Computer Vision 2

1. Chapter 2
Image Acquisition

2. This chapter includes:
• Introduction
• Image Sensors
• Representation of the Image
Data
•Types of digital images

3. Introduction
• The aim of image acquisition sub-
system is:
• to transform optical image data into
an array of numerical data.

4. Introduction
• Three major issues for image
acquisition:
1. sensing,
2. representation,
3. digitization.

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. 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. 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. 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. Solid-State Image Devices
.
.

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. 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. 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. 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. 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. Representation of the Image Data
Two forms of quantization:
• Spatial Quantization
• Amplitude ( intensity ) Quantization
.

16. Spatial Quantization
• The image is sampled at (m x n) discrete
points.
• Each sample is called a picture cell
( “pixel”).
.

17. Pixels
Pixels is the smallest addressable area of a display.
The word pixel comes from “picture element”.

18. Pixels
The resolution of an image is described as the number
of pixels horizontally times the number of pixels
vertically.
A 10x7 image

19. Pixels
We will refer to a pixel by the number of its row and
the 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. Pixels
By this convention, the x-axis is vertical and the y-axis is
horizontal.
This is consistent with the way we refer to the elements of a
matrix.
y
This is the (3,7) pixel
x

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. 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. 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. 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. 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. 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. 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. 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. Binary Images
• An example was the image shown
• we have only the two colors: white for the
edges, and black for the background.

30. Monochrome 1-bit Lena image.
Another example

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. 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. Binary Images
File size calculation:
Resolution: 640 x 480
File size = 640 x 480 x 1/8 = 38.4 kB

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. Grayscale images
• The intensities of the pixels are integers in the
interval [0,255].
• We use one byte of memory for each pixel.

36. Grayscale images
The whole image is described by an array of
numbers 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. Grayscale image of Lena.

38. Monochrome 1-bit Lena image. Grayscale image of Lena.
38 400 Bytes 307 200 Bytes

39. 8-bit Gray-level Images
File size calculation:
Resolution: 640 x 480
File size = 640 x 480 x 1 = 307 200 = 300 kB

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. Colour images
A 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. 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. 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. 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. 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. 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. 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).