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Digital Image Processing & Machine Vision Lecture 1 (Introduction & Motivation)

Digital Image Processing & Machine Vision Lecture 1 (Introduction & Motivation)

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  • 1. Digital Image Processing & Machine Vision Instructed by Dr. Abdul Rehman Abbasi One picture is worth more than ten thousand words
  • 2. Course Contents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. (with estimated no. of lectures) Introduction & Motivation (1) Fundamental Concepts (1) Image Acquisition (1) Image Enhancement (2) Morphological Operations (1) Image Segmentation (3) Feature Extraction (3) Hardware & Software Methods in Image Processing (1) Advanced Research Areas in Image Processing (1) Mini-Project Presentation or Research Article Presentations (2)
  • 3. Reference Books & Journals • Digital Image Processing by Rafael C. Gonzalez & Richard E. Woods (2nd Edition), Pearson Education • Digital Image Processing: A Practical Introduction using Java TM by Nick Efford, Pearson Education • Applied Image Processing by G.W. Awcock & R. Thomas , McGrawHill • Real-Time Image and Video Processing: From Research to Reality Nasser Kehtarnavaz and Mark Gamadia, Morgan & Claypool Publishers • • • • • Image & Vision Computing, Journal (IVC) Computer Vision & Image Understanding, Journal (CVIU) International Journal of Computer Vision (IJCV) IEEE Transactions on Pattern Analysis & Machine Intelligence (PAMI) IEEE Transactions on Image Processing
  • 4. Introduction to Image Processing • An image is a 2-Dimensional function f(x,y) where x and y are spatial coordinates, and amplitude f at any pair of coordinates (x,y) is called the intensity or gray level of the image at that point. • When x,y, and f are finite and discrete we call it a digital image. • Digital image processing means processing/computing digital images using computational means such as using a digital computer.
  • 5. Image Function & Spatial Coordinates
  • 6. Image Processing & the World
  • 7. Motivation  Medical Diagnosis  Industrial Applications  Security Applications  Biometrics & Finance  Seismic Analysis  Aerial Applications  Space Explorations
  • 8. Medical Diagnosis Digital Mammogram MRI of Knee & Spine Head CT Scan Ultrasound
  • 9. Industrial Applications • Electronic Defect Detection Product Testing/QA
  • 10. Security Applications Whole Body Scan Vehicle Identification
  • 11. Biometrics & Finance Fingerprint Verification Currency verification Personnel Verification
  • 12. Seismic Analysis Mountains Ranges in Tibetan Plain Seismic patterns showing oil (natural resources) traps
  • 13. Satellite Applications Weather Forecast Aerial Analysis
  • 14. Space Explorations Moon surface observation North Pole observation
  • 15. Imaging Spectrum Images can work in a wide energy spectrum
  • 16. Gamma Ray Imaging-1 • Nuclear Medicine • Astronomical Observations
  • 17. Gamma Ray Imaging-2 1. 2. • Inject a patient with a radioactive isotope that emits gamma rays as it decays Images are produced from the emissions collected by gamma ray detectors Positron Emission Tomography (PET)
  • 18. Imaging in Radio Band Magnetic Resonance Imaging (MRI) • Place a patient in a powerful magnet and passes radio waves through his or her body in short pulses.
  • 19. Image Types
  • 20. Some Common Image Formats and Their Characteristics Format • jpg/jpeg (Joint Photographic Experts Group) • Characteristics • Image compression, supports 8-bit per color (RGB), generational degradation when edited repeatedly. tiff (Tagged-Image File Format) • Supports 8-bit and 16-bit per color , Support s OCR and device-specific color schemes • Gif (Graphics Interchange Format) • Limited to 256 colors , Supports animation • png (Portable Network Graphics) • 16 million colors (truecolor), Good for large images, best suited for editing • bmp (Bit Map) • Simple, suited for all WINDOWS applications, uncompressed
  • 21. Few Basic Image Operations
  • 22. Contrast Enhancement
  • 23. Image Resolution
  • 24. Scaling
  • 25. Rotation
  • 26. Translate
  • 27. Reflect or Mirroring
  • 28. Image Sharpening
  • 29. Sharpening
  • 30. Image Sharpening
  • 31. Machine Vision System Components
  • 32. Components of a Generic Machine Vision System • Radiation source: Illuminating the object/scene • Camera: The optical lens • Sensor: Converting the scene into a signal • Processer: Playing with the signal • Knowledge-Base: data understanding • Action unit: responding the visual information
  • 33. MV Schematic
  • 34. Illumination + Camera + Sensor+ Signal
  • 35. Processing Unit: Preprocessing
  • 36. Processing Unit: Segmentation
  • 37. Image Understanding: Tracking people’s activities
  • 38. Image Understanding: Skin tracking
  • 39. Image Understanding: Gesture Tracking
  • 40. Comparison of Machine & Human Vision System
  • 41. Human Vision versus Machine Vision Performance Parameters Functional Parameter Human Vision Machine Vision Adaptability More adaptable to environmental conditions Not much adaptable to changing world Decision Making Humans are good in making relative comparisons Machine needs fixed numerical values to decide Consistency Human are tired and less consistent Machines are consistent Accuracy Accuracy is subjective Accuracy is higher Speed Human brain is fast in processing Machines with state of art have limited speed incomparable to human brain Spectrum Human can make use of only visible light (390-790mm) Machines can operate in Xray and infra red ranges
  • 42. That’s All for this Session