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1.2 A brief history 15

Kang 1994; Azarbayejani and Pentland 1995), which was later recognized as being the same
as the bundle adjustment techniques traditionally used in photogrammetry (Triggs, McLauch-
lan, Hartley et al. 1999). Fully automated (sparse) 3D modeling systems were built using such
techniques (Beardsley, Torr, and Zisserman 1996; Schaffalitzky and Zisserman 2002; Brown
and Lowe 2003; Snavely, Seitz, and Szeliski 2006).
Work begun in the 1980s on using detailed measurements of color and intensity combined
with accurate physical models of radiance transport and color image formation created its own
subfield known as physics-based vision. A good survey of the field can be found in the three-
volume collection on this topic (Wolff, Shafer, and Healey 1992a; Healey and Shafer 1992;
Shafer, Healey, and Wolff 1992).
Optical flow methods (see Chapter 8) continued to be improved (Nagel and Enkelmann
1986; Bolles, Baker, and Marimont 1987; Horn and Weldon Jr. 1988; Anandan 1989; Bergen,
Anandan, Hanna et al. 1992; Black and Anandan 1996; Bruhn, Weickert, and Schn¨ orr 2005;
Papenberg, Bruhn, Brox et al. 2006), with (Nagel 1986; Barron, Fleet, and Beauchemin 1994;
Baker, Black, Lewis et al. 2007) being good surveys. Similarly, a lot of progress was made
on dense stereo correspondence algorithms (see Chapter 11, Okutomi and Kanade (1993,
1994); Boykov, Veksler, and Zabih (1998); Birchfield and Tomasi (1999); Boykov, Veksler,
and Zabih (2001), and the survey and comparison in Scharstein and Szeliski (2002)), with
the biggest breakthrough being perhaps global optimization using graph cut techniques (Fig-
ure 1.9b) (Boykov, Veksler, and Zabih 2001).
Multi-view stereo algorithms (Figure 1.9c) that produce complete 3D surfaces (see Sec-
tion 11.6) were also an active topic of research (Seitz and Dyer 1999; Kutulakos and Seitz
2000) that continues to be active today (Seitz, Curless, Diebel et al. 2006). Techniques for
producing 3D volumetric descriptions from binary silhouettes (see Section 11.6.2) continued
to be developed (Potmesil 1987; Srivasan, Liang, and Hackwood 1990; Szeliski 1993; Lau-
rentini 1994), along with techniques based on tracking and reconstructing smooth occluding
contours (see Section 11.2.1 and Cipolla and Blake 1992; Vaillant and Faugeras 1992; Zheng
1994; Boyer and Berger 1997; Szeliski and Weiss 1998; Cipolla and Giblin 2000).
Tracking algorithms also improved a lot, including contour tracking using active contours
(see Section 5.1), such as snakes (Kass, Witkin, and Terzopoulos 1988), particle filters (Blake
and Isard 1998), and level sets (Malladi, Sethian, and Vemuri 1995), as well as intensity-based
(direct) techniques (Lucas and Kanade 1981; Shi and Tomasi 1994; Rehg and Kanade 1994),
often applied to tracking faces (Figure 1.9d) (Lanitis, Taylor, and Cootes 1997; Matthews and
Baker 2004; Matthews, Xiao, and Baker 2007) and whole bodies (Sidenbladh, Black, and
Fleet 2000; Hilton, Fua, and Ronfard 2006; Moeslund, Hilton, and Kr¨ uger 2006).
Image segmentation (see Chapter 5) (Figure 1.9e), a topic which has been active since
the earliest days of computer vision (Brice and Fennema 1970; Horowitz and Pavlidis 1976;
Riseman and Arbib 1977; Rosenfeld and Davis 1979; Haralick and Shapiro 1985; Pavlidis
and Liow 1990), was also an active topic of research, producing techniques based on min-
imum energy (Mumford and Shah 1989) and minimum description length (Leclerc 1989),
normalized cuts (Shi and Malik 2000), and mean shift (Comaniciu and Meer 2002).
Statistical learning techniques started appearing, first in the application of principal com-
ponent eigenface analysis to face recognition (Figure 1.9f) (see Section 14.2.1 and Turk and
Pentland 1991a) and linear dynamical systems for curve tracking (see Section 5.1.1 and Blake
and Isard 1998).