Use simple smoothing in find_cut_slices rather than a gaussian filter

Fixes #233.
On 26/09/2014 18:05, Loïc Estève wrote:
>
> In nilearn/image/image.py:
>
> > @@ -82,6 +82,50 @@ def high_variance_confounds(niimgs, n_confounds=5, percentile=2.,
> >                                             detrend=detrend)
> >
> >
> > +def _fast_smooth_array(arr):
> > +    """Simple smoothing which is less computationally expensive than
> > +    applying a gaussian filter.
> > +
> > +    The filter is [.2, 1, 2] in each direction with a normalisation to
> > +    preserve the scale.
> > +
> > +    Parameters
> > +    ----------
> > +    arr: 3D ndarray
> > +
>
> For completeness, this is what I have done, I think.
>
> Also about the naming of the functions, I am fine adding 3d 
> explicitely to |_smooth_array| or |_fast_smoth_array| if you think it 
> is better. Maybe something like |_smooth_array_three_first_dims|? 
> Better suggestions welcome!
>
> In this case should the 'public' function smooth_img be renamed too?
>
> —
> Reply to this email directly or view it on GitHub 
> <https://github.com/nilearn/nilearn/pull/241/files#r18097493>.
>
The docstrings have to indicate that this acts on the first three axes 
of the image (which is a basic convention in many NiLearn functions), 
and does not act along potential additional axes. This means that I'm 
fine with the current state of the code. I don't think that renaming is 
necessary.