural networks

[P M]

Dear Scilabers,

probably not the correct forum to ask this question, but i do not know
better.

Dealing with neural networks and image segmentation I write some Scilab
code for data augmentation.
This is to increase my training data set.  ( = more images)

Data augmentation (for now) is done by:

- image rotation
- image horizontal shifting

Now a basic question:

- Does one apply the data augmentation only for the input images and keep
the label images?
Or
- does one also rotate/shear the label images?

A "label image" in this context is a binary image (black/white).

white --> corresponds to pixels in the input image, which are of interest
black --> corresponds to pixels in the input image, which are of no
interest.

Any help is appreciated.

Thanks,
Philipp
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