High Quality Content by WIKIPEDIA articles! Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities with complementary motivations from physics and biological vision. It is a formal theory for handling image structures at different scales, by representing an image as a one-parameter family of smoothed images, the scale-space representation, parametrized by the size of the smoothing kernel used for suppressing fine-scale structures. The parameter t in this family is referred to as the scale parameter, with the interpretation that image structures of spatial size smaller than about sqrt{t} have largely been smoothed away in the scale-space level at scale t.