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@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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