Mercurial > ift6266
changeset 443:89a49dae6cf3
merge
author | Xavier Glorot <glorotxa@iro.umontreal.ca> |
---|---|
date | Mon, 03 May 2010 18:38:58 -0400 |
parents | d5b2b6397a5a (diff) 1272dc84a30c (current diff) |
children | 18841eeb433f |
files | writeup/techreport.tex |
diffstat | 3 files changed, 11 insertions(+), 2 deletions(-) [+] |
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--- a/data_generation/transformations/BruitGauss.py Mon May 03 13:55:03 2010 -0400 +++ b/data_generation/transformations/BruitGauss.py Mon May 03 18:38:58 2010 -0400 @@ -51,7 +51,7 @@ self.nb_chng=3+int(numpy.random.rand()*self.nb_chngmax*complexity) self.sigma_gauss=2.0 + numpy.random.rand()*self.sigmamax*complexity self.grandeur=12+int(numpy.random.rand()*self.grandeurmax*complexity) - #creation du noyau gaussien + #creation du noyau gaussien self.gauss=numpy.zeros((self.grandeur,self.grandeur)) x0 = y0 = self.grandeur/2.0 for i in xrange(self.grandeur):
--- a/data_generation/transformations/gimp_script.py Mon May 03 13:55:03 2010 -0400 +++ b/data_generation/transformations/gimp_script.py Mon May 03 18:38:58 2010 -0400 @@ -52,7 +52,7 @@ setpix(image) if self.mblur_length and self.blur_bool: pdb.plug_in_mblur(img, layer1, 0, self.mblur_length, self.mblur_angle, 0, 0) - if self.pinch: + if self.pinch: pdb.plug_in_whirl_pinch(img, layer1, 0.0, self.pinch, 1.0) image = getpix()
--- a/writeup/techreport.tex Mon May 03 13:55:03 2010 -0400 +++ b/writeup/techreport.tex Mon May 03 18:38:58 2010 -0400 @@ -165,6 +165,15 @@ This filter has a probability of not being applied, at all, of 60\%. +\subsection{Pixel permutation} + +This filter permuts neighbouring pixels. It selects first $\frac{complexity}{3}$ pixels randomly in the image. Each of them are then sequentially +exchanged to one other pixel in its $V4$ neighbourhood. Number of exchanges to the left, right, top, bottom are equal or does not differ from more than 1 +if the number of selected pixel is not a multiple of 4. + +It has has a probability of not being applied, at all, of 80\%. + + \subsection{Distorsion gauss} This filter simply adds, to each pixel of the image independently, a gaussian noise of mean $0$ and standard deviation $\frac{complexity}{10}$.