Introduction - Hierarchical Neural Networks for Image Interpretation - page 2

Computer vision, that is the extraction of information from images and image se-
quences, is also important for applications other than human-computer interaction.
For instance, it can be used by robots to extract information from their environment.
In the same way visual perception is crucial for us, it is for autonomous mobile
robots acting in the world designed for us. A driver assistance system in a car, for
example, must perceive all the signs and markings on the road, as well as other cars,
pedestrians, and many more objects.
Computer vision techniques are also used for the analysis of static images. In
medical imaging, for example, it can be used to aid the interpretation of images
by a physician. Another application area is the automatic interpretation of satellite
images. One particularly successful application of computer vision techniques is the
reading of documents. Machines for check reading and mail sorting are widely used.
1.1.2 Performance of the Human Visual System
Human performance for visual tasks is impressive. The human visual system per-
ceives stimuli of a high dynamic range. It works well in the brightest sunlight and
still allows for orientation under limited lighting conditions, e.g. at night. It has been
shown that we can even perceive single photons.
Under normal lighting, the system has high acuity. We are able to perceive object
details and can recognize far-away objects. Humans can also perceive color. When
presented next to each other, we can distinguish thousands of color nuances.
The visual system manages to separate objects from other objects and the back-
ground. We are also able to separate object-motion from ego-motion. This facilitates
the detection of change in the environment.
One of the most remarkable features of the human visual system is its ability to
recognize objects under transformations. Moderate changes in illumination, object
pose, and size do not affect perception. Another invariance produced by the visual
system is color constancy. By accounting for illumination changes, we perceive dif-
ferent wavelength mixtures as the same color. This inference process recovers the
reflectance properties of surfaces, the object color. We are also able to tolerate de-
formations of non-rigid objects. Object categorization is another valuable property.
If we have seen several examples of a category, say dogs, we can easily classify an
unseen animal as dog if it has the typical dog features.
The human visual system is strongest for the stimuli that are most important to
us: faces, for instance. We are able to distinguish thousands of different faces. On
the other hand, we can recognize a person although he or she has aged, changed hair
style and now wears glasses.
Human visual perception is not only remarkably robust to variances and noise,
but it is fast as well. We need only about 100ms to extract the basic gist of a scene,
we can detect targets in naturalistic scenes in 150ms, and we are able to understand
complicated scenes within 400ms.
Visual processing is mostly done subconsciously. We do not perceive the diffi-
culties involved in the task of interpreting natural stimuli. This does not mean that
this task is easy. The challenge originates in the fact that visual stimuli are frequently