CSE 473/573 RANSAC & Least Squares Devansh Arpit.

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Presentation transcript:

CSE 473/573 RANSAC & Least Squares Devansh Arpit

Mathematical Models Compact Understanding of the World Input Prediction Model Playing Golf

Mathematical Models - Example Face Recognition with varying expressions Too Easy…

Mathematical Models Face Recognition with varying expressions Learn Model Feature Space

I. RANSAC Random Sample Consensus Used for Parametric Matching/Model Fitting Applications:

Line Fitting Fit the best possible Line to these points Brute Force Search – 2 N possibilities!!! Not Feasible Better Strategy?

How RANSAC Works Random Search – Much Faster!!!

Line Fitting using RANSAC Iteration 1

Line Fitting using RANSAC Iteration 1

Line Fitting using RANSAC Iteration 1

Line Fitting using RANSAC Iteration 2

Line Fitting using RANSAC Iteration 2

Line Fitting using RANSAC Iteration 2

Line Fitting using RANSAC … Iteration 5

Line Fitting using RANSAC Iteration 5

Line Fitting using RANSAC Iteration 5

Why RANSAC Works? Inliers vs Outliers P(selecting outliers) = 17 C 2 / 27 C C 1 10 C 1 / 27 C 2 = 0.48

Why RANSAC Works? Inliers vs Outliers P(selecting outliers) 17 C 2 / 27 C C 1 10 C 1 / 27 C 2 = 0.48 After 5 iterations… P(selecting outliers) = (0.48) 5 = 0.026

Why RANSAC Works? In general: p = 1 – (1 - w n ) k Where, p = probability for selecting inliers w = ratio of inliers to total #points n = minimum #points required (for line = 2, circle =3) k = #iterations

RANSAC Algorithms

II. Least Squares Fitting Curves/Learning Data Manifolds Fitting Line Fitting Quadratic Curve Fitting Higher Degree Polynomials Learning Manifolds

Line Fitting Goal: Find a line that best explains the observed data Target: y i Data: x i Line parameter: w,b Line Model: y i = w x i + b Fitting Line

Line Fitting Line Model: y i = w x i + b Too many samples! Minimize error: min. ∑(y i - w x i + b) 2 Fitting Line w,b i=1 N

#Samples(m) vs #Model-Parameters(n) Case 1 (m=n): Unique Solution w=X\y No least square requires

#Samples(m) vs #Model-Parameters(n) Case 2 (m>n): Over-determined system of equations No Solution exists! Hence, we minimize error (fitting)

#Samples(m) vs #Model-Parameters(n) Case 3 (m<n): Under-determined system of equations Infinite Solutions exist! Which one to choose?