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ECE-537 Digital Image Processing

ECE-537 Digital Image Processing. Prof. K. J. Hintz http://cpe.gmu.edu/~khintz http://cpe.gmu.edu/. Course Objectives. Develop an Understanding of Basic Image Processing Techniques Through Lecture, Study, and Exercises

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ECE-537 Digital Image Processing

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  1. ECE-537Digital Image Processing Prof. K. J. Hintz http://cpe.gmu.edu/~khintz http://cpe.gmu.edu/ K. Hintz, 1996-2002

  2. Course Objectives • Develop an Understanding of Basic Image Processing Techniques Through Lecture, Study, and Exercises • Develop a Fluency With a Digital Image Processing (DIP) Software Package, e.g., Cantata, Through Experience • Implement an Independent DIP Project Which Demonstrates Your Ability to Integrate the Mathematical Theory With the Practical Issues K. Hintz, 1996-2002

  3. Milestone Chart K. Hintz, 1996-2002

  4. Khoros Image Processing Software • Composer • Toolboxes • Cantata (our primary interest) • Workspaces for interactive DIP programming • Glyph-based scripting language • Workspaces are in ASCII text format K. Hintz, 1996-2002

  5. Image Processing Software • Khoros Will Be Installed Soon on iris.gmu.edu • Class List Will Be Given to Computer Administrator to Put in Group for Access to Software • Students can get combination to S&T-II room ___ to have access at non-class times • Khoros can be run effectively on a sufficiently powerful PC running the Linux operating system • Windows version requires Xwin32 X K. Hintz, 1996-2002

  6. Khoros Availability • Students wishing to use Khoros at home can download the student version from the Khoros web site at no charge • Build it yourself, straightforward • http://www.khoral.com/khoros/kp2001_student/ • Binaries available from Amazon for $30 K. Hintz, 1996-2002

  7. Linux Operating System • PC based, x86 • The more RAM the better • Linux can be downloaded free • http://www.ibiblio.org/pub/Linux/distributions/ • Various distributions available on CDROM (~$40) with various levels of support K. Hintz, 1996-2002

  8. Linux Components Needed to Install/Use Khoros • gzip • ANSI-C compiler, e.g., gcc • X-Windows (X11R4, R5, or R6) Software Development Environment, e.g., XFree86 • Athena, Motif, or OLIT widget set • Lex & YaCC (or bison & flex) • Optional • perl • groff (needs C++ compiler) • Fortran Compiler or f2c (matrix & geometry TB) K. Hintz, 1996-2002

  9. Useful Free Linux Software • GIMP • GNU Image Manipulation Program • XV • Display images • Image format conversion • Color manipulation • Some image processing operators • Smooth • Dither • Enlarge • Oil paint K. Hintz, 1996-2002

  10. Digital Image • N-Dimensional Data, Not Necessarily Visual Image • Passive Sensors • Infrared • Ultraviolet • Visual • Multi-spectral, hyper-spectral • State of point • Active Sensors • Imaging radars • Synthetic aperture radar (SAR) • X-ray K. Hintz, 1996-2002

  11. Passive Sensor Data • Infrared • Single-band/Dual-band • 3-5 micron and/or 8-12 micron wavelength • Greyscale image • Ultraviolet • Visual • Greyscale • HSI K. Hintz, 1996-2002

  12. Passive Sensor Data • Multi-spectral, hyper-spectral • Each pixel is n-dimensional vector • Subdivides sensor bandwidth with narrow-band filters • State of point K. Hintz, 1996-2002

  13. IR Images of Clouds Near-IR, 3-5  Far-IR, 8-12  K. Hintz, 1996-2002

  14. UV Image of Foraminifera K. Hintz, 1996-2002

  15. Visual Image, BW vs. Color Same spatial resolution 20 kByte 29 kByte K. Hintz, 1996-2002

  16. Digital Image • Computed Data • Function representation • Visualization of complex functions, e.g., Mandelbrot set • Virtual reality • Fabric draping K. Hintz, 1996-2002

  17. Active Sensor Data • Imaging Radars • 95 GHz ==>  = 1/8 inch • Frequency chosen based on atmospheric absorption bands • (Inverse) Synthetic Aperture Radar ((I)SAR) • Multiple measurements from long baseline of moving sensor • ISAR uses movement of target • X-ray K. Hintz, 1996-2002

  18. SAR Image of Airport Synthetic Aperture Radar K. Hintz, 1996-2002

  19. X-Ray MRI Khoros K. Hintz, 1996-2002

  20. Digital Image • Quantized • Amplitude • Intensity • Absorption • Numerical value (magnitude) • Color or pseudo-color • Signal strength • Spatially (Khoros notation) • Height in rows • Width in columns K. Hintz, 1996-2002

  21. Synchronous Amplitude Quantization K. Hintz, 1996-2002

  22. Spatial Quantization K. Hintz, 1996-2002

  23. Processing vs. Analysis • Digital Image Processing (DIP) • “manipulation of images by computer” • Texture and edges • Segmentation • Enhancement • Mensuration • Movement • Feature extraction K. Hintz, 1996-2002

