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1.3 Book overview 17

re-christened as computational photography (see Chapter 10) to acknowledge the increased
use of such techniques in everyday digital photography. For example, the rapid adoption of
exposure bracketing to create high dynamic range images necessitated the development of
tone mapping algorithms (Figure 1.10c) (see Section 10.2.1) to convert such images back
to displayable results (Fattal, Lischinski, and Werman 2002; Durand and Dorsey 2002; Rein-
hard, Stark, Shirley et al. 2002; Lischinski, Farbman, Uyttendaele et al. 2006a). In addition to
merging multiple exposures, techniques were developed to merge flash images with non-flash
counterparts (Eisemann and Durand 2004; Petschnigg, Agrawala, Hoppe et al. 2004) and to
interactively or automatically select different regions from overlapping images (Agarwala,
Dontcheva, Agrawala et al. 2004).
Texture synthesis (Figure 1.10d) (see Section 10.5), quilting (Efros and Leung 1999; Efros
and Freeman 2001; Kwatra, Sch¨ odl, Essa et al. 2003) and inpainting (Bertalmio, Sapiro,
Caselles et al. 2000; Bertalmio, Vese, Sapiro et al. 2003; Criminisi, P´ erez, and Toyama 2004)
are additional topics that can be classified as computational photography techniques, since
they re-combine input image samples to produce new photographs.
A second notable trend during this past decade has been the emergence of feature-based
techniques (combined with learning) for object recognition (see Section 14.3 and Ponce,
Hebert, Schmid et al. 2006). Some of the notable papers in this area include the constellation
model of Fergus, Perona, and Zisserman (2007) (Figure 1.10e) and the pictorial structures
of Felzenszwalb and Huttenlocher (2005). Feature-based techniques also dominate other
recognition tasks, such as scene recognition (Zhang, Marszalek, Lazebnik et al. 2007) and
panorama and location recognition (Brown and Lowe 2007; Schindler, Brown, and Szeliski
2007). And while interest point (patch-based) features tend to dominate current research,
some groups are pursuing recognition based on contours (Belongie, Malik, and Puzicha 2002)
and region segmentation (Figure 1.10f) (Mori, Ren, Efros et al. 2004).
Another significant trend from this past decade has been the development of more efficient
algorithms for complex global optimization problems (see Sections 3.7 and B.5 and Szeliski,
Zabih, Scharstein et al. 2008; Blake, Kohli, and Rother 2010). While this trend began with
work on graph cuts (Boykov, Veksler, and Zabih 2001; Kohli and Torr 2007), a lot of progress
has also been made in message passing algorithms, such as loopy belief propagation (LBP)
(Yedidia, Freeman, and Weiss 2001; Kumar and Torr 2006).
The final trend, which now dominates a lot of the visual recognition research in our com-
munity, is the application of sophisticated machine learning techniques to computer vision
problems (see Section 14.5.1 and Freeman, Perona, and Sch¨ olkopf 2008). This trend coin-
cides with the increased availability of immense quantities of partially labelled data on the
Internet, which makes it more feasible to learn object categories without the use of careful
human supervision.

1.3 Book overview

In the final part of this introduction, I give a brief tour of the material in this book, as well
as a few notes on notation and some additional general references. Since computer vision is
such a broad field, it is possible to study certain aspects of it, e.g., geometric image formation
and 3D structure recovery, without engaging other parts, e.g., the modeling of reflectance and
shading. Some of the chapters in this book are only loosely coupled with others, and it is not