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                      "value": "Harris response image."
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                      "value": "Method to compute the response image from the auto-correlation matrix."
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                      "value": "Sensitivity factor to separate corners from edges, typically in range "
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                      "value": "Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix."
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              "value": "This corner detector uses information from the auto-correlation matrix A      "
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          "value": "A = [(imx**2)   (imx*imy)] = [Axx Axy]\n    [(imx*imy)   (imy**2)]   [Axy Ayy]",
          "execution_status": null
        },
        {
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              "value": "Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as      "
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          "value": "det(A) - k * trace(A)**2",
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              "value": "or      "
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          "value": "2 * det(A) / (trace(A) + eps)",
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        "value": "from skimage.feature import corner_harris, corner_peaks\nsquare = np.zeros([10, 10])\nsquare[2:8, 2:8] = 1\nsquare.astype(int)\ncorner_peaks(corner_harris(square), min_distance=1)\n",
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  "references": [
    ".. [1] https://en.wikipedia.org/wiki/Corner_detection"
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