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Deep Learning. Deng, J. ImageNet: A large-scale hierarchical image database. Krizhevsky, A. Advances in Neural Information Processing Systems 25 , — Yang, C. Single-image super-resolution: A benchmark. Dong, C. Image super-resolution using deep convolutional networks. Ledig, C. Dahl, R. Pixel Recursive Super Resolution.

Cui, Z. Deep network cascade for image super-resolution. IEEE Eur. Yang, J. Image super-resolution as sparse representation of raw image. Image Super-Resolution via Sparse Representation. Aly, H. Image up-sampling using total-variation regularization with a new observation model.


Zeyde, R. On single image scale-up using sparse-representations. Curves and Surfaces — Springer, Martin, D. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Abadi, M. TensorFlow: Large-scale machine learning on heterogeneous systems. Jinnai, H. Transmission Electron Microtomography in Polymer Research. Polymer 50 , — Transmission Electron Microtomography and Polymer Nanostructures. Macromolecules 43 , — Loos, J. Nano Lett. Macromolecules 42 , — Lu, K.

Macromolecules 40 , — Akutagawa, K. Rubber Chem. Yuasa, T. Nippon Gomu Kyokaish i 8 6, — , in Japanese. Translation is given in Int. Baeza, G. Macromolecules 46 , — Hagita, K. Large-scale reverse Monte Carlo analysis for the morphologies of silica nanoparticles in end-modified rubbers based on ultra-small-angle X-ray scattering data. Polymer C , — Vilgis, T. Reinforcement of Polymer Nano-Composites. Cambridge, Mark, J.

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Nature methods 9 , — A pyramid approach to subpixel registration based on intensity. Download references. The authors thank Dr. Tominaga, Dr. Yuasa, and Dr. We acknowledge Dr. One of the authors K. All authors discussed the obtained results. The manuscript was written with contributions from K. All authors have given approval to the final version of the manuscript. Correspondence to Katsumi Hagita. This information helps us design a better experience for all users. To learn more about cookies, please see our cookie policy. To learn more about how we use and protect your data, please see our privacy policy.

Subjects Computer Technology Nonfiction. Computer Technology Nonfiction. Learn how to read digital books for free. OverDrive uses cookies and similar technologies to improve your experience, monitor our performance, and understand overall usage trends for OverDrive services including OverDrive websites and apps. A few of the exercises ask you to find your own images and you might like to have the use of a digital camera in a few cases.

However, most of the exercises do not require you to take your own photos; when images are needed, they are either provided at the books website or you can find your own elsewhere on the web. This book and its associated software are intended to help you not only understand the key ideas but also to create neat images and effects.

Have fun! To the Teacher. Before giving descriptions of some of the possible courses that can be taught with the book, here are a few words about subject area integration. This book addresses the interdisciplinary field of computer imaging. In order to avoid a heavy bias, it covers material from more than one subject area. Topics come from photography, art, mathematics, physics of light, psychology of perception, and computer science. In addition, image processing traditionally involves signal processing, which is considered part of the field of electrical engineering.

Thus the integration of diverse subject material reflects the interdisciplinary nature of imaging as a field. Imaging is not the only modern field requiring interdisciplinary treatment. Fields like modern musical composition, computational biology, and intellectual property law are other examples.

Teaching an interdisciplinary course can be challenging, but its importance is increasing, and its a way to keep college courses relevant for students preparing to work in the age of digital everything. In spite of this, one can teach image processing within a disciplinary framework. By emphasizing certain sections of the book, the flavor of the course can be varied considerably and made to fit within a program in, say, computer science, digital arts, mathematics, or possibly even perceptual psychology.

For a course on computer science, the following topics should be emphasized: number systems and their use in representing pixel values, red-green-blue RGB color.

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The Transcentration game presented in chapter 20 illustrates in detail how Python and Swing come together to afford a complete toolkit for developing interactive software involving images. Although there have been a number of interesting image-processing tools on the market for a long time such as Adobe PhotoShop , these tools have several limitations: first, they have tended to be expensive.

