00001 /*!@file Beobot/beobot-GSnav-def.H 00002 Defined values for robot navigation using saliency and gist. 00003 Run beobot-GSnav-master at CPU_A to run Gist-Saliency model 00004 Run beobot-GSnav at CPU_B to run SIFT object recognition */ 00005 // //////////////////////////////////////////////////////////////////// // 00006 // The iLab Neuromorphic Vision C++ Toolkit - Copyright (C) 2001 by the // 00007 // University of Southern California (USC) and the iLab at USC. // 00008 // See http://iLab.usc.edu for information about this project. // 00009 // //////////////////////////////////////////////////////////////////// // 00010 // Major portions of the iLab Neuromorphic Vision Toolkit are protected // 00011 // under the U.S. patent ``Computation of Intrinsic Perceptual Saliency // 00012 // in Visual Environments, and Applications'' by Christof Koch and // 00013 // Laurent Itti, California Institute of Technology, 2001 (patent // 00014 // pending; application number 09/912,225 filed July 23, 2001; see // 00015 // http://pair.uspto.gov/cgi-bin/final/home.pl for current status). // 00016 // //////////////////////////////////////////////////////////////////// // 00017 // This file is part of the iLab Neuromorphic Vision C++ Toolkit. // 00018 // // 00019 // The iLab Neuromorphic Vision C++ Toolkit is free software; you can // 00020 // redistribute it and/or modify it under the terms of the GNU General // 00021 // Public License as published by the Free Software Foundation; either // 00022 // version 2 of the License, or (at your option) any later version. // 00023 // // 00024 // The iLab Neuromorphic Vision C++ Toolkit is distributed in the hope // 00025 // that it will be useful, but WITHOUT ANY WARRANTY; without even the // 00026 // implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR // 00027 // PURPOSE. See the GNU General Public License for more details. // 00028 // // 00029 // You should have received a copy of the GNU General Public License // 00030 // along with the iLab Neuromorphic Vision C++ Toolkit; if not, write // 00031 // to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, // 00032 // Boston, MA 02111-1307 USA. // 00033 // //////////////////////////////////////////////////////////////////// // 00034 // 00035 // Primary maintainer for this file: Christian Siagian <siagian@usc.edu> 00036 // $HeadURL: svn://isvn.usc.edu/software/invt/trunk/saliency/src/Beobot/beobot-GSnav-def.H $ 00037 // $Id: beobot-GSnav-def.H 12552 2010-01-13 23:05:00Z siagian $ 00038 // 00039 ///////////////////////////////////////////////////////////////////////// 00040 // 00041 // This is an on-going project for biologically-plausible 00042 // mobile-robotics navigation. 00043 // It accepts any inputs: video clip <input.mpg>, camera feed, frames. 00044 // 00045 // The system uses Gist to recognize places and saliency 00046 // to get better localization within the place 00047 // The program also is made to be streamline for fast processing using 00048 // parallel computation. That is the V1 features of different channels are 00049 // computed in parallel 00050 // 00051 // Currently it is able to recognize places through the use of gist features. 00052 // The place classifier uses a neural networks, 00053 // passed in a form of <input_train.txt> - 00054 // the same file is used in the training phase by train-FFN.C. 00055 // 00056 // Related files of interest: GistEstimator.C (and .H) and 00057 // GistEstimatorConfigurator.C (and .H) used by Brain.C to compute 00058 // the gist features. 00059 // test-Gist.C uses GistEstimator to extract gist features from an image. 00060 // 00061 // In parallel we use saliency to get a better spatial resolution 00062 // as well as better place accuracy. The saliency model is used to obtain 00063 // salient locations. We then use ShapeEstimator algorithm to segment out 00064 // the sub-region to get a landmark. Using SIFT we can identify the object, 00065 // create a database, etc. 00066 // 00067 // for localization, path planning we perform landmark-hopping 00068 // to get to the final destination 00069 00070 #ifndef BEOBOT_BEOBOT_GSNAV_DEF_DEFINED 00071 #define BEOBOT_BEOBOT_GSNAV_DEF_DEFINED 00072 00073 #define VENTRAL_NODE 0 00074 #define DORSAL_NODE 1 00075 00076 #define INIT_COMM 10000 00077 #define INIT_DONE 10001 00078 #define SEARCH_LM 10002 00079 #define SEARCH_LM_RES 10003 00080 #define TRACK_LM 10004 00081 #define TRACK_LM_RES 10005 00082 #define ABORT 10006 00083 00084 #define TRAIN_MODE 20000 00085 #define TRAIN_X_MODE 20001 00086 #define TEST_MODE 20002 00087 00088 #define SEARCH_NOT_DONE 30000 00089 #define LOCALIZED 30001 00090 #define NOT_LOCALIZED 30002 00091 00092 #define BC_NO_SIGNAL 40000 00093 #define BC_QUIT_SIGNAL 40001 00094 00095 #define FILE_INPUT 50000 00096 #define CAMERA_INPUT 50001 00097 00098 //! percentage of images that a landmark has to be matches 00099 //! so that we can combine it with the other landmark 00100 #define NMATCH_THRESHOLD 0.25f 00101 00102 //! number of frames over which frame rate is computed 00103 #define NAVG 20 00104 00105 //! Factor to display the sm values as greyscale: 00106 #define SMFAC 0.05F 00107 00108 //! amount of time alloted to search through landmark db 00109 #define SEARCH_TIME_LIMIT 15 //!< 15 frames 00110 00111 #endif 00112 00113 // ###################################################################### 00114 /* So things look consistent in everyone's emacs... */ 00115 /* Local Variables: */ 00116 /* indent-tabs-mode: nil */ 00117 /* End: */
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