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- 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.
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