ransac experimentation
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RANSAC experimentation. Slides by Marc van Kreveld. Experimental research. Research in computer science may result in: definitions of new, useful structures or concepts new algorithms to solve problems Further knowledge on the new structure, concept or algorithm can be obtained by: - PowerPoint PPT PresentationTRANSCRIPT
RANSAC experimentation
Slides by Marc van Kreveld
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Experimental research
• Research in computer science may result in:– definitions of new, useful structures or concepts– new algorithms to solve problems
• Further knowledge on the new structure, concept or algorithm can be obtained by:– proofs of properties, complexities, efficiency of structures,
concepts or algorithms– experiments to answer research questions where proofs
cannot be given, e.g. running times on real-World data; user studies to determine aesthetics or perceived difficulty
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Common sense tips in experiments
• Know exactly what you are doing and why• Make sure to fix some specific research questions
at the start• Do not even think about trying to answer more
questions, or hoping to get more information, during the design phase. Concentrate on the chosen questions only
• Design your data and your experiment to answer the questions as well as reasonably possible
• Make sure your experiment can be replicated4
Common sense tips in experiments
• Is your data sufficient in quality and size?• Are there enough experiments?• Are there enough different experiments to establish
relationships?• Should I report all outcomes, a summary (histogram,
mean, standard deviation, …)?• How strong can I / how weak must I phrase my
observations and conclusions?
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Visualization of results
• Table with numbers• Scatterplot• Graph• Chart• Pie diagram• Bar chart
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Visualization of results
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Visualization of results
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Visualization of results
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Visualization of results
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Visualization of results
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Visualization of results
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Visualization of results
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RANSAC
1. Choose a threshold distance d2. For #iterations do– Choose 2 points, make line L– For each point q in P, test if q lies within distance d from L
If yes, increase the support of L by 1– If L has higher support than the highest-support line
found so far, remember L and its support
3. Return L as the line with most points near it
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Goal: Given a set of points P, some of which lie on a line L*. Find line L*
RANSAC
• For testing whether a point q supports a line L, we do not actually compute the distance from q to L
• Instead, we generate two lines at distance d from L• Then we test for each point whether it lies below the
upper and above the lower line
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RANSAC
• How large should the threshold distance d be?• How many iterations should we do to have a high
probability of finding the line with highest support?
the threshold distance is related to the measurement error (~5 cm) and the flatness of the surface
the number of iterations depends on the inlier-outlier ratio and with how much probability we want to find the best line
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RANSAC, iterations
• Suppose we want to have 95% probability, p=0.95, of finding the line
• Suppose there are k points on the line (inliers) and n points in total
• Then the probability of choosing 2 points on the line is (k/n)2
• The probability of never selecting 2 points on the line in r iterations is ( 1 – (k/n)2 )r
• The probability of finding the line in r iterations is 1 – ( 1 – (k/n)2 )r
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RANSAC, iterations
• So we want 1 – ( 1 – (k/n)2 )r > p
( 1 – (k/n)2 )r < 1 – plog ( 1 – (k/n)2 )r < log (1 – p)r log ( 1 – (k/n)2 ) < log (1 – p)
r > log (1 – p) / log ( 1 – (k/n)2 )
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RANSAC, iterations
• Examples: – if 10% of the points lie on the line and we want to find it
with 95% certainty, we need nearly 300 iterations– If 5% of the points lie on the line and we want to find it
with 95% certainty, we need nearly 1200 iterations– if 10% of the points lie on the line and we want to find it
with 90% certainty, we need nearly 230 iterations
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Practical 3: RANSAC with Noise
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Practical 3: RANSAC with Noise
• 1. Does the number of iterations required to find the best plane increase when noise is present, and how does it increase in the amount of noise?
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Practical 3: RANSAC with Noise
• 1. Does the number of iterations required to find the best plane increase when noise is present, and how does it increase in the amount of noise?
• 2. When noise is present, does the facet shape influence the number of iterations that is needed, and in what way?
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Experimental research
• http://en.wikipedia.org/wiki/Design_of_experiments• http://en.wikipedia.org/wiki/Charts
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