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spatial.slides.printing.6

spatial.slides.printing.6 - Spatial Filtering Spatial...

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Spatial Filtering Spatial Filtering CS 450: Introduction to Digital Signal and Image Processing Bryan Morse BYU Computer Science Spatial Filtering Introduction Neighborhood Operations Output is a function of a pixel’s value and its neighbors Example (8-connected neighbors): g ( x , y ) = Op 0 @ f ( x - 1 , y - 1 ) , f ( x , y - 1 ) , f ( x + 1 , y - 1 ) , f ( x - 1 , y ) , f ( x , y ) , f ( x + 1 , y ) , f ( x - 1 , y + 1 ) , f ( x , y + 1 ) , f ( x + 1 , y + 1 ) 1 A Possible operations: sum, weighted sum, average, weighted average, min, max, median, ... Spatial Filtering Introduction Spatial Filtering The most common neighborhood operation is to multiply each of the pixels in the neighborhood by a weight and add them together. The local weights are sometimes called a mask or kernel . Local Neighborhood Mask f(x-1,y-1) f(x,y-1) f(x+1,y-1) f(x-1,y) f(x,y) f(x+1,y) f(x-1,y+1) f(x,y+1) f(x+1,y+1) w(-1,-1) w(0,-1) w(1,-1) w(-1,0) w(0,0) w(1,0) w(-1,1) w(0,1) w(1,1) g ( x , y ) = 1 s = - 1 1 t = - 1 w ( s , t ) f ( x + s , y + t ) Spatial Filtering Introduction Spatial Filtering Spatial Filtering Introduction Convolution Spatial filtering is often referred to as convolution .
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