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# Class Note for EECS 841 with Professor Potetz at KU 8

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COURSE
PROF.
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TYPE
Class Notes
PAGES
5
WORDS
KARMA
25 ?

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This 5 page Class Notes was uploaded by an elite notetaker on Friday February 6, 2015. The Class Notes belongs to a course at Kansas taught by a professor in Fall. Since its upload, it has received 18 views.

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Date Created: 02/06/15
Color Image Range Image Riegl LMSZB60 Relative Brightness An Ecological Explanation and Neurophysiological Basis Friday Sept 26 1200120pm Fraser 547 Deformable Template for Matching Efficient Energy Minimization Matching based on snakes and gradient descent Total energy is the sum of energies at each triangle 1 Requires an initial guess that is close to the real object Can get stuck in local minima Is not attracted to distant features Triangulated deformable templates address this 3th x Every planar graph has a dual EEK lid The dual of a template should be singly connected Dynamic Programming can be used to minimize energy in template deformation 0n63 time g Number of possible vertexlocations L5 n Number of triangles Efficient Energy Minimization Efficient Energy Minimization Total energy is the sum of energies at each triangle Total energy is the sum of energies at each triangle 1 Every planar graph has a dual Every planar graph has a dual The dual of a template should be singly connected The dual of a template should be singly connected Dynamic Programming can be used to minimize energy in Dynamic Programming can be used to minimize energy in 0n63 time gNumber of possible vertex locations 0n63 time g Number of possible vertexlocations n Number of triangles 1 n Number of triangles Ef cient Energy Minimization Total energy is the sum of energies at each triangle 6 10 9 Every planar graph has a dual The dual of a template should be singly connected Dynamic Programming can be used to minimize energy in OnG3 time gzNumber of possible vertex locations n Number oftriangles Suggested Reading Forsyth amp Ponce quotComputer Vision A Modern Approachquot Chapter 76 Forsyth amp Ponce quotComputer Vision A Modern Approachquot Chapter 221 223 Edge Detection Isn t Perfect Perceptually salient contours are not always near sharp intensity changes Convolution Convolution RED 393quot Filtering RFD 2 IE M Filtering Filtering Filtering Filtering Ii E Ii E Images as Points Feature detection Matching involves deciding how far apart they are in this space Negative Sum Squared Error SSEW 2mm gm 2321 m2 13339 Negative Sum Squared Error SSW y 2mm gar 2321 m2 13339 Negative Sum Squared Error SSEW 2W4 gm 2321 m2 13339 Rsu E Negative Sum Squared Error SSW y 20 gm z y m2 13339 W onessizetemplate NSSE 2filter2template im filter2w im Z Images as Points Feature detection i x1 Points in KXL dimensional space Matching involves deciding how far apart they are in this space Normalized Correlation 1717uvlcosd 3080H RFD ml Normalized Correlation uU lullvlcosd Rzu ml Normalized Correlation u lullvlcosd Normalized Correlation uU lullvlcosd W onessizetemplate NSSE filter2template im sqrtfilter2W imA2 40 Application 1 Template Matching 7 A 4 6 Stereo Correspondence Depth can be recovered through the horizontal disparity displacement of points in the two images

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