Calibration and Orientation of Cameras in Computer Vision

One of my greatest hates is the edited book with chapters from a number of different authors, some of which have evidently been written in haste against the final deadline, and most of which show marked divergence of content, opinion, notation and presentation. I am very relieved to be able to say that, though edited, this volume does not fall into this stereotype, in spite - or perhaps partly because - of the relatively few authors who have contributed to it. Indeed, it falls into the category of a profound study of one major, topical aspect of machine (or computer) vision, and provides suitable contrasts which display the complexities of this subject area, while at the same time bringing the reader to a clear understanding of the problems, the principles and the ways in which solutions can be found. With seven main chapters (the first `chapter' is only a five-page overview of the others, and there is no concluding chapter), it is surprising how much is packed into the volume and how skilfully the overlapping themes have been brought together - illustrating in fact that these themes are core to the subject and at the same time showing from different perspectives how they can be handled. Perhaps most important amongst these recurring themes are the roles of robust algorithms and robust statistics, the problems of ambiguity and (for example) `dangerous surfaces', the problems of focusing (which severely affects camera calibration), the importance of nonlinear distortions (which in high accuracy work absolutely have to be corrected for) and, naturally, the measurement of camera parameters (which are, as is now standard, divided into `internal' and `external' parameters). This skilful compilation is particularly welcome considering that in the Preface the Editor states `This is not a textbook. Therefore, it is neither consistent in diction nor content.'