# 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.