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User's Guide

1.1 Introduction

The field of digital image processing continues, as it has since the early 1970s, on a path of dynamic growth in terms of popular and scientific interest and the number of commercial applications. Considerable advances have been made over the past 30 years resulting in routine application of image processing to problems in medicine, manufacturing, entertainment, law enforcement, and many others. Examples include mapping internal organs in medicine using various scanning technologies (image reconstruction from projections), automatic fingerprint recognition (pattern recognition and image coding), and HDTV (video coding), to name a few.
The discipline of digital image processing covers a vast area of scientific and engineering knowledge. It is built on a foundation of 1D and 2D signal processing theory and overlaps with such disciplines as artificial intelligence (scene understanding), information theory (image coding), statistical pattern recognition (image classification), communication theory (image coding and transmission), and microelectronics (image sensors, image processing hardware). Broadly, image processing may be subdivided into the following categories: enhancement, restoration, coding, and understanding. The goal in the first three categories is to improve the pictorial information either in quality (for purposes of human interpretation) or in transmission efficiency. In the last category, the objective is to obtain a symbolic description of the scene, leading to autonomous machine reasoning and perception.
This Guide uses the format of a tutorial text on digital image processing to introduce the power and functionality of the Digital Image Processing package. Chapter 2 provides an introduction to image representation issues such as data structures, color, and sample values. It presents the details of an ImageData object, a new data structure for easy, efficient manipulation of monochrome and color images that is unique to the package. Chapters 3 through 6 present spatial operators used to perform such common image tasks as interpolation, noise reduction, contrast enhancement, thresholding, and edge detection, just to name a few. Each chapter introduces a different category of functions. The simplest image operations are those that operate on individual image pixels. These are commonly called point-based operations, since they determine an output value based on a single sample in the input image. These are presented in Chapter 3, and include such useful operations as brightness scaling, quantization, and color conversion. Functions that change the spatial relationships between pixels are in Chapter 4. These include rotation, warping, and functions that change the dimensions of an image. Area-based operators return a value based on an evaluation of the sample values in a region consisting of more than a single pixel (Chapter 5). Area-based operators are further categorized into linear (implemented using convolution and correlation) and nonlinear (implemented using block processing). The important category of morphological image processing operations is presented in Chapter 6. Common image processing tasks require the measurement of local variation of sample values. This may be used to enhance a particular image feature, such as an intensity edge or to segment an image into disjoint regions. The topic of image segmentation is covered in Chapter 7. Finally, a frequency domain representation of signals is introduced in the last two chapters of the book, which leads to a discussion of image transforms (Chapter 8) and filter design (Chapter 9). Many chapters end with a section dedicated to a specific image processing problem and detail the solution.
This Guide includes a bibliography, with an extensive listing of image processing and signal processing textbooks and references.


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