Second, they have been designed primarily for artists and consequently their designers have gone to great pains to hide the mathematics and technologies that make them work. The PixelMath program used in this book takes a different approach. Its designed to reveal the digital image not only as a visual object but as a mathematical one, too. PixelMath helps reveal the power of mathematics by providing calculator and formula interfaces to the image. Users can draw on their own creativity to come up with entirely new image effects. Not only that, but more complicated sequences of operations can be automated by the user via programming with the built-in Python interpreter.

This book, the PixelMath software, and the many ideas, activities, and materials that go with them are an outgrowth of a project that began in entitled Mathematics Experiences Through Image Processing. This project supported the development and testing of the Pixel Calculator modeled after a pocket calculator and running under Windows 3.

The author thanks the many students and colleagues who contributed in one way or another to that project, especially Prof. I apologize to anyone inadvertently left out. Intellectual property rights to the PixelMath software were later transferred to the University of Washington. Since that time, PixelMath has been used in educational research and teaching at the university. As the one person who has been with the project the entire time, the maintenance and enhancement of PixelMath has been a job for the author over the past 12 years.

After having PixelMath versions with home-grown Common Lisp and then Scheme interpreters included, it was a revelation to find Jython, and thanks go to its development community for making it such a useful component. Id like to acknowledge. I also thank my colleagues in the Department of Computer Science and Engineering who have supported this work even though it has gone against the mainstream of teaching Java and pushed beyond the boundaries of computer science.

The strong interdisciplinary intellectual culture at the University of Washington has been a big help in making this kind of project practically feasible. I would like to acknowledge the Helen R. Thanks to Kevin Vixie, Tom Asaki, and the Park City, Utah Mathematics Institute for their support during the summer of when the institutes theme was image processing. Thanks also go to Tyler Robison for comments on the manuscript.

I would like to thank the MIT Press staff for their care in the editing and production of this book, as well as the reviewers of the book proposal for their suggestions and comments. Id especially like to acknowledge my parents, Taffee and Mary-Mae Tanimoto, and my mentor, Theo Pavlidis, for inspiring me to not only undertake this project but finish it. Finally, a thank-you to my family and friends for supporting me in this seemingly endless project. We are bombarded with images in our daily lives. They continue to come from television, magazines, newspapers, books, movies, and personal film and video cameras.

However they now also come from digital scanners, digital cameras, Internet web sites, digital video cameras, and mobile phones e. They can also be synthesized in computers using drawing and painting programs, and they can be synthesized directly from mathematical expressions. There are probably more pixels in the world now on web sites, in peoples personal computers, in their digital cameras, etc. For example, the U.

Library of Congress contains approximately 20 million volumes. Thats 5 Suppose that the average digital photo contains , pixels. Certainly, some are much smaller and some much larger. Assume that the average home computer hard drive contains images. This is pretty conservative; there are many images cached by web browsers. And then there is video. Each video clip or movie is a sequence of images. A hard drive with ten movies on it has on the order of 1 trillion pixels on it.

Image and movie files are usually compressed, so that you can usually fit more than the ten movies that you could squeeze onto a 1-terabyte hard drive if each pixel took 1 byte to store. I wont try to estimate how many movies are found on the average home computer. We dont need that much detail. The conclusion is this: Pixels are numerous! Pixels are the most ubiquitous data items in existence other than bits or bytes themselves. This book is about image processing. Before we talk about processing, we should at least consider the question of what is meant by the term image.

The further we study image processing the less simple the answer may seem. Lets consider a representative collection of possible answers to the question of what an image is. In everyday life, an image is a flat, physical representation of a scene, a picture, or a design. It can be a photographic print, a pattern of light emitted from a television screen or computer monitor, an oil painting, or a childs crayon drawing. In computing, an image may be a data structure containing a two-dimensional array of pixel values together with a set of attribute-value pairs representing the header of an image file.

