title: Multi-scale approaches
text: # Multi-scale approaches
  
  The scale space representation of a signal obtained by Gaussian smoothing satisfies a number of special properties, scale-space axioms, which make it into a special form of multi-scale representation. There are, however, also other types of "multi-scale approaches" in the areas of computer vision, image processing and signal processing, in particular the notion of wavelets. The purpose of this article is to describe a few of these approaches:
  
  ## Scale-space theory for one-dimensional signals
  
  For one-dimensional signals, there exists quite a well-developed theory for continuous and discrete kernels that guarantee that new local extrema or zero-crossings cannot be created by a convolution operation. For continuous signals, it holds that all scale-space kernels can be decomposed into the following sets of primitive smoothing kernels:
  
  * the Gaussian kernel :$g(x,t)={\frac {1}{\sqrt {2\pi t}}}\exp({-x^{2}/2t})$ where $t>0$,
  * truncated exponential kernels (filters with one real pole in the s-plane):
  
         $h(x)=\exp({-ax})$ if $x\geq 0$ and 0 otherwise where $a>0$
         $h(x)=\exp({bx})$ if $x\leq 0$ and 0 otherwise where $b>0$,
  * translations,
  * rescalings.
  
  For discrete signals, we can, up to trivial translations and rescalings, decompose any discrete scale-space kernel into the following primitive operations:
  
  * the discrete Gaussian kernel
  
         $T(n,t)=I_{n}(\alpha t)$ where $\alpha ,t>0$ where $I_{n}$ are the modified Bessel functions of integer order,
  * generalized binomial kernels corresponding to linear smoothing of the form
  
      $f_{out}(x)=pf_{in}(x)+qf_{in}(x-1)$ where $p,q>0$
      $f_{out}(x)=pf_{in}(x)+qf_{in}(x+1)$ where $p,q>0$,
  * first-order recursive filters corresponding to linear smoothing of the form
  
      $f_{out}(x)=f_{in}(x)+\alpha f_{out}(x-1)$ where $\alpha >0$
      $f_{out}(x)=f_{in}(x)+\beta f_{out}(x+1)$ where $\beta >0$,
  * the one-sided Poisson kernel
  
      $p(n,t)=e^{-t}{\frac {t^{n}}{n!}}$ for $n\geq 0$ where $t\geq 0$
      $p(n,t)=e^{-t}{\frac {t^{-n}}{(-n)!}}$ for $n\leq 0$ where $t\geq 0$.
  
  From this classification, it is apparent that we require a continuous semi-group structure, there are only three classes of scale-space kernels with a continuous scale parameter; the Gaussian kernel which forms the scale-space of continuous signals, the discrete Gaussian kernel which forms the scale-space of discrete signals and the time-causal Poisson kernel that forms a temporal scale-space over discrete time. If we on the other hand sacrifice the continuous semi-group structure, there are more options:
  
  For discrete signals, the use of generalized binomial kernels provides a formal basis for defining the smoothing operation in a pyramid. For temporal data, the one-sided truncated exponential kernels and the first-order recursive filters provide a way to define time-causal scale-spaces that allow for efficient numerical implementation and respect causality over time without access to the future. The first-order recursive filters also provide a framework for defining recursive approximations to the Gaussian kernel that in a weaker sense preserve some of the scale-space properties.
  
  