![](bg8.png)
multi-mosaics enforces the robot to remain at a single
position during image acquisition it is not possible to
gather visual information while moving. Neverthe-
less, this is not a serious drawback. Most of the time
a scene can even better be represented by scanning
it from some key positions within the scene than by
monitoring all the different pathways the robot pur-
suits. Besides, building mosaics while the robot is
moving bears different problems. On the one hand no
closed-form solution for modelling the camera mo-
tion is available since homographies do not hold in
such situations, and on the other hand appropriate mo-
saic images, like, e.g., manifold mosaics (Peleg et al.,
2000), often exhibit (perspective) distortions of the
scene data hampering easy analysis and scene under-
standing. The multi-mosaics are currently primarily
used to extract additional views of an object in object
learning and recognition. However, they could also
be used for extracting 3D data of the scene provided
that the mosaic 3D world positions are given as, e.g.,
proposed in (Teller, 1998).
6 SUMMARY AND
CONCLUSIONS
Active scene analysis and exploration gains increas-
ing importance in computer vision. Since analyz-
ing image sequences of active cameras has proven a
suitable base for extracting useful information from
a scene, interactive and mobile systems are nowa-
days often equipped with active sensing devices. The
visual scene memory based on multi-mosaics pre-
sented in this paper perfectly fits into this framework
as an additional module between active data acqui-
sition on the one hand and its analysis on the other.
The memory is based on a polytopial reference coor-
dinate system. In contrast to spheres and cylinders the
polytopes provide an euclidean reference frame and,
hence, allow the direct application of standard image
analysis techniques. This is important for interactive
systems since they can work on the memorized data as
on the originally acquired input images. Further on,
based on this euclidean mosaic representation and the
chosen data processing strategy, the data within the
memory is easily updated in an online fashion. Incre-
mental parameter estimation and integration heuris-
tics are used in combination with the focus image
plane. The latter masks the underlying polytope struc-
ture of the memory and thus allows efficient data ac-
cess despite present discontinuities on the memory it-
self. Given these techniques the memory works quite
stable in practice, nevertheless, future work has to be
carried out on investigating more robust online para-
meter estimation techniques and mechanisms for au-
tomatically detecting registration errors.
The memory is ideally suited to be used with inter-
active and mobile systems that have to store and after-
wards access image sequences. Especially systems in
human-machine interaction significantly benefit from
the memory as it provides an improved and more ef-
ficient exploition of available visual data and yields a
higher flexibility as it is necessary to act in dynam-
ically changing environments as well as to perform
intuitive communication with human beings.
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