Your SlideShare is downloading. ×
0
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Lecture1
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Lecture1

151

Published on

Published in: Technology, Art & Photos
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
151
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • As opposed to [0..255]
  • Render with scanalyze????
  • Use photoshop to make something grayscale
  • Transcript

    • 1. Image ProcessingLecture 1Introduction and Application-Gaurav Gupta-Shobhit Niranjan
    • 2. Instructors Gaurav GuptaCan catch at : B109/Hall-1mail at: gauravg@iitk.ac.ingtalk: gauravg.84@GMAILmore information at: http://home.iitk.ac.in/~gauravg Shobhit NiranjanCan catch at : B211/Hall-1mail at: nshobhit@iitk.ac.ingtalk: shobhitn@GMAILmore information at: http://home.iitk.ac.in/~nshobhit
    • 3. Course Structure1. Introduction to Image Processing, Application andProspects (Today)2. Introduction, Image formation, camera models andperspective geometry3. Fourier Transform theory , Convolution andCorrelation4. Color, Image enhancement Techniques5. Binary images: thresholding, moments, topologyNote: Some topics may not be in order to maintain coherency and runningtime requirements. (Bare with us …trying to teach first time !!)
    • 4. Today Image Formation Range Transformations Point Processing Reading for this week: Gonzalez & Woods, Ch. 3
    • 5. Image Formationf(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 R2to R: f( x, y ) gives the intensity at position ( x, y ) Realistically, we expect the image only to be defined over arectangle, 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 yf x y g x yb x y  =   
    • 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 ∆ 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
    • 11. Image processing An image processing operation typically defines anew 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. Negative
    • 13. Log
    • 14. Image Enhancement
    • 15. Contrast Streching
    • 16. Image Histograms
    • 17. Histogram Equalization
    • 18. Neighborhood Processing (filtering) Q: What happens if I reshuffle all pixels withinthe image? A: It’s histogram won’t change. No pointprocessing will be affected… Need spatial information to capture this.
    • 19. Programming Assignment #1 Easy stuff to get you started withMatlab Shobhit will hold your first tutorial Topics will be from next 2 lectures
    • 20. Applications&Research Topics
    • 21. Document Handling
    • 22. Signature Verification
    • 23. Biometrics
    • 24. Fingerprint Verification / Identification
    • 25. Fingerprint Identification Research atUNRMinutiae MatchingDelaunay Triangulation
    • 26. Object Recognition
    • 27. Object Recognition Researchreference view 1 reference view 2novel view recognized
    • 28. Indexing into Databases Shape content
    • 29. Indexing into Databases (cont’d) Color, texture
    • 30. Target Recognition Department of Defense (Army, Airforce,Navy)
    • 31. Interpretation of aerial photography is a problem domain in bothcomputer vision and registration.Interpretation of Aerial Photography
    • 32. Autonomous Vehicles Land, Underwater, Space
    • 33. Traffic Monitoring
    • 34. Face Detection
    • 35. Face Recognition
    • 36. Face Detection/Recognition Research atUNR
    • 37. Facial Expression Recognition
    • 38. Face Tracking
    • 39. Face Tracking (cont’d)
    • 40. Hand Gesture Recognition Smart Human-Computer User Interfaces Sign Language Recognition
    • 41. Human Activity Recognition
    • 42. Medical Applications skin cancer breast cancer
    • 43. Morphing
    • 44. Inserting Artificial Objects into a Scene
    • 45. Companies In this Field In India and cameto 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
    • 46. Links for Self Study and a little Play http://undergraduate.csse.uwa.edu.au/units/233.4 http://www.netnam.vn/unescocourse/computervis Book: Digital Image Processing, 2nd Editionby Gonzalez and Woods, Prentice Hall
    • 47. Good Luck !!!

    ×