Introduction

1. Introduction What is “image processing and computer vision”? Image Representation

2. What is “Image Processing and Computer Vision”? Image Processing manipulate image data generate another image Computer Vision process image data generate symbolic data Image Processing and Computer Vision: 1

3. Computer Vision • Reconstruction • Recover 3D information from data • Recognition • Detect and identify objects • Understanding • What is happening in the scene? Image Processing and Computer Vision: 1

4. Historical overview • 1920s • Coding images for transmission by telegraph (3 hours) • 1960s • Computers powerful enough to store images and process in realistic times • Space program Image Processing and Computer Vision: 1

5. 1960s - 1970s • Applications • Medical imaging • Remote sensing • Astronomy Image Processing and Computer Vision: 1

6. Today • DTV • Image interpretation • Biometry • GIS • Human genome project Image Processing and Computer Vision: 1

7. Example images (1) Image Processing and Computer Vision: 1

8. Example images (2) Image Processing and Computer Vision: 1

9. Sample applications • Character recognition (OCR) • Printed text, Hand-printed text, Cursive text • Biometry • GIS Image Processing and Computer Vision: 1

10. Printed Text • Characteristics • Regular shape • Regular orientation • Good contrast • Can compare characters with a prototype Image Processing and Computer Vision: 1

11. Input Output Template Image Processing and Computer Vision: 1

12. Hand Printed Text • Characteristics • Less regularity • Must examine components of character Image Processing and Computer Vision: 1

13. Cursive Text • Totally irregular • Careful analysis of strokes Image Processing and Computer Vision: 1

14. Biometry • Using personal characteristics to identify a person • Fingerprints • Face • Iris • DNA • Gait • etc Image Processing and Computer Vision: 1

15. Iris Scan • Striations on iris are individually unique • Obvious applications: • Security • PIN Image Processing and Computer Vision: 1

16. } fixed number of samples Locate the eye in the head image Radial resampling of iris Analysis Numerical description Image Processing and Computer Vision: 1

17. GIS • Earth/Planetary Observation • Monitoring • Exploration Image Processing and Computer Vision: 1

18. Examples Image Processing and Computer Vision: 1

19. Captured data Enhancement Feature Extraction Feature Recognition Image Recognition Labels System Overview Image Processing and Computer Vision: 1

20. Image Representation • Image capture • Image quality measurements • Image resolution • Colour representation • Camera calibration • Parallels with human visual system Image Processing and Computer Vision: 1

21. Image Capture • Many sources • Consider requirements of system • Resolution • Type of data • Transducer Image Processing and Computer Vision: 1

22. Representation • Sampled data • Spatial • Amplitude • On a rectangular array Image Processing and Computer Vision: 1

23. Image Resolution • How many pixels • Spatial resolution • How many shades of grey/colours • Amplitude resolution • How many frames per second • Temporal resolution • Nyquist’s theorem Image Processing and Computer Vision: 1

24. Nyquist’s Theorem • A periodic signal can be reconstructed if the sampling interval is half the period • An object can be detected if two samples span its smallest dimension Image Processing and Computer Vision: 1

25. Spatial Resolution n, n/2, n/4, n/8, n/16 and n/32 pixels on a side. Image Processing and Computer Vision: 1

26. Amplitude Resolution • Humans can see: • About 40 shades of brightness • About 7.5 million shades of colour • Cameras can see: • Depends on signal to noise ratio • 40 dB equates to about 20 shades • Images captured: • 256 shades Image Processing and Computer Vision: 1

27. Shades of Grey 256, 16, 4 and 2 shades. Image Processing and Computer Vision: 1

28. Temporal Resolution • Nyquist’s theorem for temporal data • How much does an object move between frames? • Can motion be understood unambiguously? Image Processing and Computer Vision: 1

29. Colour Representation • Newton • White light composed of seven colours • red, orange, yellow, green, blue, indigo, violet • Three primaries could approximate many colours • red, green, blue Image Processing and Computer Vision: 1

30. CIE Colour Diagram Image Processing and Computer Vision: 1

31. Other Colour Models • YMCK • IHS • YCrCb • etc Image Processing and Computer Vision: 1

32. Camera Calibration • Link image co-ordinates and world co-ordinates • Extrinsic parameters • Location and orientation of camera with respect to a co-ordinate frame • Intrinsic parameters • Relate pixel co-ordinates with camera reference frame co-ordinates Image Processing and Computer Vision: 1

33. Extrinsic Parameters • Camera’s • Location • Orientation • With respect to world origin Image Processing and Computer Vision: 1

34. Camera frame World frame translate and rotate Image Processing and Computer Vision: 1

35. Intrinsic Parameters • Characterise • Optical • Geometric • Digital properties of camera • Relate • Image co-ordinates to camera co-ordinates Image Processing and Computer Vision: 1

36. Pinhole Camera f Z Optical centre Object Image Image and centre, object and centre are similar triangles. Image Processing and Computer Vision: 1

37. Distortionless • If • no distortions • uniform sampling • Co-ordinates linearly related • offset and scale Image Processing and Computer Vision: 1

38. Distorted • Periphery is distorted • k2 = 0 is good enough Image Processing and Computer Vision: 1

39. Parallels With Human Visual System • Image capture • Retina • Focussing • Cornea and lens • Exposure • Iris and retina Image Processing and Computer Vision: 1

40. Summary • Historical overview • Sample applications • Resolution • Colour models • Camera calibration Image Processing and Computer Vision: 1

41. 640k ought to be enough for anybody Bill Gates, 1981 Image Processing and Computer Vision: 1