Digital Image Processing, 2/E is a completely self-contained book. The A database containing images from the book and other educational sources. Completely self-contained—and heavily illustrated—this introduction to basic concepts and methodologies for digital image processing is. these discrete coordinates. In many image processing books, the image origin A digital image can be represented as a MATLAB matrix: f f(1, 1) f(1, 2) f(1, N).
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Enrique Jardiel Poncela This edition is the most comprehensive revision of Digital Image Processing since the book first appeared in As the and . PDF | This book is an attempt to present the advances in digital image processing and analysis in the form of a textbook for both undergraduate. PDF | On Jul 7, , Mahmut Sinecen and others published Digital Image Processing with In book: Applications from Engineering with MATLAB Concepts.
Generally, the image acquisition stage involves preprocessing, such as scaling. Image enhancement is among the simplest and most appealing areas of dig- ital image processing.
Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image.
Two chapters are de- voted to enhancement, not because it is more important than the other topics covered in the book but because we use enhancement as an avenue to introduce the reader to techniques that are used in other chapters as well.
Thus, rather than having a chapter dedicated to mathematical preliminaries, we introduce a number of needed mathematical concepts by showing how they apply to en- hancement. This approach allows the reader to gain familiarity with these con- cepts in the context of image processing.
A good example of this is the Fourier transform, which is introduced in Chapter 4 but is used also in several of the other chapters. Image restoration is an area that also deals with improving the appearance of an image.
However, unlike enhancement, which is subjective, image restora- tion is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Color image processing is an area that has been gaining in importance be- cause of the significant increase in the use of digital images over the Internet.
Chapter 5 covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image. Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data com- pression and for pyramidal representation, in which images are subdivided suc- cessively into smaller regions.
Al- though storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity.
This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar perhaps inadvertently to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG Joint Photographic Experts Group image compression standard. Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape.
The material in this chapter begins a transition from processes that output images to processes that output image attributes, as indicated in Section 1. Segmentation procedures partition an image into its constituent parts or ob- jects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually.
On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more ac- curate the segmentation, the more likely recognition is to succeed. Representation and description almost always follow the output of a seg- mentation stage, which usually is raw pixel data, constituting either the bound- ary of a region i.
In either case, converting the data to a form suitable for computer processing is necessary.
Digital Image Processing Using MATLAB, 2e
The first decision that must be made is whether the data should be represented as a boundary or as a com- plete region. Boundary representation is appropriate when the focus is on ex- ternal shape characteristics, such as corners and inflections.
Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations comple- ment each other. Choosing a representation is only part of the solution for trans- forming raw data into a form suitable for subsequent computer processing.
A method must also be specified for describing the data so that features of inter- est are highlighted. Description, also called feature selection, deals with extract- ing attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.
Recognition is the process that assigns a label e. As detailed in Section 1. So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig. Knowledge about a problem domain is coded into an image process- ing system in the form of a knowledge database. This knowledge may be as sim- ple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information.
In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig.
Although we do not discuss image display explicitly at this point, it is impor- tant to keep in mind that viewing the results of image processing can take place at the output of any stage in Fig. We also note that not all image processing applications require the complexity of interactions implied by Fig.
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In fact, not even all those modules are needed in some cases. For example, image enhance- ment for human visual interpretation seldom requires use of any of the other stages in Fig. In general, however, as the complexity of an image processing task increases, so does the number of processes required to solve the problem. Late in the s and early in the s, the market shifted to image processing hardware in the form of sin- gle boards designed to be compatible with industry standard buses and to fit into engineering workstation cabinets and personal computers.
In addition to low- ering costs, this market shift also served as a catalyst for a significant number of new companies whose specialty is the development of software written specif- ically for image processing. Although large-scale image processing systems still are being sold for mas- sive imaging applications, such as processing of satellite images, the trend con- tinues toward miniaturizing and blending of general-purpose small computers with specialized image processing hardware.
The function of each component is discussed in the following paragraphs, starting with image sensing. With reference to sensing, two elements are required to acquire digital im- ages. The first is a physical device that is sensitive to the energy radiated by the object we wish to image.
