Describing superpixels
Brickle Macho
bricklemacho at gmail.com
Mon Apr 22 04:41:22 EDT 2013
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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