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Introduction to Digital Image Processing with MATLAB Asia Edition.ppt
1.
1 © 2010 Cengage
Learning Engineering. All Rights Reserved. 1 Introduction to Digital Image Processing with MATLAB® Asia Edition McAndrew‧Wang‧Tseng Chapter 1: Introduction
2.
2 © 2010 Cengage
Learning Engineering. All Rights Reserved. 1.1 Images and Pictures • Human have evolved very precise visual skills: We can identify a face in an instant We can differentiate colors We can process a large amount of visual information very quickly Ch1-p.1
3.
3 © 2010 Cengage
Learning Engineering. All Rights Reserved. • Image processing involves changing the nature of an image in order to either 1. improve its pictorial information for human interpretation, or 2. render it more suitable for autonomous machine perception 1.2 What Is Image Processing? Ch1-p.1
4.
4 1.2 What Is
Image Processing? © 2010 Cengage Learning Engineering. All Rights Reserved. • Enhancing the edges of an image to make it appear sharper (Figure 1.1) • Note how the second image appears cleaner; it is a more pleasant image • Sharpening edges is a vital component of printing Condition 1. Ch1-p.2
5.
5 FIGURE 1.1 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.2
6.
6 1.2 What Is
Image Processing? © 2010 Cengage Learning Engineering. All Rights Reserved. • removing noise from an image, noise being random errors in the image (Figure 1.2) • removing motion blur from an image. An example is given in Figure 1.3 • In Figure 1.3(b), it is easier to read the number plate and to see the spikes on the fence behind the car, as well as other details not at all clear in the original image Figure 1.3(a). Ch1-p.2-3
7.
7 FIGURE 1.2 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.3
8.
8 FIGURE 1.3 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.3
9.
9 1.2 What Is
Image Processing? © 2010 Cengage Learning Engineering. All Rights Reserved. • Obtaining the edges of an image (Figure 1.4) • Once we have the edges we can measure their spread and the area contained within them • We can also use edge-detection algorithms as a first step in edge enhancement Condition 2. Ch1-p.3-4
10.
10 FIGURE 1.4 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.4
11.
11 1.2 What Is
Image Processing? © 2010 Cengage Learning Engineering. All Rights Reserved. • For measurement or counting purposes, we may not be interested in all the detail in an image (Figure 1.5) • We could, for example, measure the size and shape of the animal without being distracted by unnecessary detail Ch1-p.4-5
12.
12 FIGURE 1.5 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.4
13.
13 © 2010 Cengage
Learning Engineering. All Rights Reserved. 1.3 Image Sampling and Acquisition • Sampling refers to the process of digitizing a continuous function • e.g., sample it at 10 evenly spaced values of x only (Figure 1.6) sample it at 100 points, as shown in Figure 1.7 Ch1-p.5
14.
14 FIGURE 1.6 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.5
15.
15 FIGURE 1.7 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.5
16.
16 1.3 Image Sampling
and Acquisition © 2010 Cengage Learning Engineering. All Rights Reserved. • Nyquist criterion which says, in effect, that a continuous function can be reconstructed from its samples provided that the sampling frequency is at least twice the maxi-mum frequency in the function Ch1-p.5
17.
17 FIGURE 1.8 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.6
18.
18 © 2010 Cengage
Learning Engineering. All Rights Reserved. • Image Acquisition CCD CAMERA FIGURE 1.9 Ch1-p.7
19.
19 FIGURE 1.10 © 2010
Cengage Learning Engineering. All Rights Reserved. FLAT-BED SCANNER Ch1-p.8
20.
20 FIGURE 1.11 © 2010
Cengage Learning Engineering. All Rights Reserved. OTHER ENERGY SOURCES Ch1-p.8
21.
21 FIGURE 1.12 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.9
22.
22 1.4 Images and
Digital Images © 2010 Cengage Learning Engineering. All Rights Reserved. • We may consider this image as being a two- dimensional function f (x, y) • We may assume that in such an image, brightness values can be any real numbers in the range 0.0 (black) to 1.0 (white) • The f (x, y) values in a digital image take only integer values ranging from 1 to 256 each and the brightness values ranging from 0 (black) to 255 (white) Ch1-p.9-10
23.
23 FIGURE 1.13 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.10
24.
24 FIGURE 1.14 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.11
25.
25 1.4 Images and
Digital Images © 2010 Cengage Learning Engineering. All Rights Reserved. • A digital image can be considered as a large array of sampled points from the continuous image • These points are the pixels, which constitute the digital image • The pixels surrounding a given pixel constitute its neighborhood. Ch1-p.10
26.
26 FIGURE 1.15 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.11
27.
27 1.5 Some Applications ©
2010 Cengage Learning Engineering. All Rights Reserved. • Medicine • Agriculture • Industry • Law enforcement Ch1-p.11-12
28.
28 1.6 Aspects of
Image Processing © 2010 Cengage Learning Engineering. All Rights Reserved. • IMAGE ENHANCEMENT sharpening or deblurring an out-of-focus image highlighting edges, improving image contrast or brightening an image, and removing noise • IMAGE RESTORATION removing of blur caused by linear motion, removal of optical distortions, and removing periodic interference Ch1-p.12
29.
29 1.6 Aspects of
Image Processing © 2010 Cengage Learning Engineering. All Rights Reserved. • IMAGE SEGMENTATION finding lines, circles, or particular shapes in an image, and identifying cars, trees, buildings, or roads in an aerial photograph • A given algorithm may be used for both image enhancement or for image restoration Ch1-p.12-13
30.
30 1.7 An Image-Processing
Task © 2010 Cengage Learning Engineering. All Rights Reserved. • Acquiring the image • Preprocessing • Segmentation • Representation and description • Recognition and interpretation Ch1-p.13
31.
31 FIGURE 1.16 © 2010
Cengage Learning Engineering. All Rights Reserved. • Binary Ch1-p.14
32.
32 FIGURE 1.17 © 2010
Cengage Learning Engineering. All Rights Reserved. • Grayscale Ch1-p.14
33.
33 FIGURE 1.18 © 2010
Cengage Learning Engineering. All Rights Reserved. • True color or red-green-blue Ch1-p.15
34.
34 FIGURE 1.19 © 2010
Cengage Learning Engineering. All Rights Reserved. • Indexed Ch1-p.16
35.
35 1.9 Image File
Sizes © 2010 Cengage Learning Engineering. All Rights Reserved. • A 512 × 512 binary image • A grayscale image (Divided by 8) Ch1-p.16
36.
36 1.9 Image File
Sizes © 2010 Cengage Learning Engineering. All Rights Reserved. • A Color image Ch1-p.17
37.
37 1.10 Image Perception ©
2010 Cengage Learning Engineering. All Rights Reserved. • We should be aware of the limitations of the human visual system Observed intensities vary as to the background We may observe nonexistent intensities as bars in continuously varying gray levels Our visual system tends to undershoot or overshoot around the boundary of regions of different intensities Ch1-p.17-18
38.
38 FIGURE 1.20 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.17
39.
39 FIGURE 1.21 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.18
40.
40 FIGURE 1.22 © 2010
Cengage Learning Engineering. All Rights Reserved. Ch1-p.18
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