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Convolution Neural Network

  1. Nov 21, 2016 #1
    Hi I am using a convolution neural network (with inversion) to simulate images with the same "texture" as the input image, using a random image to start with. The activations of the CNN are first learnt with an example or source image. A cost function then minimizes the difference between the simulated features and the source features. The new simulated image has the same texture profile as the source image. The method is described here.

    http://bethgelab.org/deeptextures/

    My question is, is there a way to somehow tune the network to output a simulated image that has a greater texture parameter, such as skew or energy, or perhaps a way to incorporate these parametrized textures into the cost function.

    I'm new to CNN's so sorry if this is trivial

    Thanks

    Emma
     
  2. jcsd
  3. Nov 26, 2016 #2
    Thanks for the thread! This is an automated courtesy bump. Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post? The more details the better.
     
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