# Basics

Here are basic concepts that might help to understand documentation written in this folder:

## Convolution

The simplest way to look at this is “tweaking the input so that it would look like the shape provided”. What exact tweaking is applied depends on the kernel.

## Filters, kernels, weights

Those three words usually mean the same thing, unless context is clear about a different usage. Simply put, they are matrices, that are used to achieve certain effects on the image. Lets consider a simple one, 3 by 3 Scharr filter

`ScharrX = [1,0,-1][1,0,-1][1,0,-1]`

The filter above, when convolved with a single channel image (intensity/luminance strength), will produce a gradient in X (horizontal) direction. There is filtering that cannot be done with a kernel though, and one good example is median filter (mean is the arithmetic mean, whereas median will be the center element of a sorted array).

## Derivatives

A derivative of an image is a gradient in one of two directions: x (horizontal) and y (vertical). To compute a derivative, one can use Scharr, Sobel and other gradient filters.

## Curvature

The word, when used alone, will mean the curvature that would be generated if values of an image would be plotted in 3D graph. X and Z axises (which form horizontal plane) will correspond to X and Y indices of an image, and Y axis will correspond to value at that pixel. By little stretch of an imagination, filters (another names are kernels, weights) could be considered an image (or any 2D matrix). A mean filter would draw a flat plane, whereas Gaussian filter would draw a hill that gets sharper depending on it’s sigma value.