PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
October 2017
661
BOOK
REVIEW
Photogrammetric Computer Vision:
Statistics, Geometry, Orientation and
Reconstruction
Wolfgang Förstner and Bernhard P. Wrobel.
Springer International Publishing, Switzerland, 2016. Hard cover
ISBN 978-3-319-11549-8 $69.97 Amazon, e-book 978-3-319-
11550-4, $55.99 Amazon. xvii and 816 pp, diagrams, images,
tables, algorithms, index.
Reviewed by:
Charles Toth, Research Professor, The
Ohio State University, Columbus, Ohio and Stewart
Walker, San Diego, California.
Photogrammetric Computer Vision
represents a milestone
publication in modern photogrammetry. Long years of intense
labor by two legendary authors, eminent professors of geomat-
ics from Bonn and Darmstadt, and a talented team of assis-
tants, have produced a true
magnum opus,
a rare masterpiece,
providing the most comprehensive description of the under-
lying theory at the intersection of photogrammetry and com-
puter vision. The significance of the book is in a way mirrored
by its huge weight and size (286 x 216 mm; 11.2 x 8.5 inches),
with over 800 pages in small fonts.
The book opens with a short introduction, which immediate-
ly defines both photogrammetry and computer vision and thus
explains their joint treatment in the book. While the authors
are firm that they will not cover hardware, they say enough in
a few pages to indicate the scope of photogrammetric applica-
tions and allow readers to link this book to more traditional
texts. Importantly, the focus on probabilistic and statistical
reasoning is succinctly justified. They include useful sugges-
tions on how the book can be used for courses at different lev-
els. These confirm that the contents far exceed anything that
can be reasonably included in a single course.
Part I, “Statistics” and “Estimation” lays the critical ground
work in three intense chapters. Chapter 2, “Probability Theory
and Random Variables” is followed by the short Chapter 3 on
“Testing”. These set the tone for the book – an advanced level
of discourse, reinforced by mathematical rigor and innumerable
equations. The reader must therefore concentrate and expect
slow progress, but will be amply repaid, especially by the key
Chapter 4, “Estimation”, well over a hundred pages covering
models and estimation, i.e. it includes what most of us were
brought up to call “least-squares adjustment”. One of your re-
viewers experimented by revising the topic of variance compo-
nents: the concise exposition on pages 91-93 is exemplary.
Part II, “Geometry”, is a little longer and covers the ele-
ments that will be used in the photogrammetric processes
themselves in Part III. As we were led to expect in Part I,
the treatment is detailed and demanding. Chapters 5-10 cover
“Homogenous Representations of Points, Lines and Planes”,
“Transformations”, “Geometric Operations”, “Rotations”, “Ori-
ented Projective Geometry” and “Reasoning with Uncertain
Geometric Entities”.
With 440 pages under our belts, we are ready for the en-
trée, Part III, “Orientation and Reconstruction”. This brings
the mathematical groundwork to bear on topics we all know:
Chapter 11, “Overview”, is a useful outline of the material to
come in the next five chapters, “Geometry and Orientation of
the Single Image”, “Geometry and Orientation of the Image
Pair”, “Geometry and Orientation of the Image Triplet”, “Bun-
dle Adjustment” and “Surface Reconstruction”. In Chapter 11,
too, the authors present a taxonomy of cameras and explain
their emphasis on “central” cameras, i.e. with a single view-
point. Perhaps some readers will be disappointed that the
“non-central” camera, i.e. no single viewpoint, enjoys less cov-
erage, since it includes the pushbroom design used to acquire
most satellite imagery; a special case. We are in familiar ter-
ritory in Part III, the substance of photogrammetry, but the
rigorous mathematical approach and the widespread use of
techniques and nomenclature from computer vision provide a
freshness. In Chapter 12, for example, well known concepts
related to the single photo are presented and the collinearity
equations are given in their standard form, but the reader is
reminded that the image coordinates therein are inhomoge-
nous, i.e. concepts from photogrammetry and computer vision
Photogrammetric Engineering & Remote Sensing
Vol. 83, No. 10, October 2017, pp. 661–652.
0099-1112/17/661–652
© 2017 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.83.10.661
