Scale space theory has generated quite a number of useful
algorithms in computer vision and image processing.
Scale space appears to be a useful tool for low-level image analysis,
particularly feature extraction and segmentation. Its affects on
diminishing noise and grouping together similar, proximate structures
has been well documented. However, linear scale space
(which in the most general case can be shown to be blurring with a
Gaussian kernel)
This work studies nonuniform or anistropic scale spaces that are generated
by introducing a nonlinear, space varying conductance term into the
diffusion equation, which otherwise gives rise to Gaussian blurring:
Diffusion equation 
Nonuniform diffusion
equation
Multiscale edge-affected diffusion: A slice from MRI data (top
left) gives rise to a nonuniform scale space that preserves structure.
Geometry-limited diffusion: A hand radiograph is processed with a
five-dimensional feature space consisting of first- and second-order
tensors.
Texture segmentation: A 40 dimensional feature space captures
texture patches that differ according to phase, orientation, and size.