#include <edge_lines_likelihood.h>

 Edge_lines_likelihood (const Edge_segment_set &iimage_edge_segments, double angle_sigma, double dist_sigma, double prob_of_missing, double prob_of_noise, double max_distance, double max_angle, double collinear_distance_threshold) 
 Constructs a new Edge_lines_likelihood. More...


template<class T > 
double  compute_likelihood (T &model) 

template<class T , class U > 
double  compute_likelihood (T &model, U &camera) 
 Template function to compute the likelihood for a generic model under a given camera. The input model, with the camera, must be able to generate a model map and a set of model edges, or an edgeset (those are the two kind of features we can compute a likelihood for so far). Most of the time, you will likely write a specialization for this function, this is mostly a guideline. More...


double  operator() (std::vector< Model_edge > &model_edges) 
 Calculates and return the likelihood for a set of model edges. More...


void  draw_correspondence (Image &model_edge_image, Image &data_edge_image, const std::vector< Model_edge > &model_edges, double width) 

Edge_lines_likelihood::Edge_lines_likelihood 
( 
const Edge_segment_set & 
iimage_edge_segments, 


double 
angle_sigma, 


double 
dist_sigma, 


double 
prob_of_missing, 


double 
prob_of_noise, 


double 
max_distance, 


double 
max_angle, 


double 
collinear_distance_threshold 

) 
 
Constructs a new Edge_lines_likelihood.
 Parameters

dist_sigma  The standard diviation of the probility density function of the distance between the middle point of the image edge segment and the model edge 
angle_sigma  The sstandard diviation of the probability density function of the angle between the image edge segment and the model edge 
prob_of_missing  The probability of an edge element being missing 
prob_of_noise  The probability of an edge element being noise 
max_distance  The maximum distance allows to edge segments of a model edge within 
template<class T >
double kjb::Edge_lines_likelihood::compute_likelihood 
( 
T & 
model  ) 

Template function to compute the likelihood for a generic model. The input model must generate a set of of model edges,
 Parameters

model  The model to compute the likelihood for 
Template function to compute the likelihood for a generic model. The input model must be able to generate a model map and a set of model edges, or an edgeset (those are the two kind of features we can compute a likelihood for so far). Most of the time, you will likely write a specialization for this function, this is mostly a guideline
 Parameters

model  The model to compute the likelihood for 
mode  Mode for likelihood computation. 0 means that the likelihood will be computed by generating a model map and a set of edges from the model and the camera, 1 means that we will generate a set of edge points. 
template<class T , class U >
double kjb::Edge_lines_likelihood::compute_likelihood 
( 
T & 
model, 


U & 
camera 

) 
 
Template function to compute the likelihood for a generic model under a given camera. The input model, with the camera, must be able to generate a model map and a set of model edges, or an edgeset (those are the two kind of features we can compute a likelihood for so far). Most of the time, you will likely write a specialization for this function, this is mostly a guideline.
 Parameters

model  The model to compute the likelihood for 
camera  The camera to be used to render the model 
mode  Mode for likelihood computation. 0 means that the likelihood will be computed by generating a model map and a set of edges from the model and the camera, 1 means that we will generate a set of edge points. 
void Edge_lines_likelihood::draw_correspondence 
( 
Image & 
model_edge_image, 


Image & 
data_edge_image, 


const std::vector< Model_edge > & 
model_edges, 


double 
width 

) 
 
double Edge_lines_likelihood::operator() 
( 
std::vector< Model_edge > & 
model_edges  ) 

Calculates and return the likelihood for a set of model edges.
The documentation for this class was generated from the following files: