vision14

vision14 - Computer Vision Calibration MarcPollefeys...

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Computer Vision Calibration Marc Pollefeys COMP 256 Some slides/illustrations from Ponce, Hartley & Zisserman
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Computer Vision 2 Jan 16/18 - Introduction Jan 23/25 Cameras Radiometry Jan 30/Feb1 Color Feb 6/8 Texture Feb 13/15 Multi-View Geometry Stereo Feb 20/22 Optical flow Project proposals Feb27/Mar1 Affine SfM Projective SfM Mar 6/8 Camera Calibration Segmentation Mar 13/15 Springbreak Springbreak Mar 20/22 Fitting Prob. Segmentation Mar 27/29 Silhouettes and  Photoconsistency Linear tracking Apr 3/5 Project Update Non-linear Tracking Apr 10/12 Object Recognition Object Recognition Apr 17/19 Range data Range data Apr 24/26 Final project Final project  Tentative class schedule
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Computer Vision 3 Previously Hierarchy of 3D transformations v T v t A Projective 15dof Affine 12dof Similarity 7dof Euclidean 6dof Intersection and tangency Parallellism of planes, Volume ratios, centroids, The plane at infinity π Angles, ratios of length The absolute conic Ω Volume 1 0 t A T 1 0 t R T s 1 0 t R T
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Computer Vision 4 Camera calibration Compute relation between pixels and rays in space ?
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Computer Vision 5 Pinhole camera
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Computer Vision 6 Pinhole camera model T T Z fY Z fX Z Y X ) / , / ( ) , , ( t = 1 0 1 0 0 1 Z Y X f f Z fY fX Z Y X s linear projection in homogeneous coordinates! homogeneous coordinates non-homogeneous coordinates
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Computer Vision 7 Pinhole camera model = 1 0 1 0 0 Z Y X f f Z fY fX = 1 0 1 0 1 0 1 1 Z Y X f f Z fY fX PX x = [ ] 0 | I ) 1 , , ( diag P f f =
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Computer Vision 8 Principal point offset T y x T p Z fY p Z fX Z Y X ) / , / ( ) , , ( + + principal point T y x p p ) , ( = + + 1 0 1 0 0 1 Z Y X p f p f Z Zp fY Zp fX Z Y X y x x x s
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Computer Vision 9 Principal point offset = + + 1
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vision14 - Computer Vision Calibration MarcPollefeys...

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