blob-dog

blob_dog

skimage.feature.blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0, overlap=0.5) [source]

Finds blobs in the given grayscale image.

Blobs are found using the Difference of Gaussian (DoG) method [R126]. 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. sigma_ratio : float, optional The ratio between the standard deviation of Gaussian Kernels used for computing the Difference of Gaussians 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.

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

[R126]

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

Examples

>>> from skimage import data, feature
>>> feature.blob_dog(data.coins(), threshold=.5, max_sigma=40)
array([[  45.      ,  336.      ,   16.777216],
       [  52.      ,  155.      ,   16.777216],
       [  52.      ,  216.      ,   16.777216],
       [  54.      ,   42.      ,   16.777216],
       [  54.      ,  276.      ,   10.48576 ],
       [  58.      ,  100.      ,   10.48576 ],
       [ 120.      ,  272.      ,   16.777216],
       [ 124.      ,  337.      ,   10.48576 ],
       [ 125.      ,   45.      ,   16.777216],
       [ 125.      ,  208.      ,   10.48576 ],
       [ 127.      ,  102.      ,   10.48576 ],
       [ 128.      ,  154.      ,   10.48576 ],
       [ 185.      ,  347.      ,   16.777216],
       [ 193.      ,  213.      ,   16.777216],
       [ 194.      ,  277.      ,   16.777216],
       [ 195.      ,  102.      ,   16.777216],
       [ 196.      ,   43.      ,   10.48576 ],
       [ 198.      ,  155.      ,   10.48576 ],
       [ 260.      ,   46.      ,   16.777216],
       [ 261.      ,  173.      ,   16.777216],
       [ 263.      ,  245.      ,   16.777216],
       [ 263.      ,  302.      ,   16.777216],
       [ 267.      ,  115.      ,   10.48576 ],
       [ 267.      ,  359.      ,   16.777216]])