Hi Stefanie, Sorry, these responses should all be CCd to the list, so that others can benefit from the discussion — my bad for dropping that thread. Could you please:
- provide an example segmentation where skimage is doing worse than Fiji - provide the script and parameter settings for both Then we can help troubleshoot. I don’t know what you mean by the results “were very strange”, for example, so it’s hard to diagnose the problem. =) Starting to merge directly from pixels, as the Fiji plugin does, is expensive and can be error prone. SLIC is a fast, initial pixel merging step, from which we can merge regions according to various criteria. With the right parameters, SLIC + RAG mean color agglomeration should give quite similar results to Fiji’s approach… Juan. On 15 November 2016 at 2:39:45 am, Stefanie Lück (lue...@gmail.com) wrote: Hi Juan, thank you for your reply! I have seen and tested the RAG examples but I did not understand the SLIC step and the results were very strange... Is there any advantage? I am using SLIC anyway at the moment but the statistical region merging of ImageJ gives me better results. Thanks Stefanie 2016-11-14 13:44 GMT+01:00 Juan Nunez-Iglesias <jni.s...@gmail.com>: > Hi Stefanie! > > Have a look at skimage.future.graph! There are some relevant examples in > the gallery, too: > > http://scikit-image.org/docs/dev/auto_examples/#segmentation-of-objects > > The future.graph API is still experimental (that’s why it’s in “future”), > so we really appreciate any feedback you have about it! > > Juan. > > > > On 14 November 2016 at 8:21:12 pm, Stefanie Lück (lue...@gmail.com) wrote: > > Hi! > > I am looking for a statistical region merging segmentation. Is there > anything like this in skimage? > > Thanks in advance, > Stefanie > _______________________________________________ > scikit-image mailing list > scikit-image@python.org > https://mail.python.org/mailman/listinfo/scikit-image > >
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