In optics an image may be a convergence of rays on a plane or other surface in space, whether or not there is a physical surface present to reflect the rays. In psychology, an image may be a mental construct e. Dreaming is a visual experience, even though ones eyes are usually closed at the time.

A person can also experience an afterimage a perceived pattern of light that doesnt exist outside the eye, the result of prior stimulation with a related pattern. Visual illusions of many sorts are examples of mental images that exhibit striking differences from physical reality. There are some other notable examples of perceived images that dont correspond directly to a physical two-dimensional stimulus. One is the depth image perceived by someone viewing a stereogram. The stereograms two views of a scene are two-dimensional stimuli, but the depth map is not represented directly in either of them; its represented by subtle differences in the positioning of corresponding points in these views.

A stranger example of a perceived image is the image one experiences when observing a piece of saccade art. Saccade art is rendered by one or more linear arrays of small, intense lights, such as light-emitting diodes LEDs ; the lights flash on and off under computer control in such a way that an observer, moving his or her eyes past the arrays, will have a pattern of light painted on the retina that shows a distinct and recognizable structure, such as the shape of an animal or some legible text. One of the best examples of a saccade 1. Assuming data are stored electronically, magnetically, or optically; this doesnt include the genetic material in DNA, which is even more plentiful.

Three-dimensional movies are gaining in popularity, roughly doubling the number of pixels per movie. The storage space required for just one minute of the movie Avatar is estimated at Each array is vertically oriented and there are several feet between each of these strips. By illuminating first the leftmost array, then the next one to the right, and so on, the eye is directed to try to follow this apparent left-toright motion. Once the eye is moving with roughly this speed and direction, much faster variations in the intensities of the lights cause the formation of retinal images that show butterflies, fish, text, etc.

The technology of virtual retinal displays also known as retinal-scan display offers an even more direct channel of visual communication from a computer to a human. It bypasses the use of any array of lights or pixels and uses a laser or a single high-brightness light-emitting diode to draw a raster-scan pattern of light onto the viewers retina.

In all of these examples, there is a fundamental aspect to the image as a two-dimensional pattern of information that can be experienced in some way by a human observer. As we explore various image-processing techniques, we may lose sight of this general aspect of images. However, it is always there in the background. A digital image is a particular kind of image. From a technical perspective, its an image represented by numbers. However, theres a lot of leeway in that definition, and some people might not agree that numbers are a necessary part of the definition.

Some might say that a digital image is a spatial arrangement of pixels. Then theres the question of what a pixel is and whether pixels themselves should be represented with numbers. We should note that file formats such as the JPEG image format use mathematical representations that are not particularly pixel oriented. Its theoretically possible to have digital images without pixels by using numbers to represent functions that in turn combine to represent the image. Nonetheless, pixels are a central concept in digital imaging.

When buying a digital camera, one of the first questions one asks is How many pixels does it have? This refers to the maximum number of individual colored dots a single photo can have when taken with the camera. The cameras sensor has to have a separate sensing cell actually three cellsone each for red, green, and blue for each pixel. The usual answer to the question of what a pixel is goes something like this: A pixel is an atomic visible element of an image, a picture element or picture cell.

A pixel represents a small area of an image. In most cameras, the area is square in shape. Before we give a definition that well use in the rest of the book, lets consider some special cases. Consider a ceramic tile mosaic in which the tiles are arranged in a square grid.

Each tile has a uniform color, although the colors of any two tiles might differ. The mosaic seems to be an image. Is each tile a pixel? A natural answer is yes. However, that is not the whole. In a real mosaic, the tiles are usually separated by thin zones of grout. The grout usually is neutral in color. However, the grout cannot help but have at least some effect on the observed image. Do zones of grout then count as pixels, too? Some people would say that this mosaic is a digital image, even though we have not used numbers to represent it.

The fact that it is made up of elements that we can call pixels here actually just ceramic tiles would be enough for them to call this a digital image. One could argue here that the image area has been divided up into a finite set of small components, and that structure is a key part of a digital image. Or is it? Take a magnifying glass and look closely at the small dots on a cathode ray tube CRT as it displays an image.

