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Comprehensive Image Processing Lecture: Introduction, Applications, and Techniques

This lecture covers the basics of image processing, including image formation, Fourier Transform theory, color manipulation, and more. Learn about digital image representation and processing operations.

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Comprehensive Image Processing Lecture: Introduction, Applications, and Techniques

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  1. Activity 2: Peer Assessment ActivityIIT Bombay FDP101x : October 2018 by VIJAYARANI R

  2. Image Processing Lecture 1 Introduction and Application by VIJAYARANI R

  3. Course Structure • Introduction to Image Processing, Application and Prospects (Today) • Introduction, Image formation, camera models and perspective geometry • Fourier Transform theory , Convolution and Correlation • Color, Image enhancement  Techniques • Binary images: thresholding, moments, topology 

  4. Concepts Covered Need for Image Processing • Human Perception • Filtering • Image Enhancement • Image deblurring

  5. Image Formation f(x,y) = reflectance(x,y) * illumination(x,y) Reflectance in [0,1], illumination in [0,inf]

  6. Sampling and Quantization

  7. Sampling and Quantization

  8. What is an image? • We can think of an image as a function, f, from R2 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:

  9. Images as functions

  10. 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 D apart, we can write this as: f[i ,j] = Quantize{ f(iD, jD) } • The image can now be represented as a matrix of integer values

  11. 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?

  12. Log

  13. Image Enhancement

  14. Contrast Streching

  15. 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.

  16. Reflection Spot: Why do we need image Processing? It is motivated by two major application • Improvement of pictorial information for human perception • Image processing for autonomous machine application • Efficient storage and transmission

  17. LbD1 Q1. What effect is caused by under sampling? a)Summation b) Smoothing c) Sharpening d) Aliasing Ans.d) aliasing

  18. LbD1 Q2. In frequency domain, what is the equivalent operation of product of two functions in spatial domain. a) Correlation b) Convolution c) Fourier transform d) Fast Fourier transform Ans.b) Convolution

  19. Feedback • In this video and PPT is very useful? a) yes b) No

  20. Good Luck !!!

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