Computer Vision ECE 516
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This 3 page Class Notes was uploaded by Roel Green on Wednesday September 23, 2015. The Class Notes belongs to ECE 516 at University of New Mexico taught by Pradeep Sen in Fall. Since its upload, it has received 65 views. For similar materials see /class/212139/ece-516-university-of-new-mexico in Engineering Electrical & Compu at University of New Mexico.
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Date Created: 09/23/15
ECE 516cs 532 Computer Vision mm mum P vaueep Sen Advanced Gvapmcs Lab Annauncements No class next week I ll be out oftown Eranum mnuwm mp9 cmmms m Taday Camera models Calibration A i WW Caardinate systems A i wquot mquot Hamageneaus caardinates musmmnervw 7 mp9quot Transfarming caardinate systems A 7 zsmwm Camera models Intrinsic parameters We need to come up with a mapping Start by modeling transformation using a transformation between points in the real normalized image plane located a unit wor and points in the image distance from the pinhole This involves two kinds oftransformations Extrinsic getting from the coordinate system of the world to the coordinate system ofthe camera ntrinsic getting from the coordinate system of the camera to the coordinate system ofthe image ggmmwww A3 awnWm meme m mp9quot cmmms m Real image plane Intrinsic camera parameters Located at distance f 1 from the pinhole Pixels rectangular instead ofsquare Sensor might be skewed from manufacturing resulting in axis not being orthogonal zsmwww m ElmSS Wm m Intrinsic camera parameters Extrinsic camera parameters The camera reference ame is different from the world 39ame gmw Wmquot mp9quot cmmms m Camera parameters Ifthere is no skew in the sensor there are 4 intrinsic camera parameters at B uo v0 There are 6 extrinsic camera parameters 3 for rotation and 3 for translation 39 seems05532 Computerva mm s Eiass 54mm 2mg Properties of projection matrices Reading Forsyth Ch 2 and 3 seems05532 Computerva mm s Eiass 54mm 2mg 5mm 05532 comm m m m ev s Eiass 54mm 2mm