CS223B-L4-Calibration-1

CS223B-L4-Calibration-1 - Stanford CS223B Computer Vision,...

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Sebastian Thrun CS223B Computer Vision, Winter 2009 Stanford CS223B Computer Vision, Winter 2008/09 Lecture 4 Camera Calibration Professor Sebastian Thrun CAs: Ethan Dreyfuss, Young Min Kim, Alex Teichman
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Today’s Goals Calibration: Problem definition Solution by nonlinear Least Squares Solution via Singular Value Decomposition Homogeneous Coordinates Distortion Calibration Software
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Camera Calibration Feature Extraction Perspective Equations
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Perspective Projection, Remember? f Z Z X f x - = X O x
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Intrinsic Camera Parameters Determine the intrinsic parameters of a camera (with lens) What are Intrinsic Parameters ? (can you name 7?)
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Intrinsic Parameters f Z X O y x o o , center image y x s s , size pixel f length focal Z X f x - = 2 1 , distortion lens k k
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Intrinsic Camera Parameters Intrinsic Parameters: Focal Length f – Pixel size s x , s y – Image center o x , o y – (Nonlinear radial distortion coefficients k 1 , k 2 …) Calibration = Determine the intrinsic parameters of a camera
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Why Intrinsic Parameters Matter x x o Z X s f x + - = Z X f x - = y y o Z Y s f y + - = Z Y f y - =
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Questions Can we determine the intrinsic parameters by exposing the camera to many known objects? If so, How often do we have to see the object? How many features on the object do we need?
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Example Calibration Pattern Calibration Pattern: Object with features of known size/geometry
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Harris Corner Detector (see slides in last lecture)
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Intrinsics and Extrinsics Intrinsics: Focal Length f – Pixel size s x , s y – Image center o x , o y Extrinsics: Location and orientation of k -th calib. pattern: φ , ϕ , ψ , T
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Calibration Known calibration object, many views Compute intrinsics and extrinsics (Retain intrinsics, toss extrinsics)
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Why Tilt the Board?
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Experiment 1: Parallel Board
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Sebastian Thrun CS223B Computer Vision, Winter 2009 30cm 10cm 20cm Projective Perspective of Parallel Board Z X f x - =
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Experiment 2: Tilted Board
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Sebastian Thrun CS223B Computer Vision, Winter 2009 30cm 10cm 20cm 500cm 50cm 100cm Projective Perspective of Tilted Board
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Perspective Camera Model Step 1: Transform into camera coordinates Step 2: Transform into image coordinates ) , , , , ( ~ ~ ~ T Z Y X f Z Y X W W W C C C ψ ϕ φ =
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This note was uploaded on 01/24/2010 for the course CS 223B taught by Professor Thrun,s during the Winter '09 term at Stanford.

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CS223B-L4-Calibration-1 - Stanford CS223B Computer Vision,...

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