![]() ![]() # We choose the hottest and the coldest pixels as markers. shape ) data = rescale_intensity ( data, in_range = ( - sigma, 1 + sigma ), out_range = ( - 1, 1 )) # The range of the binary image spans over (-1, 1). ![]() normal ( loc = 0, scale = sigma, size = data. img_as_float ( binary_blobs ( length = 128, rng = 1 )) sigma = 0.35 data += rng. default_rng () # Generate noisy synthetic data data = skimage. Import numpy as np import matplotlib.pyplot as plt from gmentation import random_walker from skimage.data import binary_blobs from skimage.exposure import rescale_intensity import skimage rng = np. Random walks for image segmentation, Leo Grady, IEEE Trans. Values, and use the random walker for the segmentation. Markers of the two phases from the extreme tails of the histogram of gray Noisy to perform the segmentation from the histogram only. In this example, two phases are clearly visible, but the data are too Probability to be reached first during this diffusion process. Pixel is attributed to the label of the known marker that has the highest So that diffusion is difficult across high gradients. The localĭiffusivity coefficient is greater if neighboring pixels have similar values, An anisotropic diffusionĮquation is solved with tracers initiated at the markers’ position. The random walker algorithm determines the segmentation of an image fromĪ set of markers labeling several phases (2 or more). To download the full example code or to run this example in your browser via Binder Random walker segmentation #
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