Re: [Vxl-users] Why is the dimension of Jacobian in vnl_levenberg_marquardt is fixed in this way?

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> When estimating the fundamental matrix, N equals to the number of
> correspondences as each correspondence provides only one constraint. But
> as you are estimating the homography, N equals to ***2 times the number
> of correspondences*** as each provides two constraints.

I can't agree with you on this point. Take estimating the fundamental matrix by minimizing geometric distance $\sum_i d(x_i, \hat{x}_i)^2 + d(x'_i, \hat{x'}_i)^2 $ for instance, if one regards $d(x_i, \hat{x}_i)^2 + d(x'_i, \hat{x'}_i)^2$ as one item, the number of residual is 1 for each point correspondence. This is what FMatrixComputeNonLinear has done. If one takes $d(x_i, \hat{x}_i)^2 + d(x'_i, \hat{x'}_i)^2$ as summation of two terms, the number of residual is 2 for each point correspondence. Furthermore, if ones take d(x_i, \hat{x}_i)^2 as a summation of coordinate differences in both x and y direction, the number of residual will be 4.

For nonlinear estimation with Sampson error, I believe there is one residual for each correspondence because the error is a scaler $\epsilon^T (J J^T)^{-1} \epsilon$.

> Also, when providing vnl_levenberg_marquardt with the analytic Jacobian
> matrix, it is a good practice to turn on
> vnl_levenberg_marquardt::set_check_derivatives( int ), which will
> compare the provided Jacobian with the one computed from numerical
> differences. After verifying the two are equal within a small error
> range, it is to be turned off for the consideration of speed.

Thanks for this tip.

Lianqing Yu

----- Original Message ----- 
From: "Gehua Yang" <ya...@rp...>
To: "Lianqing Yu" <lq...@nl...>
Cc: <vxl...@li...>
Sent: Tuesday, August 29, 2006 3:27 AM
Subject: Re: [Vxl-users] Why is the dimension of Jacobian in vnl_levenberg_marquardt is fixed in this way?

> Hi,
> 
> First of all, you'll need to understand the difference between
> Homography and Fundamental matrix and the difference between their
> associated error measures, which I am afraid we (VXL community) can
> provide little help.
> 
> I'll give this problem a shot based on my understanding of your
> description.
> 
> You are confused by "the number of residuals". In the context of the
> Levenberg-Marquardt, the objective function has a least-squares form:
> sum_i( f_i^2 ) (i=1...N). Thus, the input to vnl_levenberg_marquardt is
> a vector of [f_1, f_2, ..., f_N] and the corresponding Jacobian is a
> N-times-k matrix (k is the degree of freedom).
> 
> When estimating the fundamental matrix, N equals to the number of
> correspondences as each correspondence provides only one constraint. But
> as you are estimating the homography, N equals to ***2 times the number
> of correspondences*** as each provides two constraints.
> 
> Also, when providing vnl_levenberg_marquardt with the analytic Jacobian
> matrix, it is a good practice to turn on
> vnl_levenberg_marquardt::set_check_derivatives( int ), which will
> compare the provided Jacobian with the one computed from numerical
> differences. After verifying the two are equal within a small error
> range, it is to be turned off for the consideration of speed.
> 
> Gehua
> 
> 
> Lianqing Yu wrote:
> 
>> Hi,
>> I've got an issue when I use vnl_levenberg_marquardt to refine the
>> estimation of the 2-D homography between calibration rig plane and
>> image plane.
>> My algorithm follows the gold standard algorithm proposed in [Hartley
>> & Zisserman, 2000] (algorithm 3.3, p98) and I choose to use Sampson
>> error for the sake of speed. For implementation, I write a class
>> similar to FMatrixComputeNonLinear. To gain faster convergence, I
>> provide a customized routine gradf(), in which the Jocabian is a 2N x
>> 9 matrix (equation 3.31, p114), where N is the number of point
>> correspondences between the two planes.
>> Here comes the problem. For geometric minimization of Sampson error,
>> the number of residuals is N, since there is one residual (the norm
>> $||\delta_x||^2$, equation 3.12, p83) for each correspondence:
>> HMatrix2DComputeSampson::HMatrix2DComputeSampson(int N)
>> : vnl_least_square_function(9, N, use_gradient) {}
>> However, if my class is initialized like this, I find in the
>> vnl_levenberg_marquardt code that dimension of Jocabian matrix in
>> gradf() is fixed as N x 9 and my program will crash. One workaround is
>> to set the number of residuals to 2*N.
>> HMatrix2DComputeSampson::HMatrix2DComputeSampson(int N)
>> : vnl_least_square_function(9, 2*N, use_gradient) {}
>> But this will introduce some minor problems (more RAM consumption,
>> slower speed, unintuitive).
>> In all, I'd like to know your opinions on this issue.
>> I'd appreciate your patience to read this mail and thanks in advance.
>> Lianqing Yu
>> 8/28/2006
>>
>> PhD Candidate
>> Robot Vision Group
>> National Laboratory of Pattern Recognition (NLPR)
>> Institute of Automation, Chinese Academy of Sciences (CASIA)
>>
>> No. 95, Zhongguancun East Road,
>> 100080,Beijing, China
>> Email: lq...@nl... <mailto:lq...@nl...>
>>
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