Describing superpixels

[Brickle Macho]

Hi Juan,

Thanks.  Makes more sense now when I inspect the mask.

Michael.
--




On 22/04/13 4:17 PM, Juan Nunez-Iglesias wrote:
> 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 
> <mailto: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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