skimage2.metrics.hausdorff_distance#

skimage2.metrics.hausdorff_distance(image0, image1, method='standard')[source]#

Calculate the Hausdorff distance between nonzero elements of given images.

Parameters:

image0, image1ndarray: Arrays where True represents a point that is included in a set of points. Both arrays must have the same shape.
method{‘standard’, ‘modified’}, optional, default = ‘standard’: The method to use for calculating the Hausdorff distance. standard is the standard Hausdorff distance, while modified is the modified Hausdorff distance.

Returns:

distancefloat: The Hausdorff distance between coordinates of nonzero pixels in image0 and image1, using the Euclidean distance.

Notes

The Hausdorff distance [1] is the maximum distance between any point on image0 and its nearest point on image1, and vice-versa. The Modified Hausdorff Distance (MHD) has been shown to perform better than the directed Hausdorff Distance (HD) in the following work by Dubuisson et al. [2]. The function calculates forward and backward mean distances and returns the largest of the two.

References

[1]

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

[2]

M. P. Dubuisson and A. K. Jain. A Modified Hausdorff distance for object matching. In ICPR94, pages A:566-568, Jerusalem, Israel, 1994. DOI:10.1109/ICPR.1994.576361 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.8155

Examples

>>> points_a = (3, 0)
>>> points_b = (6, 0)
>>> shape = (7, 1)
>>> image_a = np.zeros(shape, dtype=bool)
>>> image_b = np.zeros(shape, dtype=bool)
>>> image_a[points_a] = True
>>> image_b[points_b] = True
>>> hausdorff_distance(image_a, image_b)
3.0