I am now studying image processing in my spare time. My understanding of convolution is about 'response to a specific filter':
When we have a raw image, or raw signal; and a filter, aka kernel; we apply a 'moving dot product' between the image and the filter.
The filter/kernel represents our domain-knowledge about the raw signal, which is usually some kind of features or patterns. For image processing, such features are often edge, corner, SIFT, etc.
When we apply the "moving dot product", we practically divide the image into "patches" and ask each patch of the image: "Do you look like the feature I am looking for?", high response value means "yes, I am the feature (edge) you are looking for"; while low response value means "no, I am not"
My first question is : is my understanding correct?
My second question is: If my understanding is correct, then we should be able to design much more fancy kernels which represents some very specific features: such as eye (dark in the middle, surrounded by white areas), or nose (triangular shape). But I haven't found a lot of literatures in the area of defining your own kernel.
Can anyone provide any insight here?