While it's possible we might never catch Fiji in terms of "feature completeness" (which is almost impossible to define anyway, and I question the validity of the comparison or the reason this should be a goal; we ditched attempting to chase down all the Matlab image processing toolbox functions a year or two back because it wasn't productive), I think we have major advantages which need to be baked into this paragraph.

Fiji/ImageJ limit users to their own sandbox for image processing, because [their data model](http://fiji.sc/Introduction_into_Developing_Plugins#The_hierarchy_of_the_classes_representing_an_image) works only in that sandbox. Even though it's ostensibly open source, it doesn't play nice with other languages. Yes, they have a lot of plugins. However, many to most of those are not generally useful. They're also not very difficult to port. In contrast, the NumPy `ndarray` is incredibly powerful, keeping our users as close as possible to their data, while allowing immediate use throughout the scientific Python ecosystem. Need to do image processing, followed by machine learning? That's one IPython notebook here; using Fiji you'd have to do your bit, save it out, reload it in Weka or similar, and use it there. Much, much more room for error - both user and technical! Also, because we keep our users so much closer to their data, the bar for developing PRs and new features is far lower in scikit-image. I could go on, but this is already pretty long.

I'd aggressively revise this paragraph to focus on our advantages and Fiji's (major, imo) flaws.