It sounds like connected components analysis is more appropriate. You can
clean up your blobs using morphology techniques, and then use connected
components analysis to identify each blob.
The following blog post shows you to do connected components using scipy:
http://blancosilva.wordpress.com/2010/12/15/image-processing-with-numpy-scipy-and-matplotlibs-in-sage/
Martin
On Thursday, 18 April 2013, Calvin Morrison wrote:
>
>
>
> On 18 April 2013 10:41, David Reed <[email protected]> wrote:
>
>> Hi, I'm doing some realtime computer vision and have found the sklearn
>> implementation of KMeans is performing better at clustering my blobs
>> between frames.
>>
>> So I want to use sklearn, but unfortunately I don't know the number of
>> blobs, K, and was wondering if sklearn can tell me the fit of the model. I
>> was looking through the documentation but I wasn't sure if what I was
>> looking for was there.
>>
>
> You don't know the number of blobs? opencv has an awesome blob library
> that can do this pretty easily
>
>> Also, if any one has any experience clustering blobs in CV and thinks
>> this is the wrong direction to go in, please let me know.
>>
>
> I have experience using the cv blob lib, but not clustering them. you can
> checkout my code here:
>
>
> https://github.com/mutantturkey/FlyTracking/blob/master/fly-tools/filter/main.cpp
>
> This is a pretty simple usage of the cv blob lib but it might help. All it
> does is grab the two largest blobs and saves them.
>
>>
>>
> Thanks a lot for the help.
>>
>> Dave
>>
>>
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