【正文】 d on human subjective preferences regarding what constitutes a “good” enhancement result. Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Inter. It 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 pression and for pyramidal representation, in which images are subdivided successively into smaller regions. Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmi 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 Inter, which are characterized by significant pictorial content. Image pression is familiar (perhaps inadvertently) to most users of puters in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image pression standard. Morphological processing deals with tools for extracting image ponents 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. Segmentation procedures partition an image into its constituent parts or objects. 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 accurate the segmentation, the more likely recognition is to succeed. Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the bound ary of a region (., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for puter processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a plete region. Boundary representation is appropriate when the focus is on external 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 plement each other. Choosing a representation is only part of the solution for trans forming raw data into a form suitable for subsequent puter processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting 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 (., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital image processing with the development of methods for recognition of individual objects. 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 Fig2 above. Knowledge about a problem domain is coded into an image processing 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. The knowledge base also can be quite plex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing highresolution satellite images of a region in con nection with changedetection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig2 above by the use of doubleheaded arrows between the processing modules and the knowledge base, as op posed to singleheaded arrows linking the processing modules. Edge detection Edge detection is a terminology in image processing and puter vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sha