Are there distinct zones that can easily be called pixels? The answer is usually no. The pixels of an image rendered by a CRT actually seem to be represented by fuzzy collections of small dots, and there are separate dots for each of the colors red, green, and blue. Where are the pixels? A pixel is a more abstract entity than a colored region within an image. A pixel is a sample of the visual properties of an image that may be representative of a point or a region within the image.

Another example of a mosaiclike image is one of the graphic works of the artist Chuck Close. In figure 1. Each macro-pixel carries color information for its region of the main image. However, it is also a little image in its own right. There is typically a distinct shape and perhaps other details in the macro-pixel.

A generalization of this mosaic style is the photomosaic in which each macro-pixel is actually a little digital image whose details may be visible when inspected closely. An example of a photomosaic is shown in figure 1. While the generation of photomosaics usually involves computers, they can be made manually as collages with many small photos pasted onto a large board. There are many artistic decisions that can be made and some of these can be programmed into a computer, especially the criteria for choosing an image for each macropixel. This decision may include not only color but also dominant edge characteristics, color distribution within the image, and even the subject matter depicted.

A related kind of image is a mosaic made of tiles that have color variations within them, and that may have irregular shapes, such as fragments of glass, stone, or torn-up photographs. Well explore the fine art of photomosaic construction later in the book. Finally, lets consider the saccade art at the Exploratorium once again.

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Where are the pixels of those images? They are in the controlling computer, they are on the observers retina, and they are momentarily on the linear light strips, but they are never displayed on a separate two-dimensional medium. The areas represented by each pixel will be different for every observer because no two observers will scan their eyes across the light strips at exactly the same speed.

So here, once again, we have a reason to treat the notion of a pixel as more abstract than the representation of a particular area. Figure 1. Created in collaboration with Japanese woodblock artist Yasu Shibata, it is the woodblock with the worlds most complex woodblock assemblage and printing process. Courtesy of Pace Editions, Inc. This book deals with images. It also deals with processing. Fundamental to the concept of processing is the concept of function. One meaning of function is purpose, but the other, more important notion is the one used in mathematics: determining relation.

Imagine a function called double that can be used in the following way. Double 5. What do you get? The answer is This function is a relation between numbers. Its a determining relation because given a number, say 5, the double is a unique number, in this case Functions are essential to processing because they can be used to describe the relationship between the input and output data at each stage of processing. A standard mathematical definition of a function is that it is a kind of mapping from one set of values to another possibly the same set.

The mapping must be single-valuedthere must be only a single member of the second set corresponding to a given member of the first set. The first set is called the domain of the function and the second set is called the codomain sometimes the second set is called the range of the function.

Some functions relate pairs of numbers, like the doubling function just mentioned. But functions dont have to work with numbers at all. They can relate pairs of images, or they can relate complex data structures to others. In traditional mathematics classes, one is typically presented with various functions and asked to solve for this or that, or to graph the functions. Can a function itself be the solution to a scientific problem? Can a function be a work of art? Yes and yes. As well see, an entire computer program can be considered to be a function. Also, an image even the Mona Lisa can be considered to be a function.

Functions can also be used to represent artistic processes; for example, we can design a function that takes as input a photograph and that produces as output an image in the style of an Impressionist painter. In this book we will consider not only the design of functions for processing images but a little bit of the theory of functions themselves, particularly with regard to closed systems of functions called algebraic groups.

A computer program is usually defined as a sequence of instructions that tell a computer how to solve some problem or perform some activity. In this book well look closely at a particular set of conventions for programming: the Python programming language. However, there is also a more general sense of a program: a representation of information that causes a computer to behave in some particular way. Such a program might not be a sequence of instructions at all. It could be a set of rules, a set of answers to possible questions, a diagram, or even an image.

So now we have seen that images can be macro pixels, pixels can be images, images are functions, pixels and functions can be represented using numbers, programs are described by functions, and images can be programs. Programs can produce images, and with the right tools, can be visualized as images. So pixels, numbers, functions, and programs can all be considered to be different views of the same information, at least at a philosophical level. Yet, even at a practical level, these concepts are deeply intertwined. In this section we consider how the human visual system works.

