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Implementation of a general RANdom SAmple Consensus algorithm with implicit parameters. More...
#include <Ransac.h>
Public Member Functions | |
IndexRansac (int min_params, int max_params) | |
Initialize the algorithm. More... | |
int | estimate (int param_c, int support_limit, int max_rounds, MODEL *model) |
Estimates a model from input data parameters. More... | |
int | refine (int param_c, int support_limit, int max_rounds, MODEL *model, char *inlier_mask=NULL) |
Iteratively makes the estimated model better. More... | |
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int | estimateRequiredRounds (float success_propability, float inlier_percentage) |
How many rounds are needed for the Ransac to work. More... | |
Protected Member Functions | |
virtual void | doEstimate (int *params, int param_c, MODEL *model)=0 |
Creates a model estimate from a set of parameters. More... | |
virtual bool | doSupports (int param, MODEL *model)=0 |
Computes how well a parameters supports a model. More... | |
void | _doEstimate (int *params, int param_c, void *model) |
bool | _doSupports (int param, void *model) |
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RansacImpl (int min_params, int max_params, int sizeof_param, int sizeof_model) | |
int | _estimate (void *params, int param_c, int support_limit, int max_rounds, void *model) |
int | _refine (void *params, int param_c, int support_limit, int max_rounds, void *model, char *inlier_mask=NULL) |
virtual void | _doEstimate (void **params, int param_c, void *model) |
virtual bool | _doSupports (void *param, void *model) |
RansacImpl (int min_params, int max_params, int sizeof_model) | |
int | _estimate (int param_c, int support_limit, int max_rounds, void *model) |
int | _refine (int param_c, int support_limit, int max_rounds, void *model, char *inlier_mask=NULL) |
Additional Inherited Members | |
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void ** | samples |
void * | hypothesis |
int | min_params |
int | max_params |
int | sizeof_param |
int | sizeof_model |
int * | indices |
Implementation of a general RANdom SAmple Consensus algorithm with implicit parameters.
These parameters are accessed by indises. The benefit of this is that we avoid copying input data from an array into another. See Ransac class for more details.
Extending class must provide two methods:
Example fitting points to a line (compare this with the example in the Ransac class):
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inline |
Initialize the algorithm.
Uses at least min_params and at most max_params number of input data elements for model estimation.
Must be: max_params >= min_params
min_params | is the minimum number of parameters needed to create a model. |
max_params | is the maximum number of parameters to using in refining the model. |
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inlineprotectedvirtual |
Wrapper for templated parameters.
Reimplemented from RansacImpl.
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inlineprotectedvirtual |
Wrapper for templated parameters.
Reimplemented from RansacImpl.
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protectedpure virtual |
Creates a model estimate from a set of parameters.
The user must implement this method to compute model parameters from the input data.
params | An array of indises of sampled parameters. |
param_c | The number of parameter indises in the params array. |
model | Pointer to the model where to store the estimate. |
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protectedpure virtual |
Computes how well a parameters supports a model.
This method is used by the RANSAC algorithm to count how many parameters support the estimated model (inliers). Althought this is case specific, usually parameter supports the model when the distance from model prediction is not too far away from the parameter.
param | Index of the parameter to check. |
model | Pointer to the model to check the parameter against. |
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inline |
Estimates a model from input data parameters.
Randomly samples min_params number of input data elements from params array and chooses the model that has the largest set of supporting parameters (inliers) in the params array.
Note that this method always uses min_params number of parameters, that is, doEstimate method can be implemented to support only the minimum number of parameters unless refine method is used.
param_c | Number of parameters available in estimation. |
support_limit | The search is stopped if a model receives more support that this limit. |
max_rounds | How many different samples are tried before stopping the search. |
model | The estimated model is stored here. |
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inline |
Iteratively makes the estimated model better.
Starting with the estimated model, computes the model from all inlier parameters and interates until no new parameters support the model.
Note that this method uses up to max_params number of parameters, that is, doEstimate method must be implemented in such a way that it can estimate a model from a variable number of parameters.
param_c | Number of parameters available for estimation. |
support_limit | The search is stopped if a model receives more support that this limit. |
max_rounds | How many iterations of the refinement are run. |
model | The estimated model that is refined. |
inlier_mask | Byte array where 1 is stored for inliers and 0 for outliers. |