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CSE559A Lecture 22

Continue on Robust Fitting of parametric models

RANSAC

Definition: RANdom SAmple Consensus

RANSAC is a method to fit a model to a set of data points.

It is a non-deterministic algorithm that can be used to fit a model to a set of data points.

Pros:

• Simple and general
• Applicable to many different problems
• Often works well in practice

Cons:

• Lots of parameters to set
• Number of iterations grows exponentially as outlier ratio increases
• Can't always get a good initialization of the model based on the minimum number of samples.

Hough Transform

Use point-line duality to find lines.

In practice, we don't use (m,b) parameterization.

Instead, we use polar parameterization:

Algorithm outline:

• Initialize accumulator H to all zeros
• For each feature point (x_i, y_i)
  • For \theta=0 to 180
  • \rho=x_i \cos \theta + y_i \sin \theta

Noise makes the peak fuzzy.