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Introduction to Image Processing

Introduction to Image Processing. Representasi Citra Tahap-Tahap Kunci pada Image Processing Aplikasi dan Topik Penelitian pada Image Processing. Image Representation. Representasi Citra. Images are Ubiquitous. Input Optical photoreceptors Digital camera CCD array Output TVs

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Introduction to Image Processing

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  1. Introduction toImage Processing Representasi Citra Tahap-TahapKuncipada Image Processing AplikasidanTopikPenelitianpada Image Processing

  2. Image Representation Representasi Citra

  3. Images are Ubiquitous • Input • Optical photoreceptors • Digital camera CCD array • Output • TVs • Computer monitors • Printers

  4. Image Formation

  5. Sampling and Quantization

  6. Sampling and Quantization

  7. Image as Array of Pixels • An image is a 2-d rectilinear array of pixels

  8. Pixels as samples • A pixel is a sample of a continuous function

  9. y Gray level x pixel Digital image Original picture I[i, j] or I[x, y] f(x, y) What is an image?The bitmap representation • Also called “raster or pixel maps” representation • An image is broken up into a grid

  10. What is an image?The bitmap representation

  11. What is an image?The vector representation • Object-oriented representation • Does not show information of individual pixel, but information of an object (circle, line, square, etc.) Circle(100, 20, 20) Line(xa1, ya1, xa2, ya2) Line(xb1, yb1, xb2, yb2) Line(xc1, yc1, xc2, yc2) Line(xd1, yd1, xd2, yd2)

  12. Bitmap Can represent images with complex variations in colors, shades, shapes. Larger image size Fixed resolution Easier to implement Vector Can only represent simple line drawings (CAD), shapes, shadings, etc. Efficient Flexible Difficult to implement Comparison between Bitmap Representation and Vector Representation

  13. Image as a Function • 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:

  14. Image as a function

  15. Properties of Images • Spatial resolution • Width pixels/width cm and height pixels/ height cm • Intensity resolution • Intensity bits/intensity range (per channel) • Number of channels • RGB is 3 channels, grayscale is one channel

  16. Common image file formats • GIF (Graphic Interchange Format) - • PNG (Portable Network Graphics) • JPEG (Joint Photographic Experts Group) • TIFF (Tagged Image File Format) • PGM (Portable Gray Map) • FITS (Flexible Image Transport System)

  17. Key Stages in Digital Image Processing Tahap-tahap Kunci pada Pemrosesan Citra Digital

  18. Key Stages in Digital Image Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  19. Images taken from Gonzalez & Woods, Digital Image Processing (2002) Key Stages in Digital Image Processing:Image Aquisition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  20. Images taken from Gonzalez & Woods, Digital Image Processing (2002) Key Stages in Digital Image Processing:Image Enhancement Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  21. Images taken from Gonzalez & Woods, Digital Image Processing (2002) Key Stages in Digital Image Processing:Image Restoration Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  22. Images taken from Gonzalez & Woods, Digital Image Processing (2002) Key Stages in Digital Image Processing:Morphological Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  23. Images taken from Gonzalez & Woods, Digital Image Processing (2002) Key Stages in Digital Image Processing:Segmentation Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  24. Images taken from Gonzalez & Woods, Digital Image Processing (2002) Key Stages in Digital Image Processing:Object Recognition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  25. Images taken from Gonzalez & Woods, Digital Image Processing (2002) Key Stages in Digital Image Processing:Representation & Description Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  26. Key Stages in Digital Image Processing:Image Compression Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  27. Images taken from Gonzalez & Woods, Digital Image Processing (2002) Key Stages in Digital Image Processing:Colour Image Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  28. Applications and Research Topics

  29. Document Handling

  30. Signature Verification

  31. Biometrics

  32. Fingerprint Verification / Identification

  33. Fingerprint Identification Research at UNR • Minutiae Matching • Delaunay Triangulation

  34. Object Recognition

  35. Object Recognition Research • reference view 1 reference view 2 • novel view recognized

  36. Indexing into Databases • Shape content

  37. Indexing into Databases (cont’d) • Color, texture

  38. Target Recognition • Department of Defense (Army, Airforce, Navy)

  39. Interpretation of Aerial Photography Interpretation of aerial photography is a problem domain in both computer vision and registration.

  40. Autonomous Vehicles • Land, Underwater, Space

  41. Traffic Monitoring

  42. Face Detection

  43. Face Recognition

  44. Face Detection/Recognition Research at UNR

  45. Facial Expression Recognition

  46. Face Tracking

  47. Face Tracking (cont’d)

  48. Hand Gesture Recognition • Smart Human-Computer User Interfaces • Sign Language Recognition

  49. Human Activity Recognition

  50. Medical Applications • skin cancer breast cancer

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