1. SCCS 4761 Introduction What is Image Processing? Fundamental of Image Processing

2. SCCS 4762 Image & Image Processing Image and Pictures –An image is a single picture that represents something What is image processing? –Interest in digital image processing methods stems from two application areas: Improvement of pictorial information for human interpretation Processing of image data for storage, transmission, and representation for autonomous machine perception

3. SCCS 4763 Example 1: Contrast Enhancement BEFOREAFTER Gamma = 0.5 http://www.mathworks.com/access/helpdesk/help/toolbox/images/enhanc17.html

4. SCCS 4764 Example 2: Sharpening BEFORE AFTER

5. SCCS 4765 Example 3: Denoising BEFOREAFTER http://www.mathworks.com/access/helpdesk/help/toolbox/images/enhan23b.html#14283

6. SCCS 4766 Example 3: Edge Extraction http://www.cee.hw.ac.uk/hipr/html/canny.html

7. SCCS 4767 Example 4: Blurring http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node99.html

8. SCCS 4768 Example 5: Blurring & Sharpening http://www.uwec.edu/walkerjs/DSP/sharpening_images.htm

9. SCCS 4769 Example 6:Image Enhancement

10. SCCS 47610 Example 6: Image Enhancement

11. SCCS 47611 1.3 Image Acquisition and Sampling t A How many points must be used to represent this curve? At least at the rate of Nyquist rate (twice the maximum frequency in the function). Sampling

12. SCCS 47612 Undersampling Definition: sampling signals with too few points Effects: aliasing (jagged edge in image)

13. SCCS 47613 1.3.1 Using Light & Other Energy Sources Light is the predominant energy source for images. Digital images are captured using visible light, infrared, ultraviolet, etc. http://www.yorku.ca/eye/spectrum.gif

14. SCCS 47614 1.3.2 Image Acquisition Camera: digital camera –CCD (Charge-Coupled Device) –CMOS (Complementary Metal Oxide Semi- conductor) Flat-bed scanner –Scan row by row –Examples: Computed Axial Tomography, MRI, etc.

15. SCCS 47615 Imaging Sensors (a) Single sensor (b) Line sensor (c) Array sensor

16. SCCS 47616 Digital image acquisition process

17. SCCS 47617 1.4 Images and Digital Images

18. SCCS 47618 Image Sampling & Quantization Quantization Image Sampling

19. SCCS 47619 Pixel Picture elements Pixel

20. SCCS 47620 Digital Images

21. SCCS 47621 Image Representation Consider an image as a matrix Intensity of pixel ( x,y ), f( x,y ), is the member at row x and column y of the matrix Lexicographic ordering: –Rearrange image matrix into 1-D vector format –Concatenate the row together –Pixel at (x,y) is at the position x  WIDTH + y

22. SCCS 47622 Neighborhood 485049480 52553116 44535110105 510111123112 1122120111115 3  3 neighborhood (usually is the odd number)

23. SCCS 47623 1.5 Applications of Image Processing Medicine –Inspection and investigation of images obtained from x-rays, MRI, CAT scans –Analysis of cell images and chromosome karyotypes Agriculture –Satellite/aerial views of land: determine how much land is being used –Inspection of fruit and vegetables: distinguish good and fresh produce from old Industry –Automatic inspection of items on a production line –Inspection of paper samples Law enforcement –Fingerprint analysis

24. SCCS 47624 1.6 Aspects of Image Processing (image processing algorithm ) Image Enhancement : processing an image so that the result is more suitable for a particular application. –sharpening or deblurring –highlighting edges –improving image contrast or brightening image –removing noise

25. SCCS 47625 1.6 Aspects of Image Processing (cont) Image Restoration: An image may be restored by the damage done to it by known cause, for example –removing of blur caused by linear motion –removing of optical distortions –removing periodic interference Note: (i) enhancement – make it look better, (ii) restoration – remove damage

26. SCCS 47626 1.6 Aspects of Image Processing (cont) Image Segmentation: Segmentation involves subdividing an image into constitute parts –finding lines, circles, or particular shapes in an image –Identifying cars, trees buildings, or roads in an aerial photograph

27. SCCS 47627 1.7 Image Processing Task Real-world application: A system for reading the postal codes from envelopes –Image Acquisition. First we need to produce a digital image from a paper envelop. This can be done using either CCD camera or a scanner. –Preprocessing. Use some image processing algorithms to obtain the resulting image more suitable for the later process. In this application it may involve enhancing the contrast, removing noises, or identifying regions likely to contain the postal code. –Segmentation. Use some image processing algorithms to extract the region that contains postal code from the image

28. SCCS 47628 1.7 Image Processing Task (cont) –Representation and description. Extracting the particular features to differentiate between objects. Here suppose we will be looking for curves, holes, and corners that allow us to distinguish the different digits that constitute a postal code. –Recognition and interpretation. Assigning labels to objects based on their descriptors (from the previous step) and assigning meanings to these labels. We identify particular digits, and interpret a string of digits at the end of the address as the postal code.

29. SCCS 47629 1.8 Types of Digital Images Binary –1 bit/pixel (black & white image) Grayscale –8 bit/pixel (gray image) Color image: –true color: 24 bit/pixel (Red, Green Blue, 255 3 colors) Indexed image: 8 bit/pixel (color image with 256 colors)

30. SCCS 47630 Binary Image Two color: black and white. No gray. Value range: 0 : black, 1 : white http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=4052&objectType=file

31. SCCS 47631 Grayscale Image Use black (0), white (255) and shades of gray Value range: 0-255

32. SCCS 47632 Color Image 3 bytes for 1 pixel R = [0,255], G = [0, 255], B = [0,255]

33. SCCS 47633 Indexed Image Mostly 1 byte for 1 pixel Good for image composing of less than 256 colors

34. SCCS 47634 Indexed Image (cont) 1334 5443 4343 3321 0.1211 0.1530 0.1234 0.1807 0.3447 0.1729 0.2627 0.2588 0.2549 0.2197 0.2432 0.2588 0.1611 0.1768 0.1924 0.2432 0.2471 0.1924. Indices (value of the pixel) Color Map (Palette) Index 0 Color: (0.1807, 0.3447, 0.1729) Color: (0.1611, 0.1768, 0.1924)

35. SCCS 47635 1.9 Size of Image File Total number of pixel = Width  Height Size = Total number of pixel  size of pixel = Width  Height  #bit/pixel [bits] = Width  Height  #byte/pixel [byte]

36. SCCS 47636 Examples: File Size Binary image: –Width = 352 –Height = 288 –Size = ? Grayscale image: –Width = 352 –Height = 288 –Size = ?

37. SCCS 47637 1.10 Image Perception Much of image processing in concerned with making an image appear better to human beings. Therefore, we should be aware of the limitations of the human visual system. Image perception consists of –capture the image with the eye –recognize and interpret the image with the visual cortex in the brain

38. SCCS 47638 Limitations of Human Visual System Observed intensities vary as to the background

39. SCCS 47639 Limitations of Human Visual System (cont) Observation of nonexistence intensity as bars in continuously varying gray level

40. SCCS 47640 Limitations of Human Visual System (cont)

41. SCCS 47641 Limitations of Human Visual System (cont) False contouring

42. SCCS 47642 Limitations of Human Visual System (cont) Undershoot or overshoot around the boundary of regions of different intensities –Boundary appeared brighter when seeing from dark to bright region. –Boundary appeared darker when seeing from bright to dark region

43. SCCS 47643 Limitations of Human Visual System (cont) Mach bands