Secondary abstract: |
A computer vision is a special kind of scientific challenge as we are all
users of our own vision systems.
Our vision is definitely a source of the major part of information we
acquire and process each second.
A stereo vision is perhaps even greater challenge, since our own vision
system is a stereo one and
it performs a complex task, which supplies us with 3D information on our
surroundings in a very effective
way.
Making machines see is a difficult problem. On one side we have
psychological
aspects of human visual perception, which try to explain how the visual
information is processed
in the human brain. On the other side we have technical solutions, which try
to imitate
human vision. Normally, it all starts with capturing
digital images that store the basic information
about the scene in a similar way that humans see. But this information
represents only the
beginning of a difficult process. By itself it does not reveal the
information about the objects on the scene,
their color, distances etc.
to the machine. For humans, visual recognition is an easy task, but the
human brain processing methods are
still a mistery to us.
One part of the human visual perception is estimating the distances to the
objects on the scene.
This information
is also needed by robots if we want them to be completely autonomous.
In this dissertation we present a stereo panoramic depth imaging system.
The basic
system is mosaic-based, which means that we use a single standard rotating
camera and assemble the captured images in a multiperspective panoramic
image.
Due to a setoff of the
camera's optical center from the rotational center of the system we are
able to capture the motion parallax effect, which enables the stereo
reconstruction. The camera is rotating on a circular path with the step
defined by an angle equivalent to one-pixel column of the captured image.
To find the corresponding points on a stereo pair of
panoramic images the epipolar geometry needs to be determined.
It can be shown that the epipolar geometry is very simple if we are doing
the reconstruction based on a symmetric pair of stereo panoramic images.
We get a symmetric pair of stereo panoramic images when we take
symmetric columns on the left and on the right side from the captured
image center column.
This system however
cannot generate panoramic
stereo pair in real time. That is why
we have suggested a real time extension of the system,
based on
simultaneously using many
standard cameras.
We have not physically built the real time sensor, but we have performed
simulations to establish the quality of results.
Both systems have been
exhaustively analysed and compared. The analyses revealed a number of
interesting
properties of the systems.
According to the basic system accuracy we definitely can use the system for
autonomous robot localisation and navigation tasks.
The assumptions
made in the real time extension of the basic system
have been proved to be correct,
but the accuracy of the new sensor generally deteriorates in comparison to
the basic sensor.
Generally speaking, the
dissertation can
serve as a guide for panoramic depth imaging
sensor design and related issues. |