00001 /*!@file ObjRec/BayesianBiaser.C */ 00002 00003 // //////////////////////////////////////////////////////////////////// // 00004 // The iLab Neuromorphic Vision C++ Toolkit - Copyright (C) 2000-2005 // 00005 // by the University of Southern California (USC) and the iLab at USC. // 00006 // See http://iLab.usc.edu for information about this project. // 00007 // //////////////////////////////////////////////////////////////////// // 00008 // Major portions of the iLab Neuromorphic Vision Toolkit are protected // 00009 // under the U.S. patent ``Computation of Intrinsic Perceptual Saliency // 00010 // in Visual Environments, and Applications'' by Christof Koch and // 00011 // Laurent Itti, California Institute of Technology, 2001 (patent // 00012 // pending; application number 09/912,225 filed July 23, 2001; see // 00013 // http://pair.uspto.gov/cgi-bin/final/home.pl for current status). // 00014 // //////////////////////////////////////////////////////////////////// // 00015 // This file is part of the iLab Neuromorphic Vision C++ Toolkit. // 00016 // // 00017 // The iLab Neuromorphic Vision C++ Toolkit is free software; you can // 00018 // redistribute it and/or modify it under the terms of the GNU General // 00019 // Public License as published by the Free Software Foundation; either // 00020 // version 2 of the License, or (at your option) any later version. // 00021 // // 00022 // The iLab Neuromorphic Vision C++ Toolkit is distributed in the hope // 00023 // that it will be useful, but WITHOUT ANY WARRANTY; without even the // 00024 // implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR // 00025 // PURPOSE. See the GNU General Public License for more details. // 00026 // // 00027 // You should have received a copy of the GNU General Public License // 00028 // along with the iLab Neuromorphic Vision C++ Toolkit; if not, write // 00029 // to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, // 00030 // Boston, MA 02111-1307 USA. // 00031 // //////////////////////////////////////////////////////////////////// // 00032 // 00033 // Primary maintainer for this file: 00034 // $HeadURL: svn://isvn.usc.edu/software/invt/trunk/saliency/src/ObjRec/BayesianBiaser.C $ 00035 // $Id: BayesianBiaser.C 10794 2009-02-08 06:21:09Z itti $ 00036 // 00037 00038 #ifndef OBJREC_BAYESIANBIASER_C_DEFINED 00039 #define OBJREC_BAYESIANBIASER_C_DEFINED 00040 00041 #include "ObjRec/BayesianBiaser.H" 00042 00043 #include "Channels/ComplexChannel.H" 00044 #include "Channels/SingleChannel.H" 00045 00046 // ###################################################################### 00047 BayesianBiaser::BayesianBiaser(Bayes& b, 00048 const int target_class_id, 00049 const int distractor_class_id, 00050 const bool dobias) 00051 : 00052 itsBayesNetwork(b), 00053 itsClassT(target_class_id), 00054 itsClassD(distractor_class_id), 00055 itsDoBias(dobias), 00056 itsIndex(0) 00057 {} 00058 00059 // ###################################################################### 00060 BayesianBiaser::~BayesianBiaser() {} 00061 00062 // ###################################################################### 00063 void BayesianBiaser::visitChannelBase(ChannelBase& chan) 00064 { 00065 LFATAL("don't know how to handle %s", chan.tagName().c_str()); 00066 } 00067 00068 // ###################################################################### 00069 void BayesianBiaser::visitSingleChannel(SingleChannel& chan) 00070 { 00071 00072 LFATAL("This is being reworked..."); 00073 /* 00074 for (uint i = 0; i < chan.numSubmaps(); ++i) 00075 { 00076 if (itsDoBias) 00077 { 00078 if (itsClassT != -1) 00079 { 00080 const double mean = 00081 itsBayesNetwork.getMean(itsClassT, itsIndex); 00082 const double stdevSq = 00083 itsBayesNetwork.getStdevSq(itsClassT, itsIndex); 00084 00085 chan.setMean(i, mean); 00086 chan.setSigmaSq(i, stdevSq); 00087 } 00088 if (itsClassD != -1) 00089 { 00090 const double mean = 00091 itsBayesNetwork.getMean(itsClassD, itsIndex); 00092 const double stdevSq = 00093 itsBayesNetwork.getStdevSq(itsClassD, itsIndex); 00094 00095 chan.setDMean(i, mean); 00096 chan.setDSigmaSq(i, stdevSq); 00097 } 00098 } 00099 else 00100 { 00101 chan.setMean(i, 0.0F); 00102 chan.setSigmaSq(i, 0.0F); 00103 } 00104 00105 ++itsIndex; 00106 } 00107 */ 00108 //Install the submapAlg 00109 nub::ref<SubmapAlgorithmBiased> 00110 algo(new SubmapAlgorithmBiased(chan.getManager())); 00111 chan.setSubmapAlgorithm(algo); 00112 00113 } 00114 00115 // ###################################################################### 00116 void BayesianBiaser::visitComplexChannel(ComplexChannel& chan) 00117 { 00118 for (uint i = 0; i < chan.numChans(); ++i) 00119 chan.subChan(i)->accept(*this); 00120 } 00121 00122 // ###################################################################### 00123 /* So things look consistent in everyone's emacs... */ 00124 /* Local Variables: */ 00125 /* mode: c++ */ 00126 /* indent-tabs-mode: nil */ 00127 /* End: */ 00128 00129 #endif // OBJREC_BAYESIANBIASER_C_DEFINED