The second, called a digitizer, is a device for convert- ing the output of the physical sensing device into digital form. For instance, in a digital video camera, the sensors produce an electrical output proportional to light intensity. The digitizer converts these outputs to digital data. These top- ics are covered in some detail in Chapter 2. Specialized image processing hardware usually consists of the digitizer just mentioned, plus hardware that performs other primitive operations, such as an arithmetic logic unit ALU , which performs arithmetic and logical operations in parallel on entire images.
One example of how an ALU is used is in averag- ing images as quickly as they are digitized, for the purpose of noise reduction. Image displays Computer Mass storage Specialized Image processing Hardcopy image processing software hardware Image sensors Problem domain distinguishing characteristic is speed. In other words, this unit performs functions that require fast data throughputs e.
The computer in an image processing system is a general-purpose computer and can range from a PC to a supercomputer. In dedicated applications, some- times specially designed computers are used to achieve a required level of per- formance, but our interest here is on general-purpose image processing systems.
In these systems, almost any well-equipped PC-type machine is suitable for off- line image processing tasks. Software for image processing consists of specialized modules that perform specific tasks. A well-designed package also includes the capability for the user to write code that, as a minimum, utilizes the specialized modules.
More so- phisticated software packages allow the integration of those modules and gen- eral-purpose software commands from at least one computer language. Mass storage capability is a must in image processing applications. When dealing with thousands, or even millions, of images, providing adequate storage in an image processing system can be a challenge. Storage is measured in bytes eight bits , Kbytes one thousand bytes , Mbytes one mil- lion bytes , Gbytes meaning giga, or one billion, bytes , and Tbytes meaning tera, or one trillion, bytes.
One method of providing short-term storage is computer memory. Another is by specialized boards, called frame buffers, that store one or more images and can be accessed rapidly, usually at video rates e. The latter method allows virtually instantaneous image zoom, as well as scroll vertical shifts and pan horizontal shifts. Frame buffers usually are housed in the specialized image processing hardware unit shown in Fig. On- line storage generally takes the form of magnetic disks or optical-media stor- age.
The key factor characterizing on-line storage is frequent access to the stored data. Finally, archival storage is characterized by massive storage requirements but infrequent need for access. Image displays in use today are mainly color preferably flat screen TV mon- itors. Monitors are driven by the outputs of image and graphics display cards that are an integral part of the computer system.
Seldom are there requirements for image display applications that cannot be met by display cards available com- mercially as part of the computer system. In some cases, it is necessary to have stereo displays, and these are implemented in the form of headgear containing two small displays embedded in goggles worn by the user.
Hardcopy devices for recording images include laser printers, film cam- eras, heat-sensitive devices, inkjet units, and digital units, such as optical and CD-ROM disks. Film provides the highest possible resolution, but paper is the obvious medium of choice for written material. For presentations, images are dis- played on film transparencies or in a digital medium if image projection equip- ment is used. The latter approach is gaining acceptance as the standard for image presentations.
Networking is almost a default function in any computer system in use today. Because of the large amount of data inherent in image processing applications, the key consideration in image transmission is bandwidth. In dedicated net- works, this typically is not a problem, but communications with remote sites via the Internet are not always as efficient.
Fortunately, this situation is improving quickly as a result of optical fiber and other broadband technologies. Summary The main purpose of the material presented in this chapter is to provide a sense of per- spective about the origins of digital image processing and, more important, about cur- rent and future areas of application of this technology.
Although the coverage of these topics in this chapter was necessarily incomplete due to space limitations, it should have left the reader with a clear impression of the breadth and practical scope of digital image processing.
Upon concluding the study of the final chapter, the reader of this book will have arrived at a level of understanding that is the foundation for most of the work currently underway in this field. References and Further Reading References at the end of later chapters address specific topics discussed in those chap- ters, and are keyed to the Bibliography at the end of the book. However, in this chapter we follow a different format in order to summarize in one place a body of journals that publish material on image processing and related topics.
We also provide a list of books from which the reader can readily develop a historical and current perspective of activ- ities in this field. Thus, the reference material cited in this chapter is intended as a general- purpose, easily accessible guide to the published literature on image processing.
