Introduction to Image Processing

1. Introduction to Image Processing

2. What is Image Processing? • Manipulation of digital images by computer. • Image processing focuses on two major tasks: • Improvement of pictorial information for human interpretation and high level processing. • Processing of image data for storage and transmission.

3. Related Areas • Image Processing • Computer Vision • Computer Graphics

4. Image Processing

5. Image Processing • Image Enhancement

6. Image Processing (cont’d) • Image Restoration

7. Image Processing (cont’d) • Image Compression

8. Computer Graphics

9. Geometric Models Computer Graphics Projection, shading, lighting models Output: Image Synthetic Camera

10. Computer Vision

11. Output: Model Real Scene Computer Vision Cameras Images

12. Applications: Image Enhancement • One of the most common uses of IP techniques: improve quality, remove noise etc

13. Applications: Space • Launched in 1990 the Hubbletelescope can take images of very distant objects • An incorrect mirror made many of Hubble’s images useless • Image processing techniques were used to fix this!

14. Applications: Medicine • Take slice from MRI scan of a dog’s heart, and find boundaries between different types of tissue • Image with gray levels representing tissue density • Use a suitable filter to highlight edges Original MRI image of a dog’s heart Edge detection image

15. Applications: GIS • Geographic Information Systems • Digital image processing techniques are used extensively to manipulate satellite imagery. • meteorology terrain classification

16. Applications: Industrial Inspection • Human operators are expensive, slow and unreliable • Make machines do thejob instead! • Industrial vision systems are used in all kinds of industries

17. Applications: Law Enforcement • Image processing techniques are used extensively by law enforcers • Number plate recognition for speed cameras or automated toll systems • Fingerprint recognition

18. Examples: HCI • Make Human Computer Interaction (HCI) more natural • Face recognition • Gesture recognition

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

20. Image Acquisition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Representation & Description Object Recognition Problem Domain Colour Image Processing Image Compression

21. Image Enhancement Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Representation & Description Object Recognition Problem Domain Colour Image Processing Image Compression

22. Image Restoration Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Representation & Description Object Recognition Problem Domain Colour Image Processing Image Compression

23. Morphological Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Representation & Description Object Recognition Problem Domain Colour Image Processing Image Compression

24. Segmentation Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Representation & Description Object Recognition Problem Domain Colour Image Processing Image Compression

25. Representation & Description Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Representation & Description Object Recognition Problem Domain Colour Image Processing Image Compression

26. Object Recognition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Representation & Description Object Recognition Problem Domain Colour Image Processing Image Compression

27. Image Compression Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

28. Color Image Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Color Image Processing Image Compression

29. How are images represented in the computer?

30. Color images

31. A Simple model of image formation

32. What is (visible) light? • The visible portion of the electromagnetic (EM) spectrum. • Approximately between 400 and 700 nanometers.

33. Examples: Gama-Ray Imaging Gamma-ray imaging: nuclear medicine and astronomical observations

34. Examples: X-Ray Imaging X-rays: medical diagnostics, industry, and astronomy, etc.

35. Examples: Ultraviolet Imaging Ultraviolet: industrial inspection, microscopy, lasers, biological imaging, and astronomical observations

36. Examples: Infrared Imaging Infrared bands: light microscopy, astronomy, remote sensing, industry, and law enforcement.

37. Sonic images • Produced by the reflection of sound waves off an object. • High sound frequencies are used to improve resolution.

38. Range images • Can be produced by using laser range-finders. • An array of distances to the objects in the scene.

39. Image formation • There are two parts to the image formation process: • The geometry of image formation, which determines where in the image plane the projection of a point in the scene will be located. • The physics of light, which determines the brightness of a point in the image plane as a function of illumination and surface properties.

40. Pinhole camera • This is the simplest device to form an image of a 3D scene on a 2D surface. • Straight rays of light pass through a “pinhole” and form an inverted image of the object on the image plane.

41. Camera optics • In practice, the aperture must be larger to admit more light. • Lenses are placed in the aperture to focus the bundle of rays from each scene point onto the corresponding point in the image plane

42. Physics of Light f(x,y)=i(x,y)r(x,y) where • i(x,y) the amount of illumination incident to the scene 2) r(x,y) the reflectance from the object

43. CCD (Charged-Coupled Device) cameras • Tiny solid state cells convert light energy into electrical charge. • The image plane acts as a digital memory that can be read row by row by a computer.

44. Frame grabber • Usually, a CCD camera plugs into a computer board (frame grabber). • The frame grabber digitizes the signal and stores it in its memory (frame buffer).

45. Image digitization • Sampling means measuring the value of an image at a finite number of points. • Quantization is the representation of the measured value at the sampled point by an integer.

46. Image digitization (cont’d) 255 0

47. Image digitization (cont’d) 2D example

48. Effect of Image Sampling • original image sampled by a factor of 2 • sampled by a factor of 4 sampled by a factor of 8

49. Effect of Image Quantization 256 gray levels (8bits/pixel) 32 gray levels (5 bits/pixel) 16 gray levels (4 bits/pixel) 8 gray levels (3 bits/pixel) 4 gray levels (2 bits/pixel) 2 gray levels (1 bit/pixel)

50. Representing Digital Images The result of sampling and quantization is a matrix of integer numbers. Here we have an image f(x,y) that was sampled to produce M rows and N columns.