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 62 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 Ciass7 Februavv 11 June P vaueep Sen Advanced cvapmcs Lab Annauncements HW1 due Monday February 16 Eranum mnuwm Jams WWW m In the pragramming assignment Last time Color quotquotm39mm nnlmal VE DIS t helght rmr hum m nvls 5315 WWW can mm m Ian 4mm m9 Taday Averaging lter Linear lters Forsyth Ch 7 igmm wm museum ng 7 ifmwmm Bushman ms Convolution 1D ip shi multiply I 2D musmmnervw mp9quot can 7mm m Gaussian filter h A nviginal avevage may gaussian tiller Eranum mm m mp9quot cmramw m Finite differences filter nngmai derivative m y derivative in x g musmmnervw quotw 7 mp9quot cmwmuw m Linear shiftinvariant systems Linearity Superposition Scal39n Shiftinvariance the response ofa translated stimulus is just the translation of the response to that stimulus Now let s see how to apply these assumptions to discrete 1D signals EL 511 Wm cmramaw m Discrete 1D signals Input vectorf Basis eU a vector that has a1 in a single component and zero elsewhere Shiltoperator You can write f as a weighted sum of shi ed ns basis functlo Now suppose that you pass fthrough LSI sys em R Therefore ifyou know Reg you can determine Rf for any f musmmnervw mp9quot can 7mm m Discrete 1D signals If Reg is g then Rf g f 7 mgfmwm cmramaw m Continuous domain Shift operatoris similar Need a new basis function 39 seems05532 Computerva mm s m 35577FebmiNM 2mg Properties of convolution Reading Forsyth Ch 7 35577FebmiNM 2mg Commutative f g g f Associative f g h f Q h 5mm 05532 comm m on mm s Eiass7rFebmiNM 2mg
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