"图像处理手册（第5版）：全面指南与英文（matalb）原版"

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"The Image Processing Handbook (5th Edition) is a comprehensive guide to the theory and practice of image processing. Originally written in English and now available in a MATLAB version, this handbook is an essential tool for students, researchers, and professionals in the field of image processing. The book covers a wide range of topics, including image acquisition, image enhancement, image restoration, color image processing, and image compression. It also delves into advanced topics such as image segmentation, pattern recognition, and computer vision. With its detailed explanations, practical examples, and MATLAB code, this handbook is a valuable resource for anyone working with digital images. One of the key features of The Image Processing Handbook is its emphasis on practical applications. The book provides numerous examples of how image processing techniques can be used in real-world scenarios, such as medical imaging, remote sensing, and multimedia systems. The MATLAB code included in the book allows readers to experiment with the techniques and apply them to their own projects. In addition to its practical focus, the handbook also provides a solid theoretical foundation for image processing. The authors cover the mathematical and statistical principles behind image processing algorithms, making the book suitable for readers with a strong technical background. However, the clear and concise writing style also makes the material accessible to those new to the field. The Image Processing Handbook (5th Edition) is a valuable reference for anyone working in the field of image processing. Its combination of theoretical and practical content, along with the inclusion of MATLAB code, makes it a comprehensive and useful resource. Whether you are a student, researcher, or professional, this handbook will help you understand and apply the latest techniques in image processing."

Determining location ...................................... 549

Orientation ................................................... 553

Neighbor relationships ................................... 554

Alignment ..................................................... 559

Counting features .......................................... 565

Special counting procedures ........................... 568

Feature size .................................................. 572

Circles and ellipses ........................................ 574

Caliper dimensions ........................................ 576

Perimeter ...................................................... 579

Describing shape .......................................... 581

Fractal dimension .......................................... 585

Harmonic analysis ......................................... 589

Topology ...................................................... 593

Three‑dimensional measurements ..................... 594

11 Feature Recognition and

Classiﬁcation . . . . . . . . . . . . . . . . . .599

Template matching and cross‑correlation .......... 599

Parametric Description ................................... 601

Decision Points .............................................. 607

Multidimensional classiﬁcation ........................ 610

Learning systems ........................................... 615

kNN and cluster analysis ............................... 619

Expert Systems .............................................. 622

Neural Networks ........................................... 624

Syntactical models ......................................... 626

12 Tomographic Imaging . . . . . . . . . . .629

Volume imaging vs. sections ........................... 629

Basics of reconstruction .................................. 632

Algebraic reconstruction methods .................... 637

Maximum entropy ......................................... 641

Defects in reconstructed images ...................... 642

Beam hardening ........................................... 646

Imaging geometries ....................................... 652

Three‑dimensional tomography ....................... 656

High‑resolution tomography ............................ 663

13 3‑D Image Visualization . . . . . . . . .667

Sources of 3‑D data ....................................... 667

Serial sections ............................................... 669

Optical sectioning ......................................... 673

Sequential removal ........................................ 675

Stereo measurement ...................................... 677

3‑D data sets ................................................ 682

Slicing the data set ........................................

684

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Acquiring Images

Human reliance on images for information

umans are primarily visual creatures. As discussed in Chapter 2, not all animals depend

on their eyes, as we do, for most of the information received about their surroundings.

This bias in everyday life extends to how we pursue more technical goals as well. Sci-

entiﬁc instruments commonly produce images to communicate their results to the operator,

rather than generating audible tones or emitting a smell. Space missions to other planets and

equally arduous explorations of the ocean depths always include cameras as major compo-

nents, and we judge the success of those missions by the quality of the images returned. This

suggests a few of the ways in which we have extended the range of our natural vision. Simple

optical devices such as microscopes and telescopes allow us to see things that are vastly

smaller or larger than we could otherwise. Beyond the visible portion of the electromagnetic

spectrum (a narrow range of wavelengths between about 400 and 700 nm), we now have sen-

sors capable of detecting infrared and ultraviolet light, X-rays, and radio waves, and perhaps

soon even gravity waves. Figure 1.1 shows an example, an image presenting radio-telescope

data in the form of an image in which color represents the Doppler shift in the radio signal.

Such devices and presentations are used to further extend our imaging capability.

Signals other than electromagnetic radiation can be used to produce images, too. Novel types

of microscopes that use atomic-scale probes to “feel” a specimen surface present their data as

images. Acoustic waves at low frequency produce sonar images, while at gigahertz frequen-

cies the acoustic microscope produces images with resolution similar to that of the light mi-

croscope, but with image contrast that is produced by local variations in the attenuation and

refraction of sound waves rather than light. Figure 1.2 shows an acoustic microscope image

of a composite material, and Figure 1.3 shows a sonogram of a baby in the womb.

Some images such as holograms or electron diffraction patterns are recorded to show bright-

ness as a function of position, but such images are unfamiliar to the observer. Figure 1.4 and

Figure 1.5 show electron diffraction patterns from a transmission electron microscope, in

which the atomic structure of the samples is revealed (but only to those who know how to

interpret the image). Other kinds of data — including weather maps with specialized symbols,

graphs of business proﬁt and expenses, and charts with axes representing time, family income,

cholesterol level, or even more obscure parameters — have become part of our daily lives.

7254_Book.indb 1 11/1/06 3:39:41 PM

Figure 1.6 shows a few common examples. The latest developments in computer interfaces

and displays make extensive use of graphics, again to take advantage of the large bandwidth of

the human visual pathway. Tufte (1990, 1997, 2001) in particular has demonstrated the power

of appropriate graphics to communicate complex information.

There are some important differences between human vision, the kind of information it ex-

tracts from images, and the ways in which it seems to do so, as compared with the use of imag-

ing devices based on computers for technical purposes. Humans are especially poor at judging

color or brightness of features within images unless they can be exactly compared by making

them adjacent. Human vision is inherently comparative rather than quantitative, responding to

the relative size, angle, or position of several objects but unable to supply numeric measures

unless one of the reference objects is a measuring device. Overington (1976, 1992) disagrees

with this widely accepted and documented conclusion but presents no compelling counterevi-

dence. Chapter 2 shows some of the consequences of the characteristics of human vision as

they affect what we see.

This book’s purpose is not to study the human visual pathway directly, but the overview in

Chapter 2 can help us to understand how we see things so that we become better observers.

Computer-based image processing and analysis use algorithms based on human vision meth-

ods in some cases, but also employ other methods that seem not to have direct counterparts

Figure 1.1 Radio astronomy pro-

duces images such as this view of

Messier 33. These are often dis-

played with false colors to empha-

size subtle variations in signal

or, as in this example, Doppler

shift. (This image was generat-

ed with data from telescopes of

the National Radio Astronomy

Observatory, a National Science

Foundation Facility managed by

Associated Universities, Inc. With

permission.)

Figure 1.2 Scanning acoustic

microscope image (with

superimposed signal proﬁle

along one scan line) of

a polished cross section

through a composite. The

central white feature is a

ﬁber intersecting the surface

at an angle. The peaks on

either side are interference

patterns that can be used to

measure the ﬁber angle.

2 The Image Processing Handbook, Fifth Edition

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