Papers on image processing examine novel ways of using and computing kernels. Let’s discuss!

**1** Non-traditional ways of calculating the convolution, whether to output the same result more efficiently or to output a much more elegant result.

**2** An adaptive kernel that changes size or shape based on the input, other kernels or masks.

*Note: I am slow to understand math and code and I am not the only one. Remember to make the conversation accessible to our visitors. Thanks!*

To start the conversation, here are 2 topics

**A** I am reminded of the exchange between @Reptorian and @garagecoder (Reptorian G'MIC Filters) about a variable kernel size box filter based on a mask. I wonder **a** if there are new ideas or development in this area, and **b** what the applications have been.

**B** I started asking about calculating distances (along specific paths) in kernels in G'MIC exercises with the goal of detecting features such as edges. In retrospect, *movement cost* (common in games like The Battle for Wesnoth, which I highly recommend BTW, and GIS) might be an apter description than *distance*.