lecture12 - CAP5415 Computer Vision Spring 2003 Khurram...

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CAP5415 Computer Vision Spring 2003 Khurram Hassan-Shafique
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MidTerm (February 20, 2003) Imaging Geometry Camera Modeling and Calibration Filtering and Convolution Edge Detection Deformable Contours
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Least Squares Fit Standard linear solution to a classical problem. Poor Model for vision applications. ( 29 b a x f b ax y , , = + = ( 29 [ ] - i i i b a x f y 2 , , Minimize
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Line fitting can be max. likelihood - but choice of model is important
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Maximum Likelihood ( 29 2 2 2 σ + + - = i i i c by ax L Maximize the Log likelihood function L Given constraint 1 2 2 = + b a
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Who came from which line? Assume we know how many lines there are - but which lines are they? easy, if we know who came from which line Strategies Incremental line fitting K-means
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Curve Fitting by Hough Transform Let y=f ( x, a ) be the chosen parameterization of a target curve. Discretize the intervals of variation of
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This note was uploaded on 06/12/2011 for the course CAP 5415 taught by Professor Staff during the Fall '08 term at University of Central Florida.

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lecture12 - CAP5415 Computer Vision Spring 2003 Khurram...

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