Hi Mr SasiKanth
I am needed to find estimation for the homography 2D that can relate a big number of points (100, 250...)
And i found this code is very interesting, especially the function homest2D
But if you may I have some question about homest2D:
1) Why you are using approximately the same approach of Fundamental Matrix? and what this can help?
2) if we have 2 sets of points, is homest2D enough to calculate the homography?
3) did you do any experimentation with homest2D? or any comparison with other algorithm, like Normalized DLT (Hartley and Zisserman: Multiple View Geometry in Computer vision)?
Thank you in advance for any help, and if you may have any document that explain each step of the algorithm, i will be very thankful
Thank you again
Sorry for poorly commented code...
"Xstd_rgb" means standard deviation of observation noise, which means noise you get when you observe the state of something.
"Xstd_pos" and "Xstd_vec" mean standard deviation of system noise, which describes how far actual movement of target object differs from the ideal model (in this case, linear uniform motion).
State space become of 2 componets, one is "position of particle" and another is "speed of particle".
You can define these 3 types of noise by these parameters.