1Basics 2------ 3 4Here are basic concepts that might help to understand documentation 5written in this folder: 6 7Convolution 8~~~~~~~~~~~ 9 10The simplest way to look at this is "tweaking the input so that it would 11look like the shape provided". What exact tweaking is applied depends on 12the kernel. 13 14-------------- 15 16Filters, kernels, weights 17~~~~~~~~~~~~~~~~~~~~~~~~~ 18 19Those three words usually mean the same thing, unless context is clear 20about a different usage. Simply put, they are matrices, that are used to 21achieve certain effects on the image. Lets consider a simple one, 3 by 3 22Scharr filter 23 24``ScharrX = [1,0,-1][1,0,-1][1,0,-1]`` 25 26The filter above, when convolved with a single channel image 27(intensity/luminance strength), will produce a gradient in X 28(horizontal) direction. There is filtering that cannot be done with a 29kernel though, and one good example is median filter (mean is the 30arithmetic mean, whereas median will be the center element of a sorted 31array). 32 33-------------- 34 35Derivatives 36~~~~~~~~~~~ 37 38A derivative of an image is a gradient in one of two directions: x 39(horizontal) and y (vertical). To compute a derivative, one can use 40Scharr, Sobel and other gradient filters. 41 42-------------- 43 44Curvature 45~~~~~~~~~ 46 47The word, when used alone, will mean the curvature that would be 48generated if values of an image would be plotted in 3D graph. X and Z 49axises (which form horizontal plane) will correspond to X and Y indices 50of an image, and Y axis will correspond to value at that pixel. By 51little stretch of an imagination, filters (another names are kernels, 52weights) could be considered an image (or any 2D matrix). A mean filter 53would draw a flat plane, whereas Gaussian filter would draw a hill that 54gets sharper depending on it's sigma value. 55