Major refereed journals that publish articles on image processing and related topics include: The following books, listed in reverse chronological order with the number of books being biased toward more recent publications , contain material that complements our treatment of digital image processing. These books represent an easily accessible overview of the area for the past 30 years and were selected to provide a variety of treat- ments.
They range from textbooks, which cover foundation material; to handbooks, which give an overview of techniques; and finally to edited books, which contain material rep- resentative of current research in the field.
Duda, R. Pattern Classification, 2nd ed.
Ritter, G. Shapiro, L.
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Dougherty, E. Etienne, E. Goutsias, J, Vincent, L. Mallot, A. Marchand-Maillet, S. Binary Digital Image Processing: Edelman, S. Lillesand, T. Mather, P. Computer Processing of Remotely Sensed Images: Petrou, M. Image Processing: Russ, J. The Image Processing Handbook, 3rd ed. Smirnov, A. Sonka, M. Umbaugh, S. Computer Vision and Image Processing: Haskell, B. Digital Pictures: Jahne, B.
Digital Image Processing: Castleman, K. Digital Image Processing, 2nd ed. Geladi, P. Bracewell, R. Sid-Ahmed, M. Jain, R. Mitiche, A. Baxes, G. Gonzalez, R. Haralick, R. Computer and Robot Vision, vols. Pratt, W. Lim, J. Schalkoff, R. Giardina, C. Serra, J. Ballard, D. Fu, K. Nevatia, R. Pavlidis, T. Rosenfeld, R. Digital Picture Processing, 2nd ed. Hall, E. Syntactic Pattern Recognition: Andrews, H. Tou, J. Aristotle Preview The purpose of this chapter is to introduce several concepts related to digital im- ages and some of the notation used throughout the book.
Section 2. Additional topics discussed in that section include digital image representation, the effects of varying the number of samples and gray levels in an image, some important phenomena associated with sampling, and techniques for image zooming and shrinking.
Finally, Section 2. As noted in that section, linear operators play a central role in the development of image processing techniques. Hence, developing a basic under- standing of human visual perception as a first step in our journey through this book is appropriate. Given the complexity and breadth of this topic, we can only aspire to cover the most rudimentary aspects of human vision. In particu- lar, our interest lies in the mechanics and parameters related to how images are formed in the eye.
We are interested in learning the physical limitations of human vision in terms of factors that also are used in our work with digital im- ages. Thus, factors such as how human and electronic imaging compare in terms of resolution and ability to adapt to changes in illumination are not only inter- esting, they also are important from a practical point of view.
The eye is nearly a sphere, with an average diameter of approximately 20 mm. Three membranes enclose the eye: Continuous with the cornea, the sclera is an opaque mem- brane that encloses the remainder of the optic globe. The choroid lies directly below the sclera. This membrane contains a net- work of blood vessels that serve as the major source of nutrition to the eye. Even superficial injury to the choroid, often not deemed serious, can lead to se- vere eye damage as a result of inflammation that restricts blood flow.
The choroid coat is heavily pigmented and hence helps to reduce the amount of ex- traneous light entering the eye and the backscatter within the optical globe.
At its anterior extreme, the choroid is divided into the ciliary body and the iris diaphragm. The latter contracts or expands to control the amount of light that enters the eye. The central opening of the iris the pupil varies in diameter from approximately 2 to 8 mm. The front of the iris contains the visible pig- ment of the eye, whereas the back contains a black pigment. The lens is made up of concentric layers of fibrous cells and is suspended by fibers that attach to the ciliary body.
The lens is colored by a slightly yel- low pigmentation that increases with age. In extreme cases, excessive clouding of the lens, caused by the affliction commonly referred to as cataracts, can lead to poor color discrimination and loss of clear vision.
Both infrared and ultraviolet light are absorbed appreciably by pro- teins within the lens structure and, in excessive amounts, can damage the eye.
When the eye is properly focused, light from an object outside the eye is imaged on the retina. Pattern vision is afforded by the distribution of discrete light receptors over the surface of the retina. There are two classes of receptors: The cones in each eye number between 6 and 7 million.
They are located primarily in the central portion of the retina, called the fovea, and are highly sensitive to color. Humans can resolve fine de- tails with these cones largely because each one is connected to its own nerve end. Muscles controlling the eye rotate the eyeball until the image of an object of in- terest falls on the fovea.
