Hi Yuta, beta has to take a positive value. In the algorithm, the weight on a graph edge is given by exp(- beta * diff) where diff is the absolute value of pixels differences on both sides of the edge. Furthermore, the value of beta you give is normalized by ten times the standard deviation of the image, so that you don't have to worry about the image range (I know this sounds a bit weird, but that's how it's coded. I might even be responsible for this hack :-). Therefore, if you put a large value of beta there will be a very small weight on edges for which pixels have different values, and diffusion will be difficult. On the other hand, for small values diffusion will be easy and regions will be "flooded" for markers, no matter the gradients. A larger value of beta means that boundaries are more likely to lie on pixels with a strong gradient. I would advise that you start with a small value of beta (1 for example) and look at the result. If you feel like boudaries are "leaky" it means that diffusion is too fast and you should increase beta. Hope this helps Emma 2015-04-04 14:58 GMT+02:00 Juan Nunez-Iglesias <jni.soma@gmail.com>:
Hi Yuta,
Sorry, this slipped through the cracks. I haven't used random walker segmentation so I can't give you advice here... You might want to read the original publication [1], or, more practically, try out different betas on a logarithmic scale.
Juan.
[1] http://webdocs.cs.ualberta.ca/~nray1/CMPUT615/MRF/grady2006random.pdf
On Sat, Apr 4, 2015 at 12:56 AM, Yuta Sato <yutaxsato@gmail.com> wrote:
Dear skimage developers: I would really appreciate to hear the answer on my question if it does worth.
Thanks
On Thu, Mar 12, 2015 at 4:04 PM, Yuta Sato <yutaxsato@gmail.com> wrote:
In the following skimage.segmentation.random_walker algorithm: What is the range of 'beta' values that can be supplied? I am working with a single band 8bit unsigned image.
Is it 0 to 255?
skimage.segmentation.random_walker(data, labels, beta=130, mode='bf', tol=0.001, copy=True,multichannel=False, return_full_prob=False, spacing=None)
beta : float [Penalization coefficient for the random walker motion (the greater beta, the more difficult the diffusion)]
Thanks for your support.
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