43 lines
943 B
Markdown
43 lines
943 B
Markdown
# CSE559A Lecture 22
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## Continue on Robust Fitting of parametric models
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### RANSAC
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#### Definition: RANdom SAmple Consensus
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RANSAC is a method to fit a model to a set of data points.
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It is a non-deterministic algorithm that can be used to fit a model to a set of data points.
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Pros:
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- Simple and general
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- Applicable to many different problems
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- Often works well in practice
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Cons:
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- Lots of parameters to set
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- Number of iterations grows exponentially as outlier ratio increases
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- Can't always get a good initialization of the model based on the minimum number of samples.
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### Hough Transform
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Use point-line duality to find lines.
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In practice, we don't use (m,b) parameterization.
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Instead, we use polar parameterization:
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Algorithm outline:
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- Initialize accumulator $H$ to all zeros
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- For each feature point $(x_i, y_i)$
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- For $\theta=0$ to $180$
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- $\rho=x_i \cos \theta + y_i \sin \theta$
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Noise makes the peak fuzzy.
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