Reading this result: Opening = erosion then dilation. The erosion deletes anything thinner than the element (149 pixels: specks, the 1px filament, the 2px bridge) and the dilation restores the size of what survived — so small objects disappear while the big blobs keep their footprint.
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How it works
Morphological operations probe a binary image with a small structuring element. Erosion (A ⊖ B) shrinks foreground regions and deletes anything thinner than the element — noise specks, hairline bridges. Dilation (A ⊕ B) grows regions, filling small holes and gaps. Opening (A ∘ B) is an erosion followed by a dilation, so it removes small objects while keeping the size of what survives; closing (A • B) reverses the order to seal holes without growing the shapes. The morphological gradient (dilation minus erosion) leaves a one-element-wide boundary shell — a fast edge detector. Choose a square, cross, or disk element, set its radius, and apply the operation repeatedly; the same routines are exposed by scipy.ndimage's binary_erosion, binary_dilation, binary_opening, and binary_closing.
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The math, the assumptions, real-world uses, or a code translation — explained for this exact simulation.
Is this morphological erosion dilation tool really free?▾
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The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.