getting this error: Error using FastHessian_getResponse (line 2)
Not enough input arguments.
Error in FastHessian_BuildDerivative (line 4)
ds = (FastHessian_getResponse(t,r, c) - FastHessian_getResponse(b,r, c, t)) / 2;
for ds, there are only 3 arguments passed which throws an error, plz can anyone tell me urgently what can be the possible 4th argument to run this code successfully?
Any help will be highly appreciated..
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
@SasiKanth: thanks. The thing is that I think my images are affine-transformed rather than projective-transformed. I am working on Retinal OCT scans. Also, I used your build-in function to obtain the wim1 image, which is the transformed form of i1, but the registration with i2 doesn't look promising. I met the same situation when I tried SIFT algorithm. I guess it's because of the algorithm itself, if you by any chance have the similar experience, would you please offer me any knowledge? If not, thank you anyway, it's a great algorithm.
@Hasheem: There is no specification to the king of image you need to use as feature matching takes place on gray images.
@Diogo: RandStream is a function in the newer versions of MATLAB. Why don't you replace it with the function that is present in the version you are using?
@Sean: You are referring to a function called 'imtransform' which transforms images based on a provided transformation matrix. Affine is only one case where as you said, the last column needs to be 0 0 1. however, you may even provide a full projective 3x3 matrix and pass the argument 'projective' to the function 'maketform'.