Describing superpixels

[Juan Nunez-Iglesias]

Hi Brickle,

Cool problem. =) iirc the return type of these algorithms is an M x N
integer-type numpy array (where the input image is an M x N x 3 numpy
array). Every pixel with the same value belongs in the same superpixel. So,
all pixels with value 1 make up the 1st superpixel, all pixels with value 2
make up the 2nd, and so on until the nth superpixel.

Does that answer your question?

Juan.


On Mon, Apr 22, 2013 at 6:02 PM, Brickle Macho <bricklemacho at gmail.com>wrote:

> Hi,
>
> I am new to python and image processing, which may be my problem, but I
> don't understand how to interpret/use the integer mask indicating segment
> labels output from the SLIC and Quickshift algorithms.
>
> I have a RGB-D image.   Using only RGB I segment the image into
> superpixels using SLIC and Quickshift algorithms provided in scikit-image.
>  I am trying visit each superpixel, calculate some depth features for each
> superpixel.  Specifically I want to calculate the surface normal of the
> superpixel and the average angular difference with the neighbouring
> superpixels.     Eventually I plan to combine the superpixels based on
> these depth features.
>
> Could someone explain the segment_mask format/structure and how I should
> use the mask?
>
> Thanks in advance.
>
> Brickle.
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