【正文】 note the elements of a digital image. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagic (EM) spec trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra sound, electron microscopy, and putergenerated images. Thus, digital image processing enpasses a wide and varied field of applications. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and puter vi sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of puting the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as puter vision whose ultimate goal is to use puters to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in be tween image processing and puter vision. There are no clearcut boundaries in the continuum from image processing at one end to puter vision at the other. However, one useful paradigm is to consider three types of puterized processes in this continuum: low, mid, and highlevel processes. Lowlevel processes involve primitive opera tions such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A lowlevel process is characterized by the fact that both its inputs and outputs are images. Midlevel processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for puter processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (., edges, contours, and the identity of individual objects). Finally, higherlevel processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision. Based on the preceding ments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing enpasses processes whose inputs and outputs are images and, in addition, enpasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for puter processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even puter vision, depending on the level of plexity implied by the statement “making sense.” As will bee evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value. The areas of application of digital image processing are so varied that some form of anization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (., visual, Xray, and so on). The principal energy source for images in use today is the electromagic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by puter. In this section we discuss briefly how images are generated in these various categories and the areas in