This slim volume is the first of a three-volume set. Together, these three volumes will provide an updated version of the authors’ more comprehensive textbook [1]. (To date, two of the three are available [2].) The idea was to break this larger book into three “backpack-compatible” books so that each provides about a semester’s worth of material. I thoroughly endorse this approach.

This first volume starts with a chapter on digital image basics, their acquisition, aspects of size and resolution, and file formats. A second chapter introduces ImageJ, the Java package around which the book is structured. The next six chapters investigate histograms, point operations, filters, edges and contours, morphology, and color. There are two appendices: a brief one on mathematical notation and a longer one about Java fundamentals.

The trouble with a slim book is that, by necessity, a vast amount of material must be omitted. Morphology should have been saved for a later book, and the Fourier transform and spectral analysis should have been introduced right at the beginning--this is too important a topic to be left out of an introduction. This would have given greater weight to the discussion of digital convolution--all too brief in this volume, and somewhat pointless without the Fourier transform and the convolution theorem. I would also have liked to see some general material about noise and image restoration. Although the median filter is introduced and its use for noise removal discussed, it would have benefited from a more thorough discussion of noise. As it is, some topics seem to be only cursorily investigated.

That being said, the book’s overall coverage is sound--well written, with plenty of illustrative examples and elegant diagrams. One problem with any image processing text is choosing images that best illustrate the algorithms. In this book, the authors managed this difficult task. In fact, in spite of my criticism, this book is a fine introduction to image processing, and some topics--color, in particular--are very well done indeed.

Several topics that are often ignored in other texts are treated solidly and well here. It is refreshing to see such a robust and detailed discussion of nonlinear filters, even if not much space is given to matters of their efficient implementation. The chapter on edges is also appropriately detailed, although it is a bit unfair to the reader to just mention the Canny edge detector and not discuss it in any detail.

One area needing improvement is the exercises--there are simply too few of them. For example, for the entire chapter on color, which covers color spaces, conversion, and statistics, there are only six exercises. For a book that purports to be an introductory text, this is insufficient.

In summary, this is mostly a fine text. I might quibble with the choice of topics and the amount of space given to them, but with the addition of more exercises, this would be an excellent introduction to the field.