CRF Model
The Binary LN Model described in the bachelor thesis "Maximum Likelihood Learning And Inference In Conditional Random Fields" by Iulian Vlad Serban, University of Copenhagen, 2012.
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#include <vector>
#include <stdlib.h>
#include <math.h>
Go to the source code of this file.
Classes | |
class | CRF::CRFModel |
Typedefs | |
typedef std::vector< double > | double_vector |
typedef std::vector < std::vector< double > > | double_double_vector |
typedef std::vector< size_t > | int_vector |
typedef std::vector < std::vector< size_t > > | int_int_vector |
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details at http://www.gnu.org/copyleft/gpl.html
The CRFModel_ExactLikelihood is a discrete image-denoising conditional random field model using exact likelihood to calculate derivatives.
All input images are expected to be of the format std::vector< std::vector<double> >, where the first index indicates the horizontal location and the second the vertical location of the pixel relative to origin (0,0) in the top left corner. The values are doubles representing intensities of gray.
All ouput images are expected to be of the format std::vector< std::vector<unsigned int> >, where the first index indicates the horizontal location and the second the vertical location of the pixel relative to origin (0,0) in the top left corner. The values are int representing intensities of gray - most often just {0,1} represnting black and white.