🚀 go-pugleaf

A couple of weeks ago, I posted an article describing the Canny edge
operator. A number of people have e-mailed me regarding the Smoothing
stage:

ME> 1. Smooth the image with a Gaussian kernel. This has the form:
ME>    G(r) = 1/(2*pi*sigma*sigma) * exp(-r*r/(2*sigma)) for the 2D case,
ME>    where sigma is the smoothing factor. You can do this in two 1D
ME>    operations with G(x) = 1/(sigma*sqrt(2*pi)) * exp(-x*x/(2*sigma))
ME>    -- first smooth horizontally, then smooth the result vertically.

There is of course a mistake here. The Gaussian functions are actually:

  G(r) = 1/(2*pi*sigma*sigma) * exp(-r*r/(2*sigma*sigma))
                                                 ^^^^^^
  G(x) = 1/(sigma*sqrt(2*pi)) * exp(-x*x/(2*sigma*sigma))
                                                 ^^^^^^
Sorry to anyone I lead astray with my previous posting.

Simon

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Simon A.J. Winder             Computer Vision Research
 sajw@uk.ac.bath.maths    University of Bath Computing Group
 Tel: +44 (0)225 826183   /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\