There are several reasons that some understanding of human vision is important in a study of image processing. One is that an image is usually an artifact intended to be seen by humans and thus in order to know what is important in an image we need to understand how humans view it. Often we will judge images in terms of how they look to humans, and sometimes it is important to know about the human visual system in order to understand how certain judgments work resolution, flicker, color, etc.

Another reason is that one kind of image is the representation in our brain of the scene in front of us, and we should understand how that can be, so that well know this meaning of the term image. Finally, computer vision systems are often modeled after the human system, and when studying computer vision, it helps to know about the human system in order to understand analogies, strategies, terminology, and so on. The rest of this section is organized as follows. First the human eye is discussed and its similarities and differences with modern cameras are mentioned. Then the part of the brain responsible for vision is discussed.

Next, we consider illusionsmanifestations of the limitations of human vision. Understanding illusions helps us to understand how human vision works. Finally, we summarize key issues for image processing as they relate to human vision. Human eyes, as the sensing organs for the human visual system, operate somewhat like a digital camera, but with many notable differences. Lets consider first the anatomy of the. Each eye is a roughly spherical organ located in an eye socket of the skull and controlled by muscles that effect eye movements.

Following the path that a light ray takes as it enters the eye, it first passes through the cornea and past an opening known as the pupil that is formed by a ring-shaped tissue called the iris, then through the crystalline lens, and then through the transparent vitreous humor that fills the eye cavity and then to the sensing layer of the retina. In the retina there are many small, light-sensitive cells called rods and cones.

The shape of the lens is modified by muscles that control it to focus an image on the retina. The retina contains not only the rods and cones but also other neural cells that collect and process the signals that come from the rods and cones. An interesting difference between a digital camera and the eye has to do with spatial resolutionthe size of the smallest details that can be discerned by the imaging array. In the case of a 4-megapixel digital camera, for example, the image area is divided into approximately a by array of equal-sized pixels.

But in the human eye, the sizes of the the regions covered by rods and cones vary, depending on the area of the retina being considered. There is a narrow, roughly circular patch of the retina right in the middle of the. Elsewhere in the retina, much less detail is captured. In the outer periphery of the visual field, the resolution is low. However, the part of the retina handling the periphery is very sensitive to motion. To see how different the fovea and periphery are, in terms of densities of rods and cones, look at figure 1.

The retina not only senses incoming light by the reactions of its rods and cones to the light, it also processes the information in certain ways. It can accentuate changes in the amount of light from one location to a neighboring one, and it can also accentuate changes of light intensity over time. We will discuss some of these kinds of processing in more detail in subsequent chapters. Information about the image leaves the retina along a bundle of nerve fibers known as the optic nerve. The optic nerve leaves the retina at a location known as the blind spot and travels back in the head to the optic chiasm and the lateral geniculate body.

These are shown in figure 1. The blind spot is so named because at this particular location in the retina there are no rods or cones, and so the image on the retina cannot be sensed at this location. Most people are not aware that they have a blind spot in their vision because the brain fills in the missing information by integrating over multiple images. The eyes move rapidly during normal vision, so any particular part of a scene is captured many times and any missing information in one image is quickly filled in from the next.

By carefully performing a simple experiment, it is possible for a person to experience his or her blind spot and even determine its size and shape. The experiment involves fixing one eye on a small black dot on a white sheet of paper and then moving the tip of a pencil toward the blind spot until the tip disappears.

Then by carefully moving the pencil, one can trace the outline of the shape of the blind spot on the paper. Information leaving the retina travels along the optic nerve to the optic chiasm, where the nerves from the left and right eyes meet, and each divides into two parts, with half proceeding to the left part of the brain and half to the right. The result is that information from both eyes proceeds to each side of the part of the brain known as the striate cortex.

The striate cortex, also known as area 17, is multilayered.

