digital image processing

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digital image processing

  1. 1. Digital Image Processing ELE-4707
  2. 2. PRESENTED BY N.CH. KARTHIK. C.S.E-B FINAL YEAR. BITS COLLEGE ,KMM. Date :28-3-2012, Kmm.
  3. 3. Dip: “The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes”. Image processing is used for two somewhat different purposes: • improving the visual appearance of images (pictorial information ) to a human viewer, and • Preparing (processing) images for measurement of the features and structures present. The techniques that are appropriate for each of these tasks are not always the same, but there is considerable overlap. This course covers methods that are used for both purposes.
  4. 4. What Is Digital Image Processing • The field of digital image processing refers to processing digital images by means of a digital computer. • A digital image can be defined as a two-dimensional function, f (x, y), where x and y are spatial coordinates, and f intensity or gray level of the image at that point. The field of digital image processing refers to processing digital images by means of a digital computer. A digital image can be defined as a two- dimensional function, f (x, y), where x and y are spatial coordinates, and f intensity or gray level of the image at that point.
  5. 5. •Early 1920s: One of the first applications of digital imaging was in the news- paper industry – The Bartlane cable picture transmission service – Images were transferred by submarine cable between London and New York – Pictures were coded for cable transfer and reconstructed at the receiving end on a telegraph printer
  6. 6. HISTORY: Developed in the 1960s at the Jet Propulsion Laboratory, Massachusetts Institute of Technology, Bell Laboratories, University of Maryland, Research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, With the fast computers signal processors available in the 2000s, but also the cheapest. HISTORY: Developed in the 1960s at the Jet Propulsion Laboratory, Massachusetts Institute of Technology, Bell Laboratories, University of Maryland, Research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, With the fast computers signal processors available in the 2000s, but also the cheapest.
  7. 7. •Used in space:techngy: – 1964: Computers used to improve the quality of images of the moon taken by the Ranger 7 probe – Such techniques were used in other space missions including the Apollo landings A picture of the moon taken by the Ranger 7 probe minutes before landing
  8. 8. • Low-level process: (DIP) – Primitive operations where inputs and outputs are images Major functions: image pre-processing like noise reduction, contrast enhancement, image sharpening, etc. • Mid-level process (DIP and Computer Vision and Pattern Recognition) – Inputs are images, outputs are attributes (e.g., edges). major functions: segmentation, description, classification / recognition of objects • High-level process (Computer Vision) – make sense of an ensemble of recognized objects; perform the cognitive functions normally associated with vision
  9. 9. EXAMPLE OF DIP •One of the most common uses of DIP techniques: improve quality, remove noise etc
  10. 10. EXAMPLES: (e) Poorly exposed x-ray image (f) The result from contrast and edge enhancement (g) Image blurred by motion (h) The result of de-blurring
  11. 11. Examples: The Hubble Telescope •Launched in 1990 the Hubble telescope can take images of very distant objects •However, an incorrect mirror made many of Hubble’s images useless •Image processing techniques were used to fix this....
  12. 12. Finding the outline and shape of image objects, e.g. character recognition.
  13. 13. FACE DETECTION: Face detection
  14. 14. FACE TRACKING
  15. 15. 1)Biological Research: e.g. DNA typing and matching; automatic counting and classification of cell structures in bone and tissue. 2) Defence and Intelligence: e.g. Reconnaissance photo- interpretation of objects in satellite images; target acquisition and missile guidance. 3) Document Processing: e.g. Scanning, archiving and transmission (fax); automatic detection and recognition of printed text (postal sorting office, tax return processing, banking cheques). 4) Law Enforcement Forensics: e.g. Photo-ID kits, criminal photo- search, automatic fingerprint matching, DNA matching and fibre analysis
  16. 16. 5) Photography: e.g. altering colours, zooming; adding and subtracting objects to a scene; 6) Remote Sensing: e.g. Land cover analysis (water, roads, cities and cultivation),  vegetation features (water content and temperature) and crop yield analysis; 3-D terrain rendering from satellite or aircraft data (road and dam planning); fire and smoke detection. 7) Space exploration and Astronomy: satellite navigation and altitude control using star positions. 8) Video and Film Special Effects: Animation,and special effects (Star Wars).
  17. 17. EM SPECTRUM:
  18. 18. The imaging machines can cover almost the entire EM spectrum, ranging from gamma to radio waves. These include • Gamma ray images • x-ray band images • ultra-violet band images • visual light and infra-red images • Imaging based on micro-waves and radio waves •Thus, digital image processing encompasses a wide and varied field of applications. EM SPECTRUM:
  19. 19. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing:
  20. 20. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing: Image Aquisition
  21. 21. IMAGE ACQUISITION:
  22. 22. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing: Image Enhancement
  23. 23. Image Enhancement:
  24. 24. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image restoration:
  25. 25. Digital Image restoration:
  26. 26. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image processing, morphological processing:
  27. 27. Digital Image Morphological Processing:
  28. 28. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital ImageProcessing,segmentation:
  29. 29. Digital Image Processing: Segmentation:
  30. 30. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing object recognition:
  31. 31. Digital Image Processing Object Recognition:
  32. 32. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing: Representation & Description:
  33. 33. Key Stages in Digital Image Processing: Representation & Description:
  34. 34. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing, Image Compression:
  35. 35. IMAGE COMPRESSION:
  36. 36. Image Restoration Morphologica l Processing Segmentation Object Recognition Representatio n & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing, Colour Image Processing:
  37. 37. Colour Image Processing:
  38. 38. 38 DIGITAL IMAGES: Digital images are 2D arrays (matrices) of numbers:
  39. 39. 39 SAMPLING:
  40. 40. 40 Effect of Sampling and Quantization 250 x 210 samples 256 gray levels 125 x 105 samples 50 x 42 samples 25 x 21 samples 8 gray levels 4 gray levels Binary image16 gray levels
  41. 41. IMAGE ENHANCEMENT: the idea behind enhancement techniques is to bring out detail that is obscured, simply to highlight certain features of interest in an image. example of enhancement is when we increase the contrast of an image because “it looks better.”
  42. 42. Image restoration: improving the appearance of an image. Compression:  for reducing the storage required to save an image, or the bandwidth required to transmit it. Segmentation :  partition an image into its constituent parts or objects. In general, most difficult tasks in dip. Representation and description :Al most always follow the output of a segmentation stage
  43. 43. CONCLUSION: 1.Dip uses it gives effective images 2.It is used to edits image user wants type. 3.It is used in satellites ,medical, movies e.t.c. 4.Colour images styles,animation so on… 5.User understand any thing easy way.

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