[wiki:libpipi << back to libpipi]

= Gaussian filtering =

Current code:
 * [/browser/libpipi/trunk/pipi/filter/blur.c]
 * [/browser/libpipi/trunk/pipi/filter/convolution_template.h]
 * [/browser/libpipi/trunk/pipi/filter/convolution.c]

The usual way to create a Gaussian kernel is to evaluate a Gaussian function at the center of each cell:

 k[i][j] = exp(-(i²+j²)/2σ)

This usually works well, except when the kernel is thin (σ < 1). It gets worse when using our generalised kernel:

 k[i][j] = exp(-((i×cosθ-j×sinθ-dx)²+(j×cosθ+i×sinθ-dy)²)/2σ)

{{{
#!html
<p><span style="background: red; color: yellow; padding: 0 3px; font-weight: bold;">TODO</span> interpolate the Gaussian integral on more points, eg. 5×5 (currently libpipi does it at the central point and at 8 additional points in the neighbourhood).
}}}

= Median filtering =

Current code:
 * [/browser/libpipi/trunk/pipi/filter/median.c]

There are several ways to optimise a median filter.

Random links for now:
 * [http://coding.derkeiler.com/Archive/General/comp.programming/2004-10/0289.html Sliding median filter]
 * [http://iris.usc.edu/Vision-Notes/bibliography/twod268.html A shitload of papers about median filtering and its derivatives]

= Dilate / erode =

Current code:
 * [/browser/libpipi/trunk/pipi/filter/dilate.c]

Erosion and dilation already work, but only for nearest pixels.

{{{
#!html
<p><span style="background: red; color: yellow; padding: 0 3px; font-weight: bold;">TODO</span> each pixel couple test is done twice. There seems to be an opportunity for improvement there.
}}}

{{{
#!html
<p><span style="background: red; color: yellow; padding: 0 3px; font-weight: bold;">TODO</span> implement "dilate by X" (Manhattan distance).
}}}

{{{
#!html
<p><span style="background: red; color: yellow; padding: 0 3px; font-weight: bold;">TODO</span> implement "dilate by X" (Euclidian distance).
}}}

=== References ===

 * [http://ostermiller.org/dilate_and_erode.html Efficiently Implementing Dilate and Erode Image Functions]