corner-fast

corner_fast

skimage.feature.corner_fast(image, n=12, threshold=0.15) [source]

Extract FAST corners for a given image.

Parameters:

image : 2D ndarray Input image. n : int Minimum number of consecutive pixels out of 16 pixels on the circle that should all be either brighter or darker w.r.t testpixel. A point c on the circle is darker w.r.t test pixel p if Ic < Ip - threshold and brighter if Ic > Ip + threshold. Also stands for the n in FAST-n corner detector. threshold : float Threshold used in deciding whether the pixels on the circle are brighter, darker or similar w.r.t. the test pixel. Decrease the threshold when more corners are desired and vice-versa.

Returns:

response : ndarray FAST corner response image.

References

[R130]

Edward Rosten and Tom Drummond “Machine Learning for high-speed corner detection”, http://www.edwardrosten.com/work/rosten_2006_machine.pdf

[R131]

Wikipedia, “Features from accelerated segment test”, https://en.wikipedia.org/wiki/Features_from_accelerated_segment_test

Examples

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>>> from skimage.feature import corner_fast, corner_peaks >>> square = np.zeros((12, 12)) >>> square[3:9, 3:9] = 1 >>> square.astype(int) array([[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],        [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],        [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],        [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],        [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],        [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],        [0, 0, 0, 1, 1, 1, 1, 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, 0, 0],        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> corner_peaks(corner_fast(square, 9), min_distance=1) array([[3, 3],        [3, 8],        [8, 3],        [8, 8]])