Click or drag to resize
Accord.NET (logo)

RansacFundamentalEstimator Class

RANSAC Robust Fundamental Matrix Estimator.
Inheritance Hierarchy

Namespace:  Accord.Imaging
Assembly:  Accord.Vision (in Accord.Vision.dll) Version: 3.7.0
public class RansacFundamentalEstimator
Request Example View Source

The RansacFundamentalEstimator type exposes the following members.

Public methodRansacFundamentalEstimator
Creates a new RANSAC homography estimator.
Public propertyInliers
Gets the final set of inliers detected by RANSAC.
Public propertyRansac
Gets the RANSAC estimator used.
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Public methodEstimate(PointF, PointF)
Matches two sets of points using RANSAC.
Public methodEstimate(IntPoint, IntPoint)
Matches two sets of points using RANSAC.
Public methodEstimate(Point, Point)
Matches two sets of points using RANSAC.
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Extension Methods
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodToT
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)

Fitting a fundamental using RANSAC is pretty straightforward. Being a iterative method, in a single iteration a random sample of four correspondences is selected from the given correspondence points and a transformation F is then computed from those points.

After a given number of iterations, the iteration which produced the largest number of inliers is then selected as the best estimation for H.


  • P. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing. School of Computer Science and Software Engineering, The University of Western Australia. Available in:
  • E. Dubrofsky. Homography Estimation. Master thesis. Available on:

See Also