This preview shows pages 1–7. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: 1 Filtering Most slides from Steve Seitz 2 What is an image? We can think of an image as a function, f , from R 2 to R: f ( x, y ) gives the intens ity at position ( x, y ) Realistically, we expect the image only to be defined over a rectangle, with a finite range: f : [ a , b ] x [ c , d ] [0,1] A color image is just three functions pasted together. We can write this as a vectorvalued function: ( , ) ( , ) ( , ) ( , ) r x y f x y g x y b x y = 3 Images as functions 4 What is a digital image? In computer vision we usually operate on digital ( dis crete ) images: Sample the 2D space on a regular grid Quantize each sample (round to nearest integer) If our samples are apart, we can write this as: f [ i , j ] = Quantize{ f ( i , j ) } The image can now be represented as a matrix of integer values 5 Image proces s ing An image proces s ing operation typically defines a new image g in terms of an existing image f. We can transform either the domain or the range of f . Range trans formation : Whats kinds of operations can this perform? 6 Image proces s ing Some operations preserve the range but change the domain of f : What kinds of operations can this perform? 7...
View Full
Document
 Spring '08
 staff

Click to edit the document details