[Insight-users] Re: Using KalmanLinearEstimator

[Luis Ibanez]

Hi Mathieu,


1) Do you have only 6 data points in your real problem ?

    They may not be enough data for bringing the
    Kalman Estimator to convergence to a good
    estimation.

    I was assuming that you had in the order
    of hundreds to thousands of values.


2) You may find useful to print out the estimations
    as you go adding new points. One of the interesting
    advantages of the Kalman estimator is that it
    provides at estimation using the currently provided
    samples.

    E.g., move the line:

 >  VectorType estimation = filter.GetEstimator();

    inside the for loop and print out the value of
    the estimation. Then you will see how it get
    close to the [A,B] values.

    Note that the estimator is initialized with [0,0]
    so it may take a while (many measurement) to shift
    the estimation to your real A,B values.

    Maybe we should add a method to initialize the
    Estimator to a known value. In this way, the
    Kalman estimator would focus on refining the
    estimation.



    Regards,


      Luis


-------------------------
Mathieu Malaterre wrote:
> Hello,
> 
>  I must be something obviously really wrong when using the
> KalmanLinearEstimator class, because the results are way off. Could
> someone please help find the obvious mistake I must have done.
> 
> thanks !
> -Mathieu
> 
> Code is:
> 
> #include "itkKalmanLinearEstimator.h"
> #include <iostream>
> 
> int main(int, char* [] )
> {
>  typedef itk::KalmanLinearEstimator<double,2> KalmanFilterType;
> 
>  typedef KalmanFilterType::VectorType    VectorType;
>  typedef KalmanFilterType::MatrixType    MatrixType;
>  typedef KalmanFilterType::ValueType     ValueType;
> 
>  KalmanFilterType filter;
> 
>  filter.ClearEstimation();
>  filter.SetVariance(1.0);
> 
>  ValueType     measure;
>  VectorType    predictor;
> 
>  // A ~ 0.323
>  // B ~ 172.4
>  const double points[][2] = {
>    {-16.6817,167.095},
>    {-44.4257,158.118},
>    {-68.0884,150.462},
>    {-87.7176,144.111},
>    {-103.447,139.021},
>    {-115.437,135.142},
>  };
>  const unsigned int N = sizeof(points) / ( 2 * sizeof( double ));
> 
>  predictor(1)  =  1.0;
>  for(unsigned int ax=0; ax < N; ax++)
>    {
>    predictor(0) = points[ax][0];
>    measure = points[ax][1];
>    //std::cout << predictor(0) << "," << measure << std::endl;
>    filter.UpdateWithNewMeasure(measure,predictor);
>    }
> 
>  VectorType estimation = filter.GetEstimator();
> 
>  std::cout << std::endl << "The Estimation is : " << std::endl;
>  std::cout << estimation;
> 
>  std::cout << std::endl << std::endl;
>  return EXIT_SUCCESS;
> }
> 
> 
> On 5/6/07, Luis Ibanez <luis.ibanez at kitware.com> wrote:
> 
>>
>> Hi Mathieu,
>>
>> You may want to try the KalmanLinearEstimator.
>>
>> http://www.itk.org/Insight/Doxygen/html/classitk_1_1KalmanLinearEstimator.html 
>>
>>
>>
>> Your estimator will be the vector [a,b],
>>
>> and your measures will be the vector [1,Y]
>>
>>
>> The Kalman Linear Estimator should converge to the vector [a,b] that
>> minimizes the sum of squared errors between X and (a+bY).
>>
>> For an example, you may want to look at:
>>
>>
>>
>>      Insight/Testing/Code/Algorithms/
>>           itkKalmanLinearEstimatorTest.cxx
>>
>>
>>
>>     Regards,
>>
>>
>>         Luis
>>
>>
>> ---------------------------
>> Mathieu Malaterre wrote:
>> > Hello,
>> >
>> >  I have a pretty simple problem to solve and I was wondering if I
>> > could reuse any brick already existing in ITK.
>> >
>> >  I am working with a 4D dataset (x,y,y and theta), where I can
>> > express for each pixel that:
>> >
>> > I(x,y,z)/sin(theta) = F(x,y,z) + b*I(x,y,z)/tan(theta)
>> >
>> >  My goal here is to find out the b constant, I do not need the
>> > F(x,y,z) part. Obvisouly all I need to do is draw the line
>> >
>> >   X = a + b Y
>> >
>> > where X = I(x,y,z)/sin(theta) and Y = I(x,y,z)/tan(theta) and the
>> > slope will give me 'b'.
>> >
>> > Any suggestions on which class to reuse ?
>> > thanks !
>>
> 
>