Scale-invariant feature operator

In the fields of computer vision and image analysis, the scale-invariant feature operator (or SFOP) is an algorithm to detect local features in images. The algorithm was published by Förstner et al. in 2009.
Algorithm.
The scale-invariant feature operator (SFOP) is based on two theoretical concepts: 
Desired properties of keypoint detectors:
scale-invariant corner/circle detector.
Theory.
Maximize the weight.
Maximize the weight formula_1= 1/variance of a point formula_2
 formula_3 
comprising:
1. the image model
formula_4
2. the smaller eigenvalue of the structure tensor
formula_5
Reduce the search space.
Reduce the 5-dimensional search space by