Cone vision is called photopic or bright-light vision. The number of rods is much larger: Some 75 to million are distributed over the retinal surface. The larger area of distribution and the fact that sever- al rods are connected to a single nerve end reduce the amount of detail dis- cernible by these receptors.
Rods serve to give a general, overall picture of the field of view. They are not involved in color vision and are sensitive to low lev- els of illumination.
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For example, objects that appear brightly colored in day- light when seen by moonlight appear as colorless forms because only the rods are stimulated. This phenomenon is known as scotopic or dim-light vision.
Figure 2. The absence of receptors in this area results in the so-called blind spot see Fig. Except for this region, the distribution of receptors is radially sym- metric about the fovea. Receptor density is measured in degrees from the fovea that is, in degrees off axis, as measured by the angle formed by the visual axis and a line passing through the center of the lens and intersecting the retina.
The fovea itself is a circular indentation in the retina of about 1. However, in terms of future discussions, talking about square or rec- tangular arrays of sensing elements is more useful.
Thus, by taking some liberty in interpretation, we can view the fovea as a square sensor array of size 1.
The density of cones in that area of the retina is approxi- mately , elements per mm2. Based on these approximations, the number of cones in the region of highest acuity in the eye is about , elements. While the ability of humans to integrate intelli- gence and experience with vision makes this type of comparison dangerous.
Keep in mind for future discussions that the basic ability of the eye to resolve detail is certainly within the realm of current electronic imaging sensors. As illustrated in Fig. The shape of the lens is controlled by tension in the fibers of the ciliary body. To focus on distant objects, the controlling muscles cause the lens to be relatively flattened. Similarly, these muscles allow the lens to become thicker in order to focus on objects near the eye.
The distance between the center of the lens and the retina called the focal length varies from approximately 17 mm to about 14 mm, as the refractive power of the lens increases from its minimum to its maximum. Point C is the optical center of the lens. When the eye focuses on a nearby object, the lens is most strong- ly refractive.
This information makes it easy to calculate the size of the retinal image of any object. In Fig. If h is the height in mm of that object in the retinal image, the geometry of Fig. As indicated in Section 2. Perception then takes place by the relative excitation of light recep- tors, which transform radiant energy into electrical impulses that are ultimate- ly decoded by the brain.
The range of light intensity lev- els to which the human visual system can adapt is enormous—on the order of —from the scotopic threshold to the glare limit. Experimental evidence in- dicates that subjective brightness intensity as perceived by the human visual system is a logarithmic function of the light intensity incident on the eye.
Fig- ure 2. The long solid curve represents the range of intensities to which the visual system can adapt. In photopic vision alone, the range is about The transition from scotopic to photopic vision is gradual over the approximate range from 0. The essential point in interpreting the impressive dynamic range depicted in Fig. Rather, it accomplishes this large variation by changes in its overall sen- sitivity, a phenomenon known as brightness adaptation.
The total range of distinct intensity levels it can discriminate simultaneously is rather small when compared with the total adaptation range. For any given set of conditions, the current sensitivity level of the visual system is called the brightness adaptation level, which may correspond, for example, to brightness Ba in Fig. The short intersecting curve represents the range of subjective brightness that the eye can perceive when adapted to this level.
This range is rather restricted, having a level Bb at and below which all stimuli are perceived as indistinguishable blacks. The upper dashed portion of the curve is not actually restricted but, if ex- tended too far, loses its meaning because much higher intensities would simply raise the adaptation level higher than Ba.
The ability of the eye to discriminate between changes in light intensity at any specific adaptation level is also of considerable interest. A classic experiment used to determine the capability of the human visual system for brightness dis- crimination consists of having a subject look at a flat, uniformly illuminated area large enough to occupy the entire field of view.
This area typically is a dif- fuser, such as opaque glass, that is illuminated from behind by a light source whose intensity, I, can be varied. This curve shows that brightness discrimination is poor the Weber ratio is large at low levels of illumination, and it improves significantly the Weber ratio decreases as background illumination increases. The two branches in the curve reflect the fact that at low levels of illumination vision is carried out by activity of the rods, whereas at high levels showing better discrimination vi- sion is the function of cones.