  24. Analog Image Processing • Analog Image Processing • Optical SAR, Fourier transform • Film processing • Image summation • Color, e.g., TV RGB K. Hintz, 1996-2002

  25. Processing vs. Analysis • Image Analysis • Pattern recognition • Automatic control • Content • Operates on features or symbolic representation • Optical character recognition (OCR) • Handwriting • Intelligence K. Hintz, 1996-2002

  26. Fractal Dimension of IR Clouds K. Hintz, 1996-2002

  27. Character Recognition Extract Rotate Segment Classify Parse…no K. Hintz, 1996-2002

  28. Image Spatial Manipulation Matlab Example K. Hintz, 1996-2002

  29. Foreground/Background Marr, Computer Vision K. Hintz, 1996-2002

  30. Foreground/Background K. Hintz, 1996-2002

  31. Digital Image Processing • Digital Image • “...a sampled, quantized function of two dimensions that has been generated by optical means, sampled in an equally spaced rectangular grid pattern, and quantized in equal intervals of amplitude.” • Processing • “...starts with one image and produces a modified version of that image [without interpreting it]” K. Hintz, 1996-2002

  32. Discrete vs. Continuous Approaches • Image Consists of Discrete Points • Each pixel is a unique, independent entity • Process points rather than entities in image • e.g., calibration of column of individual detectors used to generate image • Assumes no underlying continuous process K. Hintz, 1996-2002

  33. Discrete vs. Continuous Approaches • Continuous Approach • Underlying process is continuous, e.g., optical image of real object • Processing mathematics are continuous with a discrete approximation, e.g, Fourier Transform and DFT K. Hintz, 1996-2002

  34. Digital Processing of Images • Completely Accurate Only when Original Data is Quantized and Output is Quantized • Usually Done for Convenience • Digital Image is Approximation of Real, Continuous Image • Digital Operations are Approximations of Continuous Mathematical Processes K. Hintz, 1996-2002

  35. Processing Artifacts • Approximate Operations on Approximate Images can Yield Sampling Effects • Moiré pattern (continuous also) • Ragged edges • Gibbs phenomenon K. Hintz, 1996-2002

  36. Gibbs* Phenomenon • Insufficient Bandwidth to Completely Represent Edge • Simulated by • Discrete Fourier transform • Discard high frequency spectral lines • Inverse DFT • Results in “Ringing” Near Edges *Ziemer, Tranter, Fannin, Signals and Systems: Continuous and Discrete, Macmillan, 1989. K. Hintz, 1996-2002

  37. Square-in-Square Cantata Workspace K. Hintz, 1996-2002

  38. Gibbs Phenomenon Workspace K. Hintz, 1996-2002

  39. Expansion of S-in-S Glyph K. Hintz, 1996-2002

  40. Inset Glyph Menu Pane K. Hintz, 1996-2002

  41. PSD of S-in-S K. Hintz, 1996-2002

  42. Interactive Thresholding K. Hintz, 1996-2002

  43. Gibbs Phenomenon K. Hintz, 1996-2002

  44. Unified DIP Approach • Characterize Effect of DIP from Knowledge of Continuous Form’s Behavior • A/D --> Process --> D/A Without Loss or Significant Degradation of Content of Interest • Sampling and DIP Artifacts • Predict their occurrence • Recognize them when they occur • Eliminate or minimize their deleterious effects K. Hintz, 1996-2002

  45. DIP is Multidisciplinary • Preparation of Object to be Imaged • Stain used for tissue (biology) • Illumination (photography) • X-ray energy and energy spectra (physics) • Selection of amplitude and spatial resolution (communications) K. Hintz, 1996-2002

  46. DIP is Multidisciplinary • Digitizing the Image • Equipment selection (CCD, near-IR, far-IR, x-ray, etc.) • Signal-to-noise ratio (communication theory) • Full dynamic range of resulting image and bias errors (A/D converters, amplifiers, electronics) • Purpose for images (end user) K. Hintz, 1996-2002

  47. Image-to-Image Consistency • Amplitude calibration • Grey-scale charts • Visual scenes • Stepped wedges • X-ray absorption images • Black-body • Spectrum proportional to T4 • Hot body, cold body calibrate endpoints • Multi-spectral K. Hintz, 1996-2002

  48. Image-to-Image Consistency • Spatial calibration • Fiducial marks • INF Treaty X-Ray example • Easily identifiable features • Crosses on roadways for satellite landmapping • Known geometry of acquisition system and scene • Structured light example K. Hintz, 1996-2002

  49. Image-to-Image Consistency • Image-to-Image Consistency • Temporal imaging • Intelligence analysis, airplanes in/out of airport • Velocity estimation • Database change • Image registration • Anomaly detection • Known clutter removal K. Hintz, 1996-2002

  50. Image-to-Image Consistency • Clutter Removal (image next slide) • Real signals, not noise • Characteristics of clutter • Statistical properties • Shape • Random • Geometric • Distinctive features K. Hintz, 1996-2002

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