![Image Formation
f(x,y) = reflectance(x,y) * illumination(x,y)
Reflectance in [0,1], illumination in [0,inf]](https://image.slidesharecdn.com/lecture1-230314065158-c650cf99/85/Lecture1-pptx-2-320.jpg)
![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:
( , )
( , ) ( , )
( , )
r x y
f x y g x y
b x y
](https://image.slidesharecdn.com/lecture1-230314065158-c650cf99/85/Lecture1-pptx-5-320.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 D apart, we can write this as:
f[i ,j] = Quantize{ f(i D, j D) }
The image can now be represented as a matrix of integer values](https://image.slidesharecdn.com/lecture1-230314065158-c650cf99/85/Lecture1-pptx-7-320.jpg)
Lecture1.pptx
- 1. Image Processing Lecture 1 Introduction and Application
- 2. Image Formation f(x,y) = reflectance(x,y) * illumination(x,y) Reflectance in [0,1], illumination in [0,inf]
- 5. 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: ( , ) ( , ) ( , ) ( , ) r x y f x y g x y b x y
- 7. 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(i D, j D) } The image can now be represented as a matrix of integer values
- 8. 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?
- 11. Image Histograms
- 13. 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.
- 14. Programming Assignment #1 Easy stuff to get you started with Matlab Shobhit will hold your first tutorial Topics will be from next 2 lectures
- 18. Biometrics
- 19. Fingerprint Verification / Identification
- 20. Fingerprint Identification Research at UNR Minutiae Matching Delaunay Triangulation
- 22. Object Recognition Research reference view 1 reference view 2 novel view recognized
- 23. Indexing into Databases Shape content
- 24. Indexing into Databases (cont’d) Color, texture
- 25. Target Recognition Department of Defense (Army, Airforce, Navy)
- 26. Interpretation of aerial photography is a problem domain in both computer vision and registration. Interpretation of Aerial Photography
- 27. Autonomous Vehicles Land, Underwater, Space
- 29. Face Detection
- 30. Face Recognition
- 31. Face Detection/Recognition Research at UNR
- 33. Face Tracking
- 35. Hand Gesture Recognition Smart Human-Computer User Interfaces Sign Language Recognition
- 37. Medical Applications skin cancer breast cancer
- 38. Morphing
- 39. Inserting Artificial Objects into a Scene
- 40. Companies In this Field In India and came to IITK* Sarnoff Corporation Kritikal Solutions National Instruments GE Laboratories Ittiam, Bangalore Interra Systems, Noida Yahoo India (Multimedia Searching) nVidia Graphics, Pune (have high requirements) ADE Bangalore, DRDO
- 41. Links for Self Study and a little Play http://undergraduate.csse.uwa.edu.au/units/2 33.412/ http://www.netnam.vn/unescocourse/compute rvision/computer.htm Book: Digital Image Processing, 2nd Edition by Gonzalez and Woods, Prentice Hall
- 42. Good Luck !!!