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Reviewer: Max Garzon

This is a revision and expansion of the 1977 edition of the textbook by the same authors [1]. A comparison of the tables of contents reveals essentially the same (standard) range of topics (image modeling and quantization; image transforms; image enhancement, restoration, and segmentation; and image description). The objective of the book remains the same. It provides senior college students in electrical engineering and computer science with an introduction to basic concepts and techniques in digital image processing fundamentals as a foundation for further study and research in the field. The reference list has been updated, and important new material on feature detection and image segmentation has been added, along with further illustrations to give the reader a feeling for the results of the new algorithms. The prerequisite is still preparation in mathematical analysis, matrix theory, and probability. According to the authors, computer programming experience is necessary, although the material is accessible without it if no implementation is required. The new additions necessitated splitting chapter 7 into separate discussions of image segmentation (now chapter 7) and image representation and description (chapter 8). Chapter 7 now includes methods to detect discontinuities (points and edges), link regions (via the Hough transform, newly introduced in section 3.7, and digraphs), and segment regions (by splitting and merging or by using motion in a fixed background). Chapter 8 has been reorganized to reflect a shift of emphasis in emerging applications in the last ten years. Methods for representation and data compression (chain codes, polygonal approximation, skeletonization and in shape and/or topological parameters) have been introduced in chapter 8. This shift has also motivated new emphasis on basic topological concepts in chapter 2, which now covers connectivity, distance functions, and basic formulas for projection and stereo imaging, spatial- vs. frequency-domain classification of image enhancement in chapter 4, which contains three brief new sections, and in chapter 5, which now considers in their own right geometric transformations that operate directly on pixels. The rest of the text has remained unchanged. The most important addition of all, however, is the 83 problems (with an instructor's manual containing solutions and course recommendations) distributed at the end of each chapter. Some problems allow students to review basic formulas that the chapters introduced. Others require more elaborate use of concepts to solve specific technical problems in specific situations. None require substantial programming assignments or a literature search. In summary, I think the changes will be welcome and the book will continue to be a valuable introduction to the intended subject for the intended audience [2,3]. I only wish that project sections were interspersed among the chapters to provide a few large projects so that students could “implement and test with real data the concepts and algorithms developed in” the book and that the concluding remarks and references included references for further reading on the use and applications of the chapter material. This is an important aspect of an undergraduate textbook for a field with an extensive literature and a great variety of applications in real life projects.