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
A and B are used for the calculation of log likelihood. This log likelihood is calculated under the assumption that RGB color of the object is observed with gaussian noise. But this assumption might not be true. Please think this is a toy to understand particle filter.