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  1. Prepare
  2. Artificial Intelligence
  3. Digital Image Analysis
  4. Morphological Operations: Closing
  5. Discussions

Morphological Operations: Closing

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7 Discussions

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  • Tooncinator
     + 0 comments

    There is no "middle pixel". The number of columns in the image is even, so there are two "middle pixels". Also, what's with all the weird, esoteric terminology with no definitions given? If you're going to use incredibly specific terminology to the field at hand, at least provide a link to definitions of your terms if not placing the definitions directly in the problem description. ie, what does "structuring element" mean, and what does "applying" it mean, etc, etc..

  • envatosan001
     + 0 comments

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  • rahulkumeriya
     + 1 comment
    import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imshow
from skimage.draw import disk
from skimage.morphology import (erosion, dilation, closing, opening,
                                area_closing, area_opening)
from skimage.color import rgb2gray

a = np.array([
    [0,0,0,0,0,0,0,0,0,0],
    [0,1,1,1,1,1,1,1,0,0],
    [0,0,0,0,1,1,1,1,0,0],
    [0,0,0,0,1,1,1,1,0,0],
    [0,0,0,1,1,1,1,1,0,0],
    [0,0,0,0,1,1,1,1,0,0],
    [0,0,0,1,1,0,0,0,0,0],
    [0,0,0,0,0,0,0,0,0,0],
    [0,0,0,0,0,0,0,0,0,0]
])

s = np.array([
        [1,1,1],
        [1,1,1],
        [1,1,1]])

unique, freq = np.unique(dilation(a,s), return_counts=True)
print("dilation", freq[1])

unique, freq = np.unique(erosion(a,s), return_counts=True)
print("erosion", freq[1])


unique, freq = np.unique(closing(a,s), return_counts=True)
print("closing", freq[1])

# plt.imshow(dilation(a,s), cmap='gray')
# plt.show()

  • andrea_massari_1
     + 0 comments

    Minor typo: "its ", not "its' " :)

  • potatoes
     + 2 comments

    I think the question should mention that the assumption is that the boundary pixels should be padded with 0's not 1's :)