blob-log

blob_log

skimage.feature.blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=0.2, overlap=0.5, log_scale=False) [source]

Finds blobs in the given grayscale image.

Blobs are found using the Laplacian of Gaussian (LoG) method [R129]. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob.

Parameters:

image : ndarray Input grayscale image, blobs are assumed to be light on dark background (white on black). min_sigma : float, optional The minimum standard deviation for Gaussian Kernel. Keep this low to detect smaller blobs. max_sigma : float, optional The maximum standard deviation for Gaussian Kernel. Keep this high to detect larger blobs. num_sigma : int, optional The number of intermediate values of standard deviations to consider between min_sigma and max_sigma. threshold : float, optional. The absolute lower bound for scale space maxima. Local maxima smaller than thresh are ignored. Reduce this to detect blobs with less intensities. overlap : float, optional A value between 0 and 1. If the area of two blobs overlaps by a fraction greater than threshold, the smaller blob is eliminated. log_scale : bool, optional If set intermediate values of standard deviations are interpolated using a logarithmic scale to the base 10. If not, linear interpolation is used.

Returns:

A : (n, 3) ndarray A 2d array with each row representing 3 values, (y,x,sigma) where (y,x) are coordinates of the blob and sigma is the standard deviation of the Gaussian kernel which detected the blob.

Notes

The radius of each blob is approximately .

References

[R129]

(1, 2) http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian

Examples

>>> from skimage import data, feature, exposure
>>> img = data.coins()
>>> img = exposure.equalize_hist(img)  # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[ 113.        ,  323.        ,    1.        ],
       [ 121.        ,  272.        ,   17.33333333],
       [ 124.        ,  336.        ,   11.88888889],
       [ 126.        ,   46.        ,   11.88888889],
       [ 126.        ,  208.        ,   11.88888889],
       [ 127.        ,  102.        ,   11.88888889],
       [ 128.        ,  154.        ,   11.88888889],
       [ 185.        ,  344.        ,   17.33333333],
       [ 194.        ,  213.        ,   17.33333333],
       [ 194.        ,  276.        ,   17.33333333],
       [ 197.        ,   44.        ,   11.88888889],
       [ 198.        ,  103.        ,   11.88888889],
       [ 198.        ,  155.        ,   11.88888889],
       [ 260.        ,  174.        ,   17.33333333],
       [ 263.        ,  244.        ,   17.33333333],
       [ 263.        ,  302.        ,   17.33333333],
       [ 266.        ,  115.        ,   11.88888889]])