[scikit-image] =?utf-8?Q?=E5=9B=9E=E5=A4=8D=EF=BC=9ARe=3A__?=Code for the short course 'Image Processing using Python', @ IAMG2017

Juan Nunez-Iglesias jni.soma at gmail.com
Thu Sep 21 02:30:19 EDT 2017


I just noticed that the tutorials repo has no license. Oops! I suggest CC-BY. Technically we would need to ask all the contributors for their permission to put any new license on it.

In the meantime, though, as the main author of that specific document, I grant Yan permission to use its contents. (Stéfan might technically need to step in also, but I think it safe to assume he’ll agree. =) A mention of the source in your book would be appreciated, Yan. =)

Juan.

On 21 Sep 2017, 4:25 PM +1000, imagepy at sina.com, wrote:
> Hi Stefan:
>   "segmentation lesson from the skimage_tutorial repo" did you means this?(https://github.com/scikit-image/skimage-tutorials/blob/master/lectures/4_segmentation.ipynb), I am writing a simple imageprocessing book, It may be useful for me.
>
> Best regards
> YXDragon
> ----- 原始邮件 -----
> 发件人:Stefan van der Walt <stefanv at berkeley.edu>
> 收件人:Alexandre Fioravante de Siqueira <siqueiraaf at gmail.com>, scikit-image at python.org
> 主题:Re: [scikit-image] Code for the short course 'Image Processing using Python', @ IAMG2017
> 日期:2017年09月21日 05点16分
>
> Hi Alex
>
> On Tue, Sep 19, 2017, at 21:25, Alexandre Fioravante de Siqueira wrote:
> > as I said in an old previous e-mail, I presented a short course using scikit-image for processing microscopy images at IAMG2017 (short course 8 at <http://iamg2017.com/courses/>).
> > An alpha version of the codes is now available; please feel free to check it at <https://github.com/alexandrejaguar/lectures/tree/master/2017/iamg2017>.
>
> Thanks for sharing these!
>
> > Since it is still very raw, I'd be glad to receive your comments, suggestions and ideas!
>
> I'd say 1) stick to gray, viridis, and other reliable colormaps and 2) plot the found features so that they are easier to observe (currently just pixels inside a big image).
>
> You may also want to take a look at the segmentation lesson from the skimage_tutorial repo, which has a few sketches to clarify the workings of watershed.
>
> Best regards
> Stéfan
>
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