Describing superpixels

[Brickle Macho]

Thanks everyone for the advice and tips.  I am getting closer to a 
solution.   The  (obvious) plan is for each edge find neighbouring 
regions, use segment_mask to extract indices into the depth map and 
calculate depth features (average depth, surface normals etc) and then 
combine segments based on these feature(s).   Some of the advice in the 
replies, suggest using a graph package and having some way to directly 
access the pixels of each super-pixel with some interesting ideas provided.

Being a little naive I was hoping to have a simple set of edges to 
traverse.   I found a Peekaboo blog entry: 
http://peekaboo-vision.blogspot.com.au/2011/08/region-connectivity-graphs-in-python.html

Which takes a segment mask/array  and creates a set of vertices and 
edges representing the region connectivity graph.  This looks like it 
might be sufficient.   Am I being too naive?  Should I just learn/use a 
graph package?

Also, any advice/gotchas on how to join/combine segments?    There seems 
to be a few join/relabel functions in the library.

Thanks again,

Michael.
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On 22/04/13 8:32 PM, St�fan van der Walt wrote:
> On Mon, Apr 22, 2013 at 11:55 AM, Juan Nunez-Iglesias
> <jni.soma at gmail.com> wrote:
>> It's very convenient to have direct access back to the pixels/voxels making
>> up each superpixel. Therefore, I stored on each node an array containing all
>> the linear indices into the `ravel`ed version of the image. This is pretty
>> expensive, though, so if could do it over, I would store the index of a
>> single pixel belonging to that superpixel and pair it with a flood fill
>> algorithm, so that a function `g.get_pixel_indices(superpixel_id)` would
>> transparently return the pixel list, but in O(superpixel_size) time while
>> only using up a singe int of space.
> Thanks for sharing that good advice. Juan.  There is also an
> intermediary storage format, one that I used before with some success.
>   Think of a 2-D example: you store the start and end indices of each
> column that comprises the object, paired with an index into the rows
> (very similar to CSR).  Perhaps it will be a bit painful to extend to
> N-d, but it is doable.
>
> Another option is to store a graph in an array.  This goes along very
> well with the implementation of ``graph.label``.  At each position in
> the array, you store the index of the previous pixel to which it is
> connected.  For any super-pixel, you can then store only the last
> pixel, and traverse indices backward to get the entire super-pixel.
> Con: 2 x storage.
>
>> Related note: does skimage have an nd implementation of flood fill?
> I think one of the recent PRs made an attempt at a 2D version called
> ``connected_component``, but otherwise no.
>
> St�fan
>