It was a busy week, so I didn't reach my goal to get more familiar with algorithm of searching feature points, but I have some new ideas.
Feature searching algorithms are versatile and they can give diverse results on photos. Some methods detect more points than others on the one photo, and a little amount of points on another. It happens due to quality of the landscape and number of well-marked objects as single trees and buildings. That's why we need to select the most stable and robust algorithm which can provide enough points in almost all cases.
FAST Corner Detector algorithm is extemely speedy and simple, but SURF is more robust and tends to search more points. I think that it would be interesting to compare these and others algorithms and pick the best. Today many feature searching approaches exist and they are being developed and improved, so I need to briefly investigate their advantages and disadvantages.
I think that the primary task is to enhance optimization and try a Levenberg-Marquardt method. I hope that the stitching process will work significantly faster.