Implementation issues When implementing scale-space smoothing in practice there are a number of different approaches that can be taken in terms of continuous or discrete Gaussian smoothing, implementation in the Fourier domain, in terms of pyramids based on binomial filters that approximate the Gaussian or using recursive filters. More details about this are given in a separate article on scale space implementation. See also Difference of Gaussians Gaussian function mipmapping References Further reading Lindeberg, Tony: Scale-space theory: A basic tool for analysing structures at different scales, in J. of Applied Statistics, 21(2), pp. 224–270, 1994. (longer pdf tutorial on scale-space) Lindeberg, Tony: Scale-space: A framework for handling image structures at multiple scales, Proc. CERN School of Computing, 96(8): 27-38, 1996. Romeny, Bart ter Haar: Introduction to Scale-Space Theory: Multiscale Geometric Image Analysis, Tutorial VBC ’96, Hamburg, Germany, Fourth International Conference on Visualization in Biomedical Computing. Florack, Luc, Romeny, Bart ter Haar, Viergever, Max, & Koenderink, Jan: Linear scale space, Journal of Mathematical Imaging and Vision volume 4: 325–351, 1994. Lindeberg, Tony, "Principles for automatic scale selection", In: B. Jähne (et al., eds.), Handbook on Computer Vision and Applications, volume 2, pp 239—274, Academic Press, Boston, USA, 1999. (tutorial on approaches to automatic scale selection) Lindeberg, Tony: "Scale-space theory" In: Encyclopedia of Mathematics, (Michiel Hazewinkel, ed) Kluwer, 1997. Web archive backup: Lecture on scale-space at the University of Massachusetts (pdf) External links Powers of ten interactive Java tutorial at Molecular Expressions website Peak detection in 1D data using a scale-space approach BSD-licensed MATLAB code Image processing Computer vision