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Much of the low-level feature processing such as detection of light-dark differences at various angles occurs here. During the late s, D. Hubel and T. Wiesel conducted experiments in which anesthetized cats were presented with various patterns: dark bars on light backgrounds in a certain position and rotated varying degrees. By implanting electrodes into specific cells of a cats striate cortex, they found that there are cells that respond specifically to these stimuli. Changing the angle of the bar made one neuron stop firing and another one start, for example.

The striate cortex connects to other parts of the brain, and much research is currently focused on better understanding what happens to visual information after it is processed by the striate cortex. This introduction to image processing features a section on visual illusions for several reasons. Illusions raise the question of how human vision works; how is it possible that we dont see the world the way it really is? Illusions can help us understand the limits of human vision.

Finally, they help us understand the mechanisms of human vision and thus help us think about the design of artificial methods for image processing and vision. Our first illusion is the simultaneous-contrast illusion figure 1. Here we are presented with two gray disks. They are both the same color and are equally luminous. However, one is surrounded by a light-gray rectangle, and the other is surrounded by a dark-gray one. The one surrounded by the light rectangle seems to be darker than the other disk. From this illusion we can infer that the eye sees intensities in the context of nearby intensities.

Although this illusion uses proximity in space, we can also do this using proximity in time. Several additional illusions are shown in figure 1. While the first four of these involve perceived lengths, curvature, and noncolinearity, the sun illusion and subjective-contour illusion e and f prompt the viewer to perceive a contour that doesnt exist and to perceive the region inside the contour as brighter than the background.

A general type of illusion is the perception of some object or form within visual noise or a random arrangement of elements, such as in tea leaves, clouds, or a Rorschach ink blot. Such an illusion is called apophenia. It was illustrated on a postage stamp see figure 1.

The formation is believed to have lasted thousands of years, but collapsed in Apophenia is an entirely expected product of the human visual system, which has evolved to perceive many possible threats and many possible hunting targets, even when they are largely hidden or camouflaged. In some cases, apophenia is simply a playful interpretation of an accidental or natural pattern. In other cases, people experiencing apophenia may attribute great significance to their perception, seeing it as a sign from a god or a threat from space aliens.

This kind of apophenia is pareidoliaapophenia in which the perceiver attributes significance to the perception. Understanding pareidolia helps provide a psychological explanation for many reported sitings of unidentified flying objects, Jesus, Bigfoot, the Loch Ness monster, and so on.

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Apophenia can occur in computer vision systems when the system matches a model of, say an automobile connecting rod, to a pattern of discoloration on an assembly-line conveyor belt. In extreme cases, machine vision systems can be considered to be hallucinating when their decision biases are adjusted so that the strength of evidence required to reach a recognition decision is zero. False positives are a problem for pattern recognition systems, whether human or artificial.

As we study image processing there are certain key concepts that well cover and that relate to human vision. These include resolution, light sensitivity, color, image stability, and other ideas. Resolution is the capacity to represent spatial detail in an image. In the human eye, resolution is largely determined by the density of rods and cones in the fovea of the eye, although the brain can integrate multiple views to obtain somewhat higher-resolution information.

The periphery of the retina senses information at a much lower resolution than does the. In digital imaging, resolution is most strongly associated with the number of pixels in an image. For example, a megapixel camera generally supports higher-resolution photography than does a 3-megapixel camera.

The actual amount of detail in an image depends, not only on the number of pixels in the image, but also on the characteristics of the optical system that captured the image, as well as other forces that affect the image, such as light levels, fog, visual noise, motion, focus, and digital preprocessing. Light sensitivity and color sensitivity relate to how much light and what wavelengths of light are necessary to stimulate a sensor to produce output. Human rods are more sensitive to light than cones, but they do not provide color distinctions. Closely associated with sensitivity is the concept of dynamic range, the extent to which we can make distinctions among slightly different low-intensity stimuli and slightly different high-intensity stimuli at the same time, i.

