CS223B-L6-Boosting

CS223B-L6-Boosting - Stanford CS223B Computer Vision,...

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Stanford CS223B Computer Vision, Winter 2008/09 Lecture 6 Boosting Professor Sebastian Thrun CAs: Ethan Dreyfuss, Young Min Kim, Alex Teichman
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Output of Face Detector on Test Images Slide credit: Frank Dellaert, Paul Viola, Foryth&Ponce
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Gabor Filters
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Using Many Simple Features Viola Jones / Haar Features (Generalized) Haar Features: rectangular blocks, white or black 3 types of features: two rectangles: horizontal/vertical three rectangles four rectangles in 24x24 window: 180,000 possible features
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Sebastian Thrun CS223B Computer Vision, Winter 2009 Integral Image Def: The integral image at location ( x , y ), is the sum of the pixel values above and to the left of ( x , y ), inclusive. We can calculate the integral image representation of the image in a single pass. (x,y) s ( x , y ) = s ( x , y -1) + i ( x , y ) ii ( x , y ) = ii ( x -1, y ) + s( x , y ) (0,0) x y Slide credit: Gyozo Gidofalvi
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Sebastian Thrun CS223B Computer Vision, Winter 2009
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This note was uploaded on 11/30/2009 for the course CS 223B taught by Professor Thrun,s during the Winter '09 term at Stanford.

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CS223B-L6-Boosting - Stanford CS223B Computer Vision,...

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