52              int sizeof_param, 
int sizeof_model);
    55   int _estimate(
void* params, 
int param_c,
    56                 int support_limit, 
int max_rounds,
    59   int _refine(
void* params, 
int param_c,
    60               int support_limit, 
int max_rounds,
    61               void* model, 
char *inlier_mask = NULL);
    63   virtual void _doEstimate(
void** params, 
int param_c, 
void* model) {};
    64   virtual bool _doSupports(
void* param, 
void* model) { 
return false; };
    72   int _estimate(
int param_c,
    73                 int support_limit, 
int max_rounds,
    76   int _refine(
int param_c,
    77               int support_limit, 
int max_rounds,
    78               void* model, 
char *inlier_mask = NULL);
    80   virtual void _doEstimate(
int* params, 
int param_c, 
void* model) {};
    81   virtual bool _doSupports(
int param, 
void* model) { 
return false; };
    98   int estimateRequiredRounds(
float success_propability,
    99                              float inlier_percentage);
   170   template <
typename MODEL, 
typename PARAMETER>
   183     virtual void doEstimate(PARAMETER** params, 
int param_c, MODEL* model) = 0;
   196     virtual bool doSupports(PARAMETER* 
param, MODEL* model) = 0;
   202       doEstimate((PARAMETER**)params, param_c, (MODEL*)model);
   209       return doSupports((PARAMETER*)param, (MODEL*)model);
   226       : 
RansacImpl(min_params, max_params, sizeof(PARAMETER), sizeof(MODEL)) {}
   251                  int support_limit, 
int max_rounds,
   253       return _estimate(params, param_c, support_limit, max_rounds, model);
   275     int refine(PARAMETER* params, 
int param_c,
   276                int support_limit, 
int max_rounds,
   277                MODEL* model, 
char *inlier_mask = NULL) {
   278       return _refine(params, param_c, support_limit, max_rounds, model, inlier_mask);
   342   template <
typename MODEL>
   355     virtual void doEstimate(
int* params, 
int param_c, MODEL* model) = 0;
   368     virtual bool doSupports(
int param, MODEL* model) = 0;
   374       doEstimate(params, param_c, (MODEL*)model);
   381       return doSupports(param, (MODEL*)model);
   398       : 
RansacImpl(min_params, max_params, sizeof(MODEL)) {}
   422                  int support_limit, 
int max_rounds,
   424       return _estimate(param_c, support_limit, max_rounds, model);
   446                int support_limit, 
int max_rounds,
   447                MODEL* model, 
char *inlier_mask = NULL) {
   448       return _refine(param_c, support_limit, max_rounds, model, inlier_mask);
   455 #endif //__Ransac_h__ 
int refine(int param_c, int support_limit, int max_rounds, MODEL *model, char *inlier_mask=NULL)
Iteratively makes the estimated model better. 
Implementation of a general RANdom SAmple Consensus algorithm with implicit parameters. 
bool param(const std::string ¶m_name, T ¶m_val, const T &default_val)
virtual bool _doSupports(int param, void *model)
int estimate(int param_c, int support_limit, int max_rounds, MODEL *model)
Estimates a model from input data parameters. 
IndexRansac(int min_params, int max_params)
Initialize the algorithm. 
int refine(PARAMETER *params, int param_c, int support_limit, int max_rounds, MODEL *model, char *inlier_mask=NULL)
Iteratively makes the estimated model better. 
int estimate(PARAMETER *params, int param_c, int support_limit, int max_rounds, MODEL *model)
Estimates a model from input data parameters. 
virtual void _doEstimate(void **params, int param_c, void *model)
void _doEstimate(int *params, int param_c, void *model)
virtual void _doEstimate(int *params, int param_c, void *model)
Implementation of a general RANdom SAmple Consensus algorithm. 
bool _doSupports(void *param, void *model)
Ransac(int min_params, int max_params)
Initialize the algorithm. 
bool _doSupports(int param, void *model)
This file defines library export definitions, version numbers and build information. 
Internal implementation of RANSAC. Please use Ransac or IndexRansac. 
void _doEstimate(void **params, int param_c, void *model)