- convolution (wikipedia)
- edge detection with convolution
- image processing fundamentals
- LU decomposition
- graphs as matrices
- 3D projection

**key ideas**

**convolution**combines two functions (or images) by putting one (scaled) at every position of the other.- for image processing, one function is the image, the other is the "kernel", typically much smaller
- gaussian blur is a primary example; the gaussian is the "kernel"
- by using kernels like [-1,0,1] or [1,-2,1] we can take first or 2nd derivatives
- in horizontal, vertical, or diagonal directions
- which can give a gradient (direction and magnitude of pixel change) at each point
- the length of that gradient is a typical "edge detection"
**inverse**of a matrix is typically done by LU decomposition (gaussian elimination)**sparse**matrices are once where most of the entries are zero; there are special storage methods and algorithms for dealing with them, depending on where the zeros are.**rotations**and**projections**are 3D/2D transformations of objects described by (x,y,z) or (x,y) coordinates in space. Both are accomplished with matrix multiplications using specific rotation or projection matrices.