The DoH approach is the fastest of all these approaches. It detects the blobs by computing maximums in the matrix of the Determinant of Hessian of the image. The size of blobs does not have any impact on the speed of detection. Both the bright blobs on the dark background and the dark blobs on the bright backgrounds are detected by this approach, but the small blobs are not detected accurately.

The next code block demonstrates how to implement these aforementioned three algorithms using scikit-image:

from numpy import sqrt
from skimage.feature import blob_dog, blob_log, blob_doh

im = imread('../images/butterfly.png') 
im_gray = rgb2gray(im)
log_blobs = blob_log(im_gray, max_sigma=30, num_sigma=10, threshold=.1)
log_blobs[:, 2] = sqrt(2) * log_blobs[:, 2] # Compute radius in the 3rd column
dog_blobs = blob_dog(im_gray, max_sigma=30, threshold=0.1)
dog_blobs[:, 2] = sqrt(2) * dog_blobs[:, 2]
doh_blobs = blob_doh(im_gray, max_sigma=30, threshold=0.005)
list_blobs = [log_blobs, dog_blobs, doh_blobs]
color, titles = ['yellow', 'lime', 'red'], ['Laplacian of Gaussian', 'Difference of Gaussian', 'Determinant of Hessian']
sequence = zip(list_blobs, colors, titles)
fig, axes = pylab.subplots(2, 2, figsize=(20, 20), sharex=True, sharey=True)
axes = axes.ravel()
axes[0].imshow(im, interpolation='nearest')
axes[0].set_title('original image', size=30), axes[0].set_axis_off()
for idx, (blobs, color, title) in enumerate(sequence):
 axes[idx+1].imshow(im, interpolation='nearest')
 axes[idx+1].set_title('Blobs with ' + title, size=30)
 for blob in blobs:
   y, x, row = blob
   col = pylab.Circle((x, y), row, color=color, linewidth=2, fill=False)
   axes[idx+1].add_patch(col),    axes[idx+1].set_axis_off()
pylab.tight_layout(), pylab.show()

The next screenshots show the output of the code which is the blobs detected with different algorithms:

Both the corner and blob features possess repeatability, saliency, and locality properties.