556 • S. T. Barnard and M. A. Fischler
HIGH
OBLIQUE
HIGH
OBLIQUE
OBLIQUE
r ~ OBLIQUE
VERTICAL ~
Figure
2. Vertical and oblique aerial imagery. Aerial images are usually recorded in long
sequences f~om an aircraft. Vertical images are made with the camera aligned as closely as possible
with the true vertical. Oblique images are made by intentionally aligning the camera between the
true vertical and horizontal directions. Oblique views that include the horizon are called
"ldgh
oblique." Even though oblique views are somewhat more difficult to analyze than vertical views,
they cover more area and are therefore a less expensive means of image acquisition. (From
THOM66. Reprinted with permission from the American Society of Photogrammetry; copyright
1966 by the American Society of Photogrammetry.)
has been studied in two contexts: as a pas-
sive navigation aid for robot aircraft
[HANN80], and as part of a control system
for surface vehicles [MORA79, MORAS1,
GESNS0]. The images used for aircraft nav-
igation are similar to the aerial photographs
used for cartography, except that long se-
quences of images are used, and multispec-
tral sensors are often employed. The images
used for surface vehicle control, however,
are quite different; they are horizontal,
comparatively high-resolution images.
Research on computational models of hu-
man stereo vision has largely employed syn-
thetic random-dot stereograms for experi-
mental investigation [MARR76, GRIM79,
GRIM80, GRIMS1]. A random-dot stereo-
gram consists of two synthetic images of
uncorrelated dots, which happen to be per-
spective views of the same virtual surfaces
[JULE71]. Each image by itself contains no
information for depth because it consists of
only random dots. When the two are viewed
stereoscopically, however, the three-dimen-
sional virtual surfaces are readily perceived.
Random-dot stereograms exclude all mo-
nocular depth cues, and the exact corre-
spondences are known because the images
are generated synthetically. Because the
parameters of random-dot stereograms,
such as noise and density, can be controlled,
they allow systematic comparison of human
and machine performance. Experiments on
human stereo vision using natural imagery
instead of random-dot stereograms have
also been done (e.g., see GRIM80).
Different stereo applications often in-
volve different kinds of scenes. Perhaps the
most significant and widely recognized dif-
ference in scene domains is between scenes
containing cultural features such as build-
ings and roads, and those containing only
natural objects and surfaces, such as moun-
tains, flat or "rolling" terrain, foliage, and
water. Important stereo applications range
over both domains. Low-resolution aerial
imagery, for example, usually contains
mostly natural features, although cultural
features are sometimes found. Industrial
applications, on the other hand, tend to
Computing Surveys, Vol. 14, No 4, December 1982