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Introduction

Introduction. What is Image Processing? Fundamental of Image Processing. 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:

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Introduction

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  1. Introduction What is Image Processing? Fundamental of Image Processing SCCS 476

  2. 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 SCCS 476

  3. Example 1: Contrast Enhancement BEFORE AFTER Gamma = 0.5 http://www.mathworks.com/access/helpdesk/help/toolbox/images/enhanc17.html SCCS 476

  4. Example 2: Sharpening BEFORE AFTER SCCS 476

  5. Example 3: Denoising BEFORE AFTER http://www.mathworks.com/access/helpdesk/help/toolbox/images/enhan23b.html#14283 SCCS 476

  6. Example 3: Edge Extraction http://www.cee.hw.ac.uk/hipr/html/canny.html SCCS 476

  7. Example 4: Blurring http://www.maths.abdn.ac.uk/~igc/tch/mx4002/notes/node99.html SCCS 476

  8. Example 5: Blurring & Sharpening http://www.uwec.edu/walkerjs/DSP/sharpening_images.htm SCCS 476

  9. Example 6:Image Enhancement SCCS 476

  10. Example 6: Image Enhancement SCCS 476

  11. 1.3 Image Acquisition and Sampling Sampling A t 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). SCCS 476

  12. Undersampling • Definition: sampling signals with too few points • Effects: aliasing (jagged edge in image) SCCS 476

  13. 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 SCCS 476

  14. 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. SCCS 476

  15. Imaging Sensors (a) Single sensor (b) Line sensor (c) Array sensor SCCS 476

  16. Digital image acquisition process SCCS 476

  17. 1.4 Images and Digital Images SCCS 476

  18. Image Sampling & Quantization Quantization Image Sampling SCCS 476

  19. Pixel Pixel • Picture elements SCCS 476

  20. Digital Images SCCS 476

  21. Image Representation • Consider an image as a matrix • Intensity of pixel (x,y), f(x,y), is the member at row x and column yof the matrix • Lexicographic ordering: • Rearrange image matrix into 1-D vector format • Concatenate the row together • Pixel at (x,y) is at the positionx WIDTH + y SCCS 476

  22. 48 50 49 48 0 48 52 55 3 116 44 53 5 110 105 51 0 111 123 112 1 122 120 111 115 Neighborhood 3  3 neighborhood (usually is the odd number) SCCS 476

  23. 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 SCCS 476

  24. 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 SCCS 476

  25. 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 SCCS 476

  26. 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 SCCS 476

  27. 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 SCCS 476

  28. 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. SCCS 476

  29. 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, 2553 colors) • Indexed image: 8 bit/pixel (color image with 256 colors) SCCS 476

  30. Binary Image • Two color: black and white. No gray. • Value range: 0 : black, 1 : white SCCS 476 http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=4052&objectType=file

  31. Grayscale Image • Use black (0), white (255) and shades of gray • Value range: 0-255 SCCS 476

  32. Color Image • 3 bytes for 1 pixel • R = [0,255], G = [0, 255], B = [0,255] SCCS 476

  33. Indexed Image • Mostly 1 byte for 1 pixel • Good for image composing of less than 256 colors SCCS 476

  34. 1 3 3 4 5 4 4 3 4 3 4 3 3 3 2 1 Indexed Image (cont) 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 . . . Index 0 Color: (0.1807, 0.3447, 0.1729) Color: (0.1611, 0.1768, 0.1924) Indices (value of the pixel) Color Map (Palette) SCCS 476

  35. 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] SCCS 476

  36. Examples: File Size • Binary image: • Width = 352 • Height = 288 • Size = ? • Grayscale image: • Width = 352 • Height = 288 • Size = ? SCCS 476

  37. 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 SCCS 476

  38. Limitations of Human Visual System • Observed intensities vary as to the background SCCS 476

  39. Limitations of Human Visual System (cont) • Observation of nonexistence intensity as bars in continuously varying gray level SCCS 476

  40. Limitations of Human Visual System (cont) SCCS 476

  41. Limitations of Human Visual System (cont) False contouring SCCS 476

  42. 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 SCCS 476

  43. Limitations of Human Visual System (cont) Mach bands SCCS 476

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