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itkKalmanLinearEstimator.h

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00001 /*========================================================================= 00002 00003 Program: Insight Segmentation & Registration Toolkit 00004 Module: $RCSfile: itkKalmanLinearEstimator.h,v $ 00005 Language: C++ 00006 Date: $Date: 2003/09/10 14:28:33 $ 00007 Version: $Revision: 1.15 $ 00008 00009 Copyright (c) Insight Software Consortium. All rights reserved. 00010 See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. 00011 00012 This software is distributed WITHOUT ANY WARRANTY; without even 00013 the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 00014 PURPOSE. See the above copyright notices for more information. 00015 00016 =========================================================================*/ 00017 #ifndef __itkKalmanLinearEstimator_h 00018 #define __itkKalmanLinearEstimator_h 00019 00020 #include "itkMacro.h" 00021 00022 #include <vnl/vnl_vector_fixed.h> 00023 #include <vnl/vnl_matrix_fixed.h> 00024 00025 namespace itk 00026 { 00027 00040 template <class T, unsigned int VEstimatorDimension> 00041 class ITK_EXPORT KalmanLinearEstimator 00042 { 00043 public: 00046 itkStaticConstMacro( Dimension, unsigned int, 00047 VEstimatorDimension); 00048 00051 typedef vnl_vector_fixed<T,VEstimatorDimension> VectorType; 00052 00055 typedef vnl_matrix_fixed<T,VEstimatorDimension,VEstimatorDimension> MatrixType; 00056 00061 typedef T ValueType; 00062 00067 void UpdateWithNewMeasure( const ValueType & newMeasure, 00068 const VectorType & newPredictor ); 00069 00073 void ClearEstimation(void) 00074 { VectorType v(T(0)); m_Estimator = v; } 00075 00078 void ClearVariance(void) 00079 { 00080 m_Variance.set_identity(); 00081 } 00082 00089 void SetVariance(const ValueType & var = 1.0) 00090 { 00091 m_Variance.set_identity(); 00092 m_Variance *= var; 00093 } 00094 00100 void SetVariance(const MatrixType & m) 00101 { m_Variance = m; } 00102 00105 const VectorType & GetEstimator(void) const 00106 { return m_Estimator; } 00107 00110 const MatrixType & GetVariance(void) const 00111 { return m_Variance; } 00112 00113 private: 00118 void UpdateVariance( const VectorType & ); 00119 00122 VectorType m_Estimator; 00123 00130 MatrixType m_Variance; 00131 00132 }; 00133 00134 00135 template <class T, unsigned int VEstimatorDimension> 00136 void 00137 KalmanLinearEstimator<T,VEstimatorDimension> 00138 ::UpdateWithNewMeasure( const ValueType & newMeasure, 00139 const VectorType & newPredictor ) 00140 { 00141 ValueType measurePrediction = dot_product( newPredictor , m_Estimator ); 00142 00143 ValueType errorMeasurePrediction = newMeasure - measurePrediction; 00144 00145 VectorType Corrector = m_Variance * newPredictor; 00146 00147 for( unsigned int j=0; j<VEstimatorDimension; j++) 00148 { 00149 m_Estimator(j) += Corrector(j) * errorMeasurePrediction; 00150 } 00151 00152 UpdateVariance( newPredictor ); 00153 00154 } 00155 00156 00157 template <class T, unsigned int VEstimatorDimension> 00158 void 00159 KalmanLinearEstimator<T,VEstimatorDimension> 00160 ::UpdateVariance( const VectorType & newPredictor ) 00161 { 00162 VectorType aux = m_Variance * newPredictor; 00163 00164 ValueType denominator = 1.0/(1.0 + dot_product( aux , newPredictor ) ); 00165 00166 for( unsigned int col=0; col<VEstimatorDimension; col++) 00167 { 00168 for( unsigned int row=0; row<VEstimatorDimension; row++) 00169 { 00170 m_Variance(col,row) -= aux(col)*aux(row)*denominator; 00171 } 00172 } 00173 } 00174 00175 } // end namespace itk 00176 00177 #endif

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