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14 1 Introduction

(a) (b) (c)

(d) (e) (f)

Figure 1.9 Examples of computer vision algorithms from the 1990s: (a) factorization-based structure from
motion (Tomasi and Kanade 1992) c 1992 Springer, (b) dense stereo matching (Boykov, Veksler, and Zabih
2001), (c) multi-view reconstruction (Seitz and Dyer 1999) c 1999 Springer, (d) face tracking (Matthews, Xiao,
and Baker 2007), (e) image segmentation (Belongie, Fowlkes, Chung et al. 2002) c 2002 Springer, (f) face
recognition (Turk and Pentland 1991a).

regularization, MRFs, and even higher-level vision).
Three-dimensional range data processing (acquisition, merging, modeling, and recogni-
tion; see Figure 1.8f) continued being actively explored during this decade (Agin and Binford
1976; Besl and Jain 1985; Faugeras and Hebert 1987; Curless and Levoy 1996). The compi-
lation by Kanade (1987) contains a lot of the interesting papers in this area.

1990s. While a lot of the previously mentioned topics continued to be explored, a few of
them became significantly more active.
A burst of activity in using projective invariants for recognition (Mundy and Zisserman
1992) evolved into a concerted effort to solve the structure from motion problem (see Chap-
ter 7). A lot of the initial activity was directed at projective reconstructions, which did not
require knowledge of camera calibration (Faugeras 1992; Hartley, Gupta, and Chang 1992;
Hartley 1994a; Faugeras and Luong 2001; Hartley and Zisserman 2004). Simultaneously, fac-
torization techniques (Section 7.3) were developed to solve efficiently problems for which or-
thographic camera approximations were applicable (Figure 1.9a) (Tomasi and Kanade 1992;
Poelman and Kanade 1997; Anandan and Irani 2002) and then later extended to the perspec-
tive case (Christy and Horaud 1996; Triggs 1996). Eventually, the field started using full
global optimization (see Section 7.4 and Taylor, Kriegman, and Anandan 1991; Szeliski and