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Computer Vision 2

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  1. 1. Chapter 2Image Acquisition
  2. 2. This chapter includes: • Introduction • Image Sensors • Representation of the Image Data •Types of digital images
  3. 3. Introduction• The aim of image acquisition sub- system is:• to transform optical image data into an array of numerical data.
  4. 4. Introduction• Three major issues for image acquisition:1. sensing,2. representation,3. digitization.
  5. 5. Image Sensors• Image sensing is carried out by different techniques.• The most commonly used devices are: 1. Vidicon cameras, 2. solid-state arrays, 3. laser scanners.
  6. 6. The Vidicon• The Vidicon is a device used to transform optical images into electrical signals.• An electron beam in the tube is deflected to scan the image.• The net current through the photo conductive surface varies according to the scanned image position.
  7. 7. The Vidicon • The Vidicon , while relatively inexpensive, has some disadvantages. • The signal contains a large component of. high-frequency noise; • fragile and easily broken by vibration or shock.
  8. 8. Solid-State Image Devices •These elements are highly durable, compact, and attaining higher resolution.. •The two main kinds of sensors used in digital cameras are: • CCD (charge coupled device) • CMOS (complementary metal oxide on silicon)
  9. 9. Solid-State Image Devices ..
  10. 10. Solid-State Image Devices (CCD) • In a CCD, photons are accumulated in each active well during the exposure time. • The charges are transferred from well to well and convert it to voltage at output node..
  11. 11. Solid-State Image Devices (CMOS) • In CMOS, the photos hitting the sensor directly affect the conductivity (or gain) of a photosensitive transistor. • The resulting voltage is then amplified and sampled ..
  12. 12. Laser Scanner • Particularly important in industrial applications. • devices that obtain a “ depth map “.. • The laser light is transmitted and then measuring the phase of the arriving reflected light.
  13. 13. Representation of the Image Data Representation should fulfill two requirements: 1. Facilitate processing by means of a. computer. 2. Contain all the information that defines characteristics of the image.
  14. 14. Representation of the Image Data • The optical sub-system will deliver a continuous two-dimensional function f(x,y). • f(x, y) represents the intensity of light at. each point. • f(x, y) is quantized so that it can be represented as an array of numbers.
  15. 15. Representation of the Image Data Two forms of quantization: • Spatial Quantization • Amplitude ( intensity ) Quantization.
  16. 16. Spatial Quantization • The image is sampled at (m x n) discrete points. • Each sample is called a picture cell ( “pixel”)..
  17. 17. PixelsPixels is the smallest addressable area of a display.The word pixel comes from “picture element”.
  18. 18. PixelsThe resolution of an image is described as the numberof pixels horizontally times the number of pixelsvertically. A 10x7 image
  19. 19. PixelsWe will refer to a pixel by the number of its row andthe number of its column. 1 2 3 4 5 6 7 8 9 10 1 2 3 4 This is the (3,7) 5 pixel 6 7
  20. 20. PixelsBy this convention, the x-axis is vertical and the y-axis ishorizontal.This is consistent with the way we refer to the elements of amatrix. y This is the (3,7) pixel x
  21. 21. Amplitude Quantization • Each pixel assigned a numerical code. • The code represents the intensity of the image function at that point. • The resolution of the code is determined by the number of quantization levels ( gray. levels ).
  22. 22. Amplitude Quantization • The set of the gray levels ranging from black to white is called the gray scale of the system. • The number of gray levels is usually an. integral power of 2, such that: • black = 0 - white =2L – 1 • where L is an integer and there are 2L gray levels in the gray scale.
  23. 23. Digital images• We consider the image as being a two dimensional function,• The function values give the brightness of the image at any given point
  24. 24. Digital images• A digital image is obtained by quantizing the output signals obtained from image acquisition devices.• We consider a digital image as a matrix.• Its rows and columns indices identify a point in the image.• The matrix element value indicates the gray level at that point.
