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.