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#rele... 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 Happy image processing! The scikit-image team