  25. 25. Digital images• A digital image differs from a photo in that the x, y and f(x, y) values are all discrete. Usually they take on only integer values,
  26. 26. Color Digital images• An image is broken into thousands of pixels.• An image stored in this way is called a bitmap.• Pixels are represented by three numbers. • Red 0-255 • Blue 0-255 • Green 0-255
  27. 27. Types of Digital Images• Black&white images –Binary images (1-bit images) –Grayscale images (8-bit gray-level images)• Color images – 24-bit color images – 8-bit color images
  28. 28. Binary Images• Each pixel is stored as a single bit (0 or 1),• The intensities of the pixels are either 0 or 1.• Such images are called binary and use only one bit per pixel.• Such an image is also called a 1-bit monochrome image since it contains no color.
  29. 29. Binary Images• An example was the image shown• we have only the two colors: white for the edges, and black for the background.
  30. 30. Monochrome 1-bit Lena image.Another example
  31. 31. Binary Images• To generate Binary image from grey scale image.• A Threshold value, T, is used to partition the image into pixels with just two values, such that :• IF f (x,y) >= T THEN g (x,y) = 1• IF f (x,y) < T THEN g (x,y) = 0• where g (x,y) denotes the binary version of f (x,y).
  32. 32. Image Data Structures• Pixels -- picture elements in digital images• Image Resolution -- number of pixels in a digital image :• Resolution = width x height• higher resolution always yields better quality.• File size = width x height x #ofBytesPerPixel
  33. 33. Binary ImagesFile size calculation:Resolution: 640 x 480File size = 640 x 480 x 1/8 = 38.4 kB
  34. 34. Grayscale images• Each pixel has a gray-value between 0 and 255.• The high values correspond to bright pixels and the low values correspond to dark pixels.• A dark pixel might have a value of 10, and a bright one might be 230.
  35. 35. Grayscale images• The intensities of the pixels are integers in the interval [0,255].• We use one byte of memory for each pixel.
  36. 36. Grayscale imagesThe whole image is described by an array ofnumbers called matrix. 0.09 0.76 0.12 0.43 0.98 0 0.32 0.25 0 0.39 0.89 0.23 0.35 0.34 0.34 0.54
  37. 37. Grayscale image of Lena.
  38. 38. Monochrome 1-bit Lena image. Grayscale image of Lena. 38 400 Bytes 307 200 Bytes
  39. 39. 8-bit Gray-level ImagesFile size calculation:Resolution: 640 x 480File size = 640 x 480 x 1 = 307 200 = 300 kB
  40. 40. Colour images• Colour image are usually described in the RGB colour space.• The primary colours red, green and blue are combined to reproduce other colours.
  41. 41. Colour imagesA colour image is described by three matrices. 0.56 0.82 0.75 0.65 0.87 0.31 0.16 0.56 0.92 0.19 0.84 0.71 0.37 0.93 0.73 0.48 0.38 0.02
  42. 42. Colour images• In the RGB colour space, a colour is represented by a triplet (r,g,b)• r gives the intensity of the red component• g gives the intensity of the green component• b gives the intensity of the blue component• You will often see the values of r,g,b as integers in the interval [0,255].
  43. 43. Colour images• Each pixel is represented by three bytes (e.g., RGB)- 24-bit Color Images• Supports 256 x 256 x 256 possible combined colors (16,777,216)• A 640 x 480 24-bit color image would require 921.6 KB of storage
  44. 44. Indexed images• 8-bit Color Images• One byte for each pixel• Requires Color Look-Up Tables (LUTs)• A 640 x 480 8-bit color image requires 307.2 KB of storage (the same as 8-bit grayscale)
  45. 45. Indexed images• The image has an associated color map which is simply a list of all the colors used in that image.• Each pixel has a value which does not give its color (as for an RGB image), but an index to the color in the map.
  46. 46. 8-bit Color Images• Such image files use the concept of a lookup table to store color information.• Basically, the image stores not color, but instead a code value, for each pixel.• Each code is actually an index into a table with 3-byte values that specify the color for a pixel with that lookup table index.
  47. 47. Color Look-up Tables (LUTs)• The idea used in 8-bit color images is to store only the index, or code value, for each pixel.• Then, if a pixel stores the value 25, the meaning is to go to row 25 in a color look-up table (LUT).