If the background illumination is held constant and the intensity of the other source, instead of flashing, is now allowed to vary incrementally from never being perceived to always being perceived, the typical observer can dis- cern a total of one to two dozen different intensity changes.
Roughly, this re- sult is related to the number of different intensities a person can see at any one point in a monochrome image. This result does not mean that an image can be represented by such a small number of intensity values because, as the eye roams about the image, the average background changes, thus allowing a different set of incremental changes to be detected at each new adaptation level.
The net consequence is that the eye is capable of a much broader range of overall intensity discrimination. In fact, we show in Section 2. Two phenomena clearly demonstrate that perceived brightness is not a sim- ple function of intensity. The first is based on the fact that the visual system tends to undershoot or overshoot around the boundary of regions of different intensities.
Al- though the intensity of the stripes is constant, we actually perceive a brightness pattern that is strongly scalloped, especially near the boundaries [Fig. These seemingly scalloped bands are called Mach bands after Ernst Mach, who first described the phenomenon in All the center squares have exactly the same intensity. The relative vertical positions between the two profiles in b have no special significance; they were chosen for clarity.
Perceived brightness Actual illumination However, they appear to the eye to become darker as the background gets lighter. A more familiar example is a piece of paper that seems white when lying on a desk, but can appear totally black when used to shield the eyes while look- ing directly at a bright sky. All the inner squares have the same in- tensity, but they appear progressively darker as the background becomes lighter. Other examples of human perception phenomena are optical illusions, in which the eye fills in nonexisting information or wrongly perceives geometrical properties of objects.
Some examples are shown in Fig. The same effect, this time with a circle, can be seen in Fig.
The two horizontal line segments in Fig. Finally, all lines in Fig. Yet the crosshatching cre- ates the illusion that those lines are far from being parallel. Optical illusions are a characteristic of the human visual system that is not fully understood. We now consider this topic in more detail. The visible spectrum is shown zoomed to facilitate explanation, but note that the visible spectrum is a rather narrow portion of the EM spectrum. As shown in Fig. On one end of the spectrum are radio waves with wavelengths billions of times longer than those of visible light.
On the other end of the spectrum are gamma rays with wavelengths millions of times smaller than those of visible light. The electromagnetic spectrum can be expressed in terms of wavelength, fre- quency, or energy. Frequency is measured in Hertz Hz , with one Hertz being equal to one cycle of a sinusoidal wave per second.
A commonly used unit of en- ergy is the electron-volt. Electromagnetic waves can be visualized as propagating sinusoidal waves with wavelength l Fig. Each bundle of energy is called a photon. We see from Eq. Thus, radio waves have photons with low energies, microwaves have more energy than radio waves, infrared still more, then visible, ultraviolet, X-rays, and finally gamma rays, the most energetic of all.
This is the reason that gamma rays are so dangerous to living organisms. Light is a particular type of electromagnetic radiation that can be seen and sensed by the human eye.
The visible color spectrum is shown expanded in Fig. The visible band of the electromagnetic spectrum spans the range from approximately 0. For convenience, the color spectrum is divided into six broad regions: No color or other component of the electromagnetic spectrum ends abrupt- ly, but rather each range blends smoothly into the next, as shown in Fig.
The colors that humans perceive in an object are determined by the nature of the light reflected from the object. A body that reflects light and is relatively bal- anced in all visible wavelengths appears white to the observer.
However, a body that favors reflectance in a limited range of the visible spectrum exhibits some shades of color. For example, green objects reflect light with wavelengths primarily in the to nm range while absorbing most of the energy at other wavelengths.
Light that is void of color is called achromatic or monochromatic light. The only attribute of such light is its intensity, or amount.
The term gray level gen- erally is used to describe monochromatic intensity because it ranges from black, to grays, and finally to white. Chromatic light spans the electromagnetic ener- gy spectrum from approximately 0. Three basic quantities are used to describe the quality of a chromatic light source: Radiance is the total amount of energy that flows from the light source, and it is usually measured in watts W. Luminance, measured in lumens lm , gives a measure of the amount of energy an observ- er perceives from a light source.