The human retina has a remarkable dynamic range, but the eye must still adapt over a period of time when moving from bright environments to dark environments or vice versa. Color is about the way humans perceive the distribution of light wavelengths in samples of light, especially those containing unequal amounts of the three additive primary colors red, green, and blue. We will have more to say about this in another chapter.

Humans are sensitive and almost hypersensitive to contours. The subjective-contour illusion in figure 1. One kind of image stability is about the ability of the brain to integrate multiple exposures between eye movements to have a relatively constant mental image of a scene. We can compare this to the antijitter features of modern video cameras.

Humans see in a particular band of the electromagnetic radiation spectrum. Does it have to be? No, some other animals see in other parts of the spectrum. For example, the honeybee has receptors for ultraviolet light as well as yellow-green and blue but not red. People who are not color-blind perceive colors in a three-dimensional space spanned by the additive primaries red, green, and blue. The physics of color would permit much higherdimensional color perception. Why are we limited to only three of them? A person has two eyes.

Why not one, three, five, or more? There is a kind of arbitrariness of nature about the human visual system. Artificial systems can be created that have multiple cameras, cameras that respond to infrared or ultraviolet light, and that have lenses very different from the human lens. Some of these possibilities make sense in particular engineering applications. Chapter 19 on computational photography helps to broaden our perspective of how future cameras could work. Digital cameras, like film cameras, use optical lenses to capture light and focus it into an image on an image plane.

Instead of photographic film, a digital camera has a solid-state. Two types of solid-state sensors are commonly used today. Less expensive cameras have commonly used complementary metal-oxide semiconductor CMOS transducers. The ways the sensors using the two technologies work are similar enough that the following discussion for the CCD case is representative of both.

Cameras with CCD arrays typically produce high-quality, low-noise images. In a CCD array, light falling onto each small zone of the image known as a photosite causes an accumulation of electric charge. The amount of charge depends upon the brightness of the light and the length of time during which the accumulation takes place. There are typically three photosites per pixel of the image: one for red, one for green, and one for blue see figure 1. The red photosites are covered with a filter layer that lets in red light and blocks blue and green. The green and blue photosites work in a similar manner.

Right after the image is captured in the photosites, the charge accumulated at each photosite is electrically transferred to a small storage area next to the photosite. The storage area is protected from the light, so that any change in the charge there is minimized. These numbers are then transferred from the sensor chip to a bulk storage unit such as a CompactFlash memory card. Before the image data are written on the flash memory card, they may be digitally enhanced within the camera and then reduced in volume using a method such as JPEG compression.

As mentioned earlier, pixels have been flooding the Internet at increasing rates. What can we do with all these pixels? Since we have a lot of them, we ought to know how to get value out of them. One way to obtain value is to extract information. Another is to make. If we cannot obtain value or add value, we could of course delete images and recover disk space, memory space, etc. Or we could save the images but compress them to recover at least some of the space they take on disks.

How do we do these things that add value or save space? Thats part of what this book is about.

Interdisciplinary Introduction Image Processing Pixels by Steven Tanimoto

More than that, this is a book about empowering you to work with digital images using the rich languages of mathematics and computer programming, and about helping you understand at least some aspects of how the Internet and the objects in it really work. Pixels are in some sense atomic units of visual information. They are analogous to digital audio waveform samples and to three-dimensional volume elements voxels.

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Understanding how pixels are processed in computers provides a key to understanding the digital world more generally, and its a big step toward fluency in information technology. Lets now begin to explore a world of visual information processing in a way that allows us to develop our artistic, technical, and mathematical faculties all at the same time.

Eye and Brain: The Psychology of Seeing. Luckiesh, M. New York: Van Nostrand. Available online at www. Jeffries, S. The rise of the camera phone. The Guardian, Jan. Metropolitan Museum. Chuck Close Prints: Process and Collaboration. Special Exhibitions. Rath, J. The data-crunching powerhouse behind Avatar. Visual Perception: An Introduction. London, UK: Psychology Press. Digital Photography. Choose the web site of some institution or corporation. Find a page there containing at least one image. Inspect the file and determine how many bytes are in the image file.