1. Digital Image Processing Lecture 1: Introduction February 21, 2005 Prof. Charlene Tsai tsaic@cs.ccu.edu.tw Prof. Charlene Tsai tsaic@cs.ccu.edu.tw http://www.cs.ccu.edu.tw/~tsaic/teaching/spring2005_undgrad/main.html

2. Digital Image ProcessionLecture 1 2 Why do we need digital image processing?  Image is better than any other information form for human being to perceive.  Humans are primarily visual creatures – above 90% of the information about the world (a picture is better than a thousand words)  However, vision is not intuitive for machines  projection of 3D world to 2D images => loss of information  interpretation of dynamic scenes, such as a moving camera and moving objects  Image is better than any other information form for human being to perceive.  Humans are primarily visual creatures – above 90% of the information about the world (a picture is better than a thousand words)  However, vision is not intuitive for machines  projection of 3D world to 2D images => loss of information  interpretation of dynamic scenes, such as a moving camera and moving objects

3. Digital Image ProcessionLecture 1 3 What is digital image processing?  Image understanding, image analysis, and computer vision aim to imitate the process of human vision electronically  Image acquisition  Preprocessing  Segmentation  Representation and description  Recognition and interpretation  Image understanding, image analysis, and computer vision aim to imitate the process of human vision electronically  Image acquisition  Preprocessing  Segmentation  Representation and description  Recognition and interpretation

4. Digital Image ProcessionLecture 1 4 General procedures  Goal: to obtain similar effect provided by biological systems  Two-level approaches  Low level image processing. Very little knowledge about the content or semantics of images  High level image understanding. Imitating human cognition and ability to infer information contained in the image.  Goal: to obtain similar effect provided by biological systems  Two-level approaches  Low level image processing. Very little knowledge about the content or semantics of images  High level image understanding. Imitating human cognition and ability to infer information contained in the image.

5. Digital Image ProcessionLecture 1 5 Low level image processing  Very little knowledge about the content of the images.  Data are the original images, represented as matrices of intensity values, i.e. sampling of a continuous field using a discrete grid.  Focus of this course.  Very little knowledge about the content of the images.  Data are the original images, represented as matrices of intensity values, i.e. sampling of a continuous field using a discrete grid.  Focus of this course.

6. Digital Image ProcessionLecture 1 6 Low level image processing Origin (Ox,Oy) Spacing (Sy) Spacing (Sx) Pixel Value Pixel Region 3x3 neighborhood

7. Digital Image ProcessionLecture 1 7 Low level image processing  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration

8. Digital Image ProcessionLecture 1 8 Low level image processing  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration

9. Digital Image ProcessionLecture 1 9 Low level image processing  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration

10. Digital Image ProcessionLecture 1 10 Low level image processing  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration

11. Digital Image ProcessionLecture 1 11 Low level image processing  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration

12. Digital Image ProcessionLecture 1 12 Low level image processing  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration

13. Digital Image ProcessionLecture 1 13 Low level image processing  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration Erosion Dilation

14. Digital Image ProcessionLecture 1 14 Low level image processing  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration  Image compression  Noise reduction  Edge extraction  Contrast enhancement  Segmentation  Thresholding  Morphology  Image restoration

15. Digital Image ProcessionLecture 1 15 High level image understanding  To imitate human cognition according to the information contained in the image.  Data represent knowledge about the image content, and are often in symbolic form.  Data representation is specific to the high- level goal.  To imitate human cognition according to the information contained in the image.  Data represent knowledge about the image content, and are often in symbolic form.  Data representation is specific to the high- level goal.

16. Digital Image ProcessionLecture 1 16 High level image understanding Landmarks (bifurcation/crossover) Traces (vessel centerlines)  What are the high-level components?  What tasks can be achieved?  What are the high-level components?  What tasks can be achieved?

17. Digital Image ProcessionLecture 1 17 Applications  Medicine  Defense  Meteorology  Environmental science  Manufacture  Surveillance  Crime investigation  Medicine  Defense  Meteorology  Environmental science  Manufacture  Surveillance  Crime investigation

18. Digital Image ProcessionLecture 1 18 Applications: Medicine CT (computed Tomography) PET (Positron Emission Tomography PET/CT

19. Digital Image ProcessionLecture 1 19 Applications: Surveillance Positioning pixel target in Aerial images Photograph A Photograph B

20. Digital Image ProcessionLecture 1 20 Applications: Meteorology

21. Digital Image ProcessionLecture 1 21 Applications: Environmental Science

22. Digital Image ProcessionLecture 1 22 Applications: Manufacture

23. Digital Image ProcessionLecture 1 23 Application: Surveillance

24. Digital Image ProcessionLecture 1 24 Applications: Crime Investigation Fingerprint enhancement

25. Digital Image ProcessionLecture 1 25 What are the difficulties?  Poor understanding of the human vision system Do you see a young or an old lady?

26. Digital Image ProcessionLecture 1 26 What are the difficulties?  Human vision system tends to group related regions together, not odd mixture of the two alternatives.  Attending to different regions or contours initiate a change of perception  This illustrates once more that vision is an active process that attempts to make sense of incoming information.  Human vision system tends to group related regions together, not odd mixture of the two alternatives.  Attending to different regions or contours initiate a change of perception  This illustrates once more that vision is an active process that attempts to make sense of incoming information.

27. Digital Image ProcessionLecture 1 27 What are the difficulties?  The interpretation is based heavily on prior knowledge.

28. Digital Image ProcessionLecture 1 28 Class Format – Efficiency of Learning  What we read10%  What we hear20%  What we see30%  What we hear + see50%  What we say ourselves70%  What we do ourselves90%  What we read10%  What we hear20%  What we see30%  What we hear + see50%  What we say ourselves70%  What we do ourselves90%

29. Digital Image ProcessionLecture 1 29 Class Format – Efficiency of Learning  This leads to in-class discussion and quizzes.  50-minute lecture  Remaining for group discussion & in-class quiz  This leads to in-class discussion and quizzes.  50-minute lecture  Remaining for group discussion & in-class quiz

30. Digital Image ProcessionLecture 1 30 Course requirements  In-class quizzes 10%  6 Homework assignments30%  Final project20%  Midtermexam20%  Final exam20%  Peer learning is encouraged  BUT, NO PLAGIARISM!!! (20% deduction if caught)  In-class quizzes 10%  6 Homework assignments30%  Final project20%  Midtermexam20%  Final exam20%  Peer learning is encouraged  BUT, NO PLAGIARISM!!! (20% deduction if caught)

31. Digital Image ProcessionLecture 1 31 Textbooks and Programming Tool  Prescribed:  Alasdair McAndrew: Introduction to Digital Image Processing with Matlab, 2004. (We should cover major sections of the book)  Other references:  Rafael C. Gonzalez, Richard E. Woods: Digital Image Processing. Prentice Hall; 2nd edition, 2002  Programming: Matlab with Image Processing Toolbox  Prescribed:  Alasdair McAndrew: Introduction to Digital Image Processing with Matlab, 2004. (We should cover major sections of the book)  Other references:  Rafael C. Gonzalez, Richard E. Woods: Digital Image Processing. Prentice Hall; 2nd edition, 2002  Programming: Matlab with Image Processing Toolbox

32. Digital Image ProcessionLecture 1 32 Example: Detection of ozone layer hole Over the Antarctic, normal value around 300 DU

33. Digital Image ProcessionLecture 1 33 Looking ahead: lecture2  Image types  File format  Matlab programming.  Image types  File format  Matlab programming.