load_sift skimage.io.load_sift(f)

dilation skimage.morphology.dilation(image, selem=None, *args, **kwargs)

threshold_adaptive skimage.filters.threshold_adaptive(image, block_size, method='gaussian', offset=0, mode='reflect', param=None)

perimeter skimage.measure.perimeter(image, neighbourhood=4)

Module: restoration Image restoration module.

CENSURE class skimage.feature.CENSURE(min_scale=1, max_scale=7, mode='DoB', non_max_threshold=0.15, line_threshold=10)

MultiImage class skimage.io.MultiImage(filename, conserve_memory=True, dtype=None, **imread_kwargs)

Picture class skimage.novice.Picture(path=None, array=None, xy_array=None)

A crash course on NumPy for images Images manipulated by scikit-image are simply NumPy arrays. Hence, a large fraction of operations on images will just consist in using NumPy:

rank_order skimage.filters.rank_order(image)