Home Page Toolkit Overview Using GML   User Input Services     Finger Tracker     Calibrator     Frame Grabber     Service protocol Obtaining GML   Installing GML   Licence Developer Documentation   Tcl/Tk API     The GML Canvas     Image processing   Tcl Scripts Library     List of Classes     List of Files   C/C++ API     List of Classes     List of Files

gml_Gaussian2.h // gml_Gaussian2.h -- // // Declares and defines the "gml_TGaussian2" class. Instances of this class store // the gaussian information (mean, covariance) about a set of 2 dimentional samples. // // Copyright (c) 1997-2004 The CLIPS-IMAG Laboratory. // // See the file "gml_LicenseTerms.txt" for information on usage and redistribution // of this file, and for a DISCLAIMER OF ALL WARRANTIES. // // Created in March, 1997 (FB). // Ported to GML in January, 2004 (JL). #ifndef __GML_GAUSSIAN2__ #define __GML_GAUSSIAN2__ // project includes #include "gml/base/gml_Errors.h" #include "gml/base/gml_Types.h" #include "gml/math/gml_ElMat2.h" template <class T> class gml_TGaussian2; /// 2D Gaussians with single-precision floting-point coordinates. typedef gml_TGaussian2 <Float32> gml_TGaussian2_Float32; /// 2D Gaussians with double-precision floting-point coordinates. typedef gml_TGaussian2 <Float64> gml_TGaussian2_Float64; /// 2D Gaussians with 32-bit integer coordinates. typedef gml_TGaussian2 <UInt32> gml_TGaussian2_UInt32; /// 2D Gaussians with 64-bit integer coordinates. typedef gml_TGaussian2 <UInt64> gml_TGaussian2_UInt64; /// gml_TGaussian2 -- /// /// Compute and maintain a gaussian representation (approximation) of a set /// of 2-dimensionnal samples template <class T> class gml_TGaussian2 { public: /// Constructor: zero the fields gml_TGaussian2(); /// Setup the structure gml_TError Init (); /// Zero the structure void Reset (); /// Do nothing void Dispose () {} /// Set <this> to be the exact copy of <gauss>. void Clone (gml_TGaussian2<T> *gauss); /// Adds the distribution of <gauss> in <this>. void Add (gml_TGaussian2<T> *gauss); /// Adds a sample in the set. /// <s> must point to an array of two T values. void AddSample (T * s); /// Adds a sample in the set. void AddSample (T x, T y); /// Adds a weighted sample in the set. void AddSample (T x, T y, T weight); /// Return the total number of samples added. T Samples () { return fCount; } /// Compute mean, covariance, and inverse covariance. /// Returns an error if the covariance matrix could not /// be inverted. gml_TError UpdateStats (); /// Returns the squared mahalanobis distance from (x, y) to the /// gaussian. /// @warning Call UpdateStats() before calling GetMahaSquared(). T GetMahaSquared (T x, T y); /// Return the gaussian's value at (x, y). /// @warning Call UpdateStats() before calling Value(). T Value (T x, T y); /// Calculates the mean of the gaussian and puts the result /// in <m>. <m> must point to an array of two T values. void GetMean (T * m); /// Calculates the covariance matrix of the gaussian and puts the result /// in <m>. <m> must point to an array of four T values. void GetVar (T * m); T fCount; ///< number of samples added to set T fSum[2]; ///< sum of samples by components T fSumSq[2]; ///< sum of samples squared by components T fMixSum; ///< sum of component products // the following are computed by UpdateStats() T fMean[2]; T fCovariance[4]; T fInvCovariance[4]; }; //////////////////////////////////////////////////////////////////////////////// // // // inline function definition // // // //////////////////////////////////////////////////////////////////////////////// // // Constructor // template <class T> inline gml_TGaussian2<T>::gml_TGaussian2 () { Reset(); } // // Init // template <class T> inline gml_TError gml_TGaussian2<T>::Init () { Reset(); return gml_cNoError; } // // Reset // template <class T> inline void gml_TGaussian2<T>::Reset () { fCount = T(0); fSum[0] = T(0); fSum[1] = T(0); fSumSq[0] = T(0); fSumSq[1] = T(0); fMixSum = T(0); } // // Clone // template <class T> inline void gml_TGaussian2<T>::Clone (gml_TGaussian2<T> *gauss) { *this = *gauss; } // // Add // template <class T> inline void gml_TGaussian2<T>::Add (gml_TGaussian2<T> *gauss) { fCount += gauss->fCount; fSum[0] += gauss->fSum[0]; fSum[1] += gauss->fSum[1]; fSumSq[0] += gauss->fSumSq[0]; fSumSq[1] += gauss->fSumSq[1]; fMixSum += gauss->fMixSum; } // // AddSample // template <class T> inline void gml_TGaussian2<T>::AddSample (T * s) { fCount += T(1); fSum[0] += s[0]; fSumSq[0] += s[0] * s[0]; fSum[1] += s[1]; fSumSq[1] += s[1] * s[1]; fMixSum += s[1] * s[0]; } // // AddSample // template <class T> inline void gml_TGaussian2<T>::AddSample (T x, T y) { fCount += T(1); fSum[0] += x; fSumSq[0] += x * x; fSum[1] += y; fSumSq[1] += y * y; fMixSum += x * y; } // // AddSample, weighted // template <class T> inline void gml_TGaussian2<T>::AddSample (T x, T y, T weight) { fCount += weight; fSum[0] += weight * x; fSumSq[0] += weight * x * x; fSum[1] += weight * y; fSumSq[1] += weight * y * y; fMixSum += weight * x * y; } // // UpdateStats // template <class T> inline gml_TError gml_TGaussian2<T>::UpdateStats () { GetMean (fMean); GetVar (fCovariance); return gml_EM2_Invert (fCovariance, fInvCovariance); } // // GetMahaSquared // template <class T> inline T gml_TGaussian2<T>::GetMahaSquared (T x, T y) { T xn, yn; xn = x - fMean[0]; yn = y - fMean[1]; return fInvCovariance[0] * xn * xn + fInvCovariance[1] * xn * yn * 2 + fInvCovariance[3] * yn * yn; } // // Value // template <class T> inline T gml_TGaussian2<T>::Value (T x, T y) { return Exp (- GetMahaSquared (x, y)); } // // GetMean // template <class T> inline void gml_TGaussian2<T>::GetMean (T * m) { m[0] = fSum[0] / fCount; m[1] = fSum[1] / fCount; } // // GetVar // template <class T> inline void gml_TGaussian2<T>::GetVar (T * m) { T mu0 = fSum[0] / fCount; T mu1 = fSum[1] / fCount; m[0] = fSumSq[0] / fCount - mu0 * mu0; m[3] = fSumSq[1] / fCount - mu1 * mu1; m[1] = m[2] = fMixSum / fCount - mu0 * mu1; } #endif /* __GML_GAUSSIAN2__ */ Generated on Tue Jun 12 14:03:27 2007 for gml by Doxygen 1.5.2.

Contact: julien (dot) letessier (at) gmail (dot) com. Copyright (c) 2000-2007 CLIPS-IMAG Laboratory, Grenoble, France. All rights reserved. W3CXHTML 1.0 W3CCSS 2.0