Mahotas - Getting SURF Dense Points

In this article we will see how we can get the speeded up robust dense feature of image in mahotas. In computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. For this we are going to use the fluorescent microscopy image from a nuclear segmentation benchmark. We can get the image with the help of command given below
 

mahotas.demos.nuclear_image()

Below is the nuclear_image 
 

In order to do this we will use surf.dense method 
 

Syntax : surf.surf(img, spacing)
Argument : It takes image object and integer as argument
Return : It returns numpy.ndarray i.e descriptors at dense points 
 

Example 1 : 
 

Output : 
 

Example 2 : 
 

Output :