Lecture1

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  • As opposed to [0..255]
  • Render with scanalyze????
  • Use photoshop to make something grayscale
  • Lecture1

    1. 1. Image ProcessingLecture 1Introduction and Application-Gaurav Gupta-Shobhit Niranjan
    2. 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. 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. 4. Today Image Formation Range Transformations Point Processing Reading for this week: Gonzalez & Woods, Ch. 3
    5. 5. Image Formationf(x,y) = reflectance(x,y) * illumination(x,y)Reflectance in [0,1], illumination in [0,inf]
    6. 6. Sampling and Quantization
    7. 7. Sampling and Quantization
    8. 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. 9. Images as functions
    10. 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. 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. 12. Negative
    13. 13. Log
    14. 14. Image Enhancement
    15. 15. Contrast Streching
    16. 16. Image Histograms
    17. 17. Histogram Equalization
    18. 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. 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. 20. Applications&Research Topics
    21. 21. Document Handling
    22. 22. Signature Verification
    23. 23. Biometrics
    24. 24. Fingerprint Verification / Identification
    25. 25. Fingerprint Identification Research atUNRMinutiae MatchingDelaunay Triangulation
    26. 26. Object Recognition
    27. 27. Object Recognition Researchreference view 1 reference view 2novel view recognized
    28. 28. Indexing into Databases Shape content
    29. 29. Indexing into Databases (cont’d) Color, texture
    30. 30. Target Recognition Department of Defense (Army, Airforce,Navy)
    31. 31. Interpretation of aerial photography is a problem domain in bothcomputer vision and registration.Interpretation of Aerial Photography
    32. 32. Autonomous Vehicles Land, Underwater, Space
    33. 33. Traffic Monitoring
    34. 34. Face Detection
    35. 35. Face Recognition
    36. 36. Face Detection/Recognition Research atUNR
    37. 37. Facial Expression Recognition
    38. 38. Face Tracking
    39. 39. Face Tracking (cont’d)
    40. 40. Hand Gesture Recognition Smart Human-Computer User Interfaces Sign Language Recognition
    41. 41. Human Activity Recognition
    42. 42. Medical Applications skin cancer breast cancer
    43. 43. Morphing
    44. 44. Inserting Artificial Objects into a Scene
    45. 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. 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. 47. Good Luck !!!

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