For example, light emitted from a source op- erating in the far infrared region of the spectrum could have significant energy radiance , but an observer would hardly perceive it; its luminance would be almost zero.
Finally, as discussed in Section 2. Continuing with the discussion of Fig. As discussed in Section 1. Hard high-energy X-rays are used in industrial applications.
Chest X-rays are in the high end shorter wavelength of the soft X-rays region and dental X-rays are in the lower energy end of that band. The soft X-ray band transitions into the far ultraviolet light region, which in turn blends with the visible spectrum at longer wavelengths.
The opposite end of this band is called the far-infrared region. This latter region blends with the microwave band. This band is well known as the source of energy in microwave ovens, but it has many other uses, including communication and radar. Finally, the radio wave band encompasses television as well as AM and FM radio. In the higher energies, radio signals emanating from certain stellar bodies are useful in as- tronomical observations.
Examples of images in most of the bands just discussed are given in Section 1. In principle, if a sensor can be developed that is capable of detecting energy radiated by a band of the electromagnetic spectrum, we can image events of in- terest in that band.
For example, a water molecule has a diameter on the order of 10—10 m. Thus, to study molecules, we would need a source capable of emitting in the far ultraviolet or soft X-ray region. This limitation, along with the physical properties of the sensor material, establishes the fundamental lim- its on the capability of imaging sensors, such as visible, infrared, and other sen- sors in use today. Although imaging is based predominantly on energy radiated by electro- magnetic waves, this is not the only method for image generation.
For example, as discussed in Section 1. Other major sources of digital images are electron beams for electron microscopy and synthetic images used in graphics and visualization. We enclose illumina- tion and scene in quotes to emphasize the fact that they are considerably more general than the familiar situation in which a visible light source illuminates a common everyday 3-D three-dimensional scene.
But, as noted earlier, it could originate from less traditional sources, such as ultrasound or even a computer-generated illumination pattern. Similarly, the scene elements could be familiar objects, but they can just as eas- ily be molecules, buried rock formations, or a human brain.
We could even image a source, such as acquiring images of the sun. Depending on the nature of the source, illumination energy is reflected from, or transmitted through, objects.
An example in the first category is light reflected from a planar surface. In some applications, the re- flected or transmitted energy is focused onto a photoconverter e. Electron microscopy and some applications of gamma imaging use this approach.
The idea is simple: Sensing material Power in b Line sensor. The output voltage waveform is the response of the sensor s , and a dig- ital quantity is obtained from each sensor by digitizing its response. In this section, we look at the principal modalities for image sensing and generation. Image digitizing is discussed in Section 2. Perhaps the most fa- miliar sensor of this type is the photodiode, which is constructed of silicon ma- terials and whose output voltage waveform is proportional to light.
The use of a filter in front of a sensor improves selectivity. For example, a green pass fil- ter in front of a light sensor favors light in the green band of the color spec- trum. As a consequence, the sensor output will be stronger for green light than for other components in the visible spectrum.
In order to generate a 2-D image using a single sensor, there has to be rela- tive displacements in both the x- and y-directions between the sensor and the area to be imaged.
The single sensor is mounted on a lead screw that provides motion in the perpendicular direction. Since me- chanical motion can be controlled with high precision, this method is an inex- pensive but slow way to obtain high-resolution images. Other similar mechanical arrangements use a flat bed, with the sensor moving in two linear directions. These types of mechanical digitizers sometimes are referred to as microdensitometers.
The leading textbook in its field for more than twenty years, it continues its cutting-edge focus on contemporary developments in all mainstream areas of image processing—e. It focuses on material that is fundamental and has a broad scope of application. Sign In. Update Cancel. Enter a world of mysticism and magic.
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Film Sensor Rotation Linear motion One image line out per increment of rotation and full linear displacement of sensor from left to right. It is based on a signifi- cant expansion of the material previously included as a section in the chapter on image representation and description. The latter approach is gaining acceptance as the standard for image presentations.
The layers above also are bright, but their brightness does not vary as strongly across the layers. When an appreciable number of pixels exhibit this property, the image will have high contrast. We illustrate imaging in this band with examples from microscopy and astronomy. In particu- lar, our interest lies in the mechanics and parameters related to how images are formed in the eye.
Figure a courtesy of Mr.