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              "value": "This function operates on the following ideas:"
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          "ordered": true,
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          "children": [
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                      "value": "Make a first pass over the image's last dimension and flag candidates    for local maxima by comparing pixels in only one direction.    If the pixels aren't connected in the last dimension all pixels are    flagged as candidates instead."
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                      "value": "Perform a flood-fill to find all connected pixels that have the same    gray value and are part of the plateau."
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                      "value": "Consider the connected neighborhood of a plateau: if no bordering sample    has a higher gray level, mark the plateau as a definite local maximum."
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                      "value": " is returned with "
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                      "name": "connectivity"
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                      "value": "If True, the output will be a tuple of one-dimensional arrays representing the indices of local maxima in each dimension. If False, the output will be a boolean array with the same shape as "
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        "value": "from skimage.morphology import local_maxima\nimage = np.zeros((4, 7), dtype=int)\nimage[1:3, 1:3] = 1\nimage[3, 0] = 1\nimage[1:3, 4:6] = 2\nimage[3, 6] = 3\nimage\n",
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        "value": "\nFind local maxima by comparing to all neighboring pixels (maximal\nconnectivity):\n\n"
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        "value": "\nFind local maxima without comparing to diagonal pixels (connectivity 1):\n\n"
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        "value": "\nand exclude maxima that border the image edge:\n\n"
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