Announcement: scikit-image 0.12
===============================

The scikit-image team is very pleased to announce the release of version
0.12 of scikit-image.

scikit-image is an image processing toolbox for Python and SciPy, that
includes algorithms for segmentation, geometric transformations, color
space manipulation, analysis, filtering, morphology, feature detection,
and more, for 2-D and 3-D (sometimes n-D) images.

For more information, examples, and documentation, please visit our
website:

http://scikit-image.org

and our example gallery

http://scikit-image.org/docs/dev/auto_examples/

This release includes new features such as image inpainting, seam
carving, image comparison metrics, and a parallelization framework based
on dask. The release has also improved the support of 3-D images, with
3-D skeletonization, 3-D phantom data. and partial support of
measure.regionprops for 3-D images. Also note that the handling of
background pixels by measure.label, a function labeling connected
components, has changed to be consistent with scipy.ndimage.label.

For this release, we merged over 200 pull requests from 64 contributors,
with bug fixes, cleanups, improved documentation and new features.
Release notes are available on
http://scikit-image.org/docs/0.12.x/release_notes_and_installation.html#release-notes
and include a more detailed list of changes, and the complete list of
contributors to this release.

The release can be downloaded on PyPi
https://pypi.python.org/pypi/scikit-image or directly installed using pip

pip install --upgrade scikit-image

Please let us know any issues you might have on the issue tracker
https://github.com/scikit-image/scikit-image/issues

Many thanks to all the developers who made this release possible, and a
warm welcome to our new contributors.

Happy image processing!
The scikit-image team