Scale Space Implementation

Scale Space Implementation

The linear scale-space representation of an N-dimensional continuous signal is obtained by convolving with an N-dimensional Gaussian kernel

L(x_1, x_2, dots, x_N, t) =
int_{u_1=-infty}^{infty} int_{u_2=-infty}^{infty} dots int_{u_N=-infty}^{infty}
&f_C(x_1-u_1, x_2-u_2, dots, x_N-u_N, t)\
, cdot , &g_N(u_1, u_2, dots, u_N, t) , du_1 , du_2 dots du_N

However, for implementation, this definition is impractical, since it is continuous. When applying the scale space concept to a discrete signal, different approaches can be taken. This article is a brief summary of some of the most frequently used methods.

Read more about Scale Space Implementation:  Separability, The Sampled Gaussian Kernel, The Discrete Gaussian Kernel, Recursive Filters, Finite-impulse-response (FIR) Smoothers, Real-time Implementation Within Pyramids and Discrete Approximation of Scale-normalized Derivatives, Other Multi-scale Approaches, See Also

Other articles related to "scale space implementation":

Scale Space Implementation - See Also
... scale space pyramid (image processing) multi-scale approaches Gaussian filter. ...

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