corner-subpix

corner_subpix

skimage.feature.corner_subpix(image, corners, window_size=11, alpha=0.99) [source]

Determine subpixel position of corners.

A statistical test decides whether the corner is defined as the intersection of two edges or a single peak. Depending on the classification result, the subpixel corner location is determined based on the local covariance of the grey-values. If the significance level for either statistical test is not sufficient, the corner cannot be classified, and the output subpixel position is set to NaN.

Parameters:	image : ndarray Input image. corners : (N, 2) ndarray Corner coordinates (row, col). window_size : int, optional Search window size for subpixel estimation. alpha : float, optional Significance level for corner classification.
Returns:	positions : (N, 2) ndarray Subpixel corner positions. NaN for “not classified” corners.

References

[R142]

http://www.ipb.uni-bonn.de/uploads/tx_ikgpublication/ foerstner87.fast.pdf

[R143]

http://en.wikipedia.org/wiki/Corner_detection

Examples

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>>> from skimage.feature import corner_harris, corner_peaks, corner_subpix >>> img = np.zeros((10, 10)) >>> img[:5, :5] = 1 >>> img[5:, 5:] = 1 >>> img.astype(int) array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]]) >>> coords = corner_peaks(corner_harris(img), min_distance=2) >>> coords_subpix = corner_subpix(img, coords, window_size=7) >>> coords_subpix array([[ 4.5,  4.5]])