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# lecture14 - EECS 442 Computer vision Introduction to Image...

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EECS 442 – Computer vision Introduction to Image Filters Reading: [FP] Chapters: 7,8 Some slides of this lectures are courtesy of prof F. Li, prof S. Lazebnik, and various other lecturers • Convolution • Blurring • Sharpening • Multi-scale representation • Aliasing and sampling

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P = [x,y,z] From the 3D to 2D Image 3D world p = [x,y] •Let’s now focus on 2D •Extract building blocks
Extract useful building blocks

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• We can think of an image as a function, f , from R 2 to R: f ( x, y ) gives the intensity at position ( x, y ) Defined over a rectangle, with a finite range: f : [ a , b ] x [ c , d ] Æ [0,255] • A color image: ( , ) ( , ) ( , ) ( , ) r x y f x y g x y b x y = Source: S. Seitz Images as functions
Images as functions Source: S. Seitz x y f(x,y)

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• Images are usually digital (discrete): Sample the 2D space on a regular grid • The image can now be represented as a matrix of integer values 62 79 23 119 120 105 4 0 10 10 9 62 12 78 34 0 10 58 197 46 46 0 0 48 176 135 5 188 191 68 0 49 2 1 1 29 26 37 0 77 0 89 144 147 187 102 62 208 255 252 0 166 123 62 0 31 166 63 127 17 1 0 99 30 Source: S. Seitz pixel Images as functions
Filters • Linear filtering: – Form a new image whose pixels are a weighted sum of original pixel values use the same set of weights at each point Goal: Extract useful information from the images •Features (edges, corners, blobs…)

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