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Transcript of Ch2
![Page 1: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/1.jpg)
Chapter 2
Image Acquisition
![Page 2: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/2.jpg)
This chapter includes:• Introduction
• Image Sensors
• Representation of the Image Data
• Types of digital images
![Page 3: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/3.jpg)
• The aim of image acquisition sub-system is:
• to transform optical image data into an array of numerical data.
Introduction
![Page 4: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/4.jpg)
• Three major issues for image acquisition:
1. sensing,
2. representation,
3. digitization.
Introduction
![Page 5: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/5.jpg)
• Image sensing is carried out by different techniques.
• The most commonly used devices are:1. Vidicon cameras,2. solid-state arrays,3. laser scanners.
Image Sensors
![Page 6: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/6.jpg)
• 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.
The Vidicon
![Page 7: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/7.jpg)
• 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.
.
The Vidicon
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.
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)
![Page 9: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/9.jpg)
.
.
Solid-State Image Devices
![Page 10: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/10.jpg)
• 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.
.
Solid-State Image Devices (CCD)
![Page 11: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/11.jpg)
• 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 .
.
Solid-State Image Devices (CMOS)
![Page 12: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/12.jpg)
• 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.
.
Laser Scanner
![Page 13: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/13.jpg)
Representation should fulfill two requirements:
1. Facilitate processing by means of a computer.
2. Contain all the information that defines characteristics of the image.
.
Representation of the Image Data
![Page 14: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/14.jpg)
.
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.
![Page 15: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/15.jpg)
.
Representation of the Image Data
Two forms of quantization:• Spatial Quantization• Amplitude ( intensity ) Quantization
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.
Spatial Quantization
• The image is sampled at (m x n) discrete points.
• Each sample is called a picture cell ( “pixel”).
![Page 17: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/17.jpg)
Pixels
Pixels is the smallest addressable area of a display.
The word pixel comes from “picture element”.
![Page 18: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/18.jpg)
PixelsThe resolution of an image is described as the number of pixels horizontally times the number of pixels vertically.
A 10x7 image
![Page 19: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/19.jpg)
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
2
1
3
4
5
6
7
This is the (3,7) pixel
![Page 20: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/20.jpg)
PixelsBy 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.
This is the (3,7) pixel
y
x
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.
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 ).
![Page 22: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/22.jpg)
.
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.
![Page 23: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/23.jpg)
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
![Page 24: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/24.jpg)
• 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.
Digital images
![Page 25: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/25.jpg)
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,
![Page 26: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/26.jpg)
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
![Page 27: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/27.jpg)
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
![Page 28: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/28.jpg)
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.
![Page 29: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/29.jpg)
Binary Images• An example was the image shown
• we have only the two colors: white for the edges, and black for the background.
![Page 30: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/30.jpg)
Monochrome 1-bit Lena image.
Another example
![Page 31: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/31.jpg)
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).
![Page 32: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/32.jpg)
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
![Page 33: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/33.jpg)
Binary ImagesFile size calculation:
Resolution: 640 x 480
File size = 640 x 480 x 1/8 = 38.4 kB
![Page 34: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/34.jpg)
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.
![Page 35: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/35.jpg)
Grayscale images
• The intensities of the pixels are integers in the interval [0,255].
• We use one byte of memory for each pixel.
![Page 36: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/36.jpg)
Grayscale images
The whole image is described by an array of numbers called matrix.
54.034.034.035.0
23.089.039.00
25.032.0098.0
43.012.076.009.0
![Page 37: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/37.jpg)
Grayscale image of Lena.
![Page 38: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/38.jpg)
Grayscale image of Lena.Monochrome 1-bit Lena image.
307 200 Bytes38 400 Bytes
![Page 39: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/39.jpg)
8-bit Gray-level Images
File size calculation:
Resolution: 640 x 480
File size = 640 x 480 x 1 = 307 200 = 300 kB
![Page 40: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/40.jpg)
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.
![Page 41: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/41.jpg)
Colour images
A colour image is described by three matrices.
31.087.065.0
75.082.056.0
02.038.048.0
73.093.037.071.084.019.0
92.056.016.0
![Page 42: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/42.jpg)
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].
![Page 43: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/43.jpg)
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
![Page 44: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/44.jpg)
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)
![Page 45: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/45.jpg)
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.
![Page 46: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/46.jpg)
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.
![Page 47: Ch2](https://reader033.fdocuments.us/reader033/viewer/2022061217/54b4dc0e4a7959bc678b465f/html5/thumbnails/47.jpg)
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).