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# lec9 - Announcements Edge Detection Introduction to...

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1 CSE152, Spr 2011 Intro Computer Vision Edge Detection Introduction to Computer Vision CSE 152 Lecture 9 CSE152, Spr 2011 Intro Computer Vision Announcements Assignment 2 assigned and due Tuesday, May 3. Midterm: Thursday, May 5. CSE152, Spr 2011 Intro Computer Vision Convolution Image (I) Kernel (K) * Note: Typically Kernel is relatively small in vision applications. -2 1 1 2 -1 -1 CSE152, Spr 2011 Intro Computer Vision Convolution: R= K*I I R Kernel size is m+1 by m+1 m=2 CSE152, Spr 2011 Intro Computer Vision Properties of convolution Let f,g,h be images and * denote convolution Commutative: f*g=g*f Associative: f*(g*h)=(f*g)*h Linear: for scalars a & b and images f,g,h (a f +b g )* h =a( f * h )+b( g * h ) Differentiation rule CSE152, Spr 2011 Intro Computer Vision Gaussian Noise: sigma=1 Gaussian Noise: sigma=16

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2 CSE152, Spr 2011 Intro Computer Vision An Isotropic Gaussian The picture shows a smoothing kernel proportional to (which is a reasonable model of a circularly symmetric fuzzy blob) e x 2 + y 2 2 σ 2 CSE152, Spr 2011 Intro Computer Vision Smoothing with a Gaussian Kernel: CSE152, Spr 2011 Intro Computer Vision Other Types of Noise Impulsive noise randomly pick a pixel and randomly set to a value saturated version is called salt and pepper noise Quantization effects Often called noise although it is not statistical Unanticipated image structures Also often called noise although it is a real repeatable signal. CSE152, Spr 2011 Intro Computer Vision Median filters : example filters have width 5 : CSE152, Spr 2011 Intro Computer Vision Edges CSE152, Spr 2011 Intro Computer Vision Corners
3 CSE152, Spr 2011 Intro Computer Vision Physical causes of edges 1. Object boundaries 2. Surface normal discontinuities 3. Reflectance (albedo) discontinuities 4.

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lec9 - Announcements Edge Detection Introduction to...

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