Range of beta values in segmentation algorithm?

Yuta Sato yutaxsato at gmail.com
Sat Apr 4 09:47:25 EDT 2015


Thanks Emmanuelle Gouillart for describing me all the hack!

On Sat, Apr 4, 2015 at 10:22 PM, Emmanuelle Gouillart <
emmanuelle.gouillart at normalesup.org> wrote:

> 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 at 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 at 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 at 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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