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![Image Formation f(x,y) = reflectance(x,y) * illumination(x,y) Reflectance in [0,1], illumination in [0,inf]](https://image.slidesharecdn.com/pointprocessing-100216100011-phpapp01/75/Point-Processing-3-2048.jpg)
![What is an image? 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 ) 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 “vector-valued” function:](https://image.slidesharecdn.com/pointprocessing-100216100011-phpapp01/75/Point-Processing-6-2048.jpg)
![What is a digital image? We usually operate on digital ( discrete ) 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](https://image.slidesharecdn.com/pointprocessing-100216100011-phpapp01/75/Point-Processing-8-2048.jpg)
![Image Formation f(x,y) = reflectance(x,y) * illumination(x,y) Reflectance in [0,1], illumination in [0,inf]](https://crownmelresort.com/image.slidesharecdn.com/pointprocessing-100216100011-phpapp01/75/Point-Processing-3-2048.jpg)
![What is an image? 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 ) 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 “vector-valued” function:](https://crownmelresort.com/image.slidesharecdn.com/pointprocessing-100216100011-phpapp01/75/Point-Processing-6-2048.jpg)
![What is a digital image? We usually operate on digital ( discrete ) 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](https://crownmelresort.com/image.slidesharecdn.com/pointprocessing-100216100011-phpapp01/75/Point-Processing-8-2048.jpg)
Uploaded byGowrishankar S
Point Processing
This document discusses image processing and digital images. It covers topics like: - How images can be represented as functions that map pixel locations to intensity values. - The process of sampling and quantizing to convert a continuous image into a digital image represented as a matrix of integer values. - Different types of image processing operations including point processing that transform pixel values independently and neighborhood processing that considers pixel locations. - Specific point processing techniques like negative, log transformations, and gamma correction. - Image enhancement methods like contrast stretching, histograms, and histogram equalization.
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Point Processing
- 1. Image Processing I15-463: Rendering and Image Processing Alexei Efros … with most slides shamelessly stolen from Steve Seitz and Gonzalez & Woods
- 2. Today Image FormationRange Transformations Point Processing Programming Assignment #1 OUT Reading for this week: Gonzalez & Woods, Ch. 3 Forsyth & Ponce, Ch. 7
- 3. Image Formation f(x,y)= reflectance(x,y) * illumination(x,y) Reflectance in [0,1], illumination in [0,inf]
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- 6. What is animage? 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 ) 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 “vector-valued” function:
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- 8. What is adigital image? We usually operate on digital ( discrete ) 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
- 9. Image processing An image processing operation typically defines a new image g in terms of an existing image f. We can transform either the range of f . Or the domain of f : What kinds of operations can each perform?
- 10. Point Processing Thesimplest kind of range transformations are these independent of position x,y: g = t(f) This is called point processing. What can they do? What’s the form of t ? Important: every pixel for himself – spatial information completely lost!
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- 15. Gamma Correction GammaMeasuring Applet: http:// www.cs.berkeley.edu/~efros/java/gamma/gamma.html
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- 23. Neighborhood Processing (filtering)Q: What happens if I reshuffle all pixels within the image? A: It’s histogram won’t change. No point processing will be affected… Need spatial information to capture this.
- 24. Programming Assignment #1Easy stuff to get you started with Matlab James will hold tutorial this week Distance Functions SSD Normalized Correlation Bells and Whistles Point Processing (color?) Neighborhood Processing Using your data (3 copies!) Using your data (other images)