Including Interaction in an Automated Modelling System (*)
Institute for Robotics and Intelligent Systems
University of Southern California
Los Angeles, California 90089-0273
Ph.: (213) 740-6428, Fax: (213) 740-7877
email: email@example.com, firstname.lastname@example.org
These WWW pages describe an approach for including operator input and guidance
in an automated building extraction system.
Images and MPEG movies should help the reader to understand the types
of interaction (Please use Netscape Version 1.1 or higher).
A paper about this work
was also be presented at the
1995 IEEE International Symposium on Computer Vision.
Short description of the approach
An approach for including interaction in an automatic building
detection and description system is described. It uses intermediate
results and computations of the automatic analysis to add or correct
the 3-D description of a scene. The proposed method requires a minimum
amount of easily obtainable information from the user. The number of
interaction steps is less or equal to those of computer assisted
manual systems with a final fitting step. The system is built on top
of a a monocular automatic building system developed at USC and has been tested on a number of
examples with good results.
Working on extraction of 3-D site models, we propose
a strategy for combining the activities of an operator and a machine
by taking advantage of what perceptual abilities a machine does
have. Our goal is to provide a minimum amount of input to the machine
and let the machine make the decisions that it can. Our approach is
based on the observation that the automatic systems often work quite
reliably under certain conditions and the operator should not need to
do this work. Also, when automatic systems fail, they fail due to some
salient difficulties. In such cases, the operator may be able to
supply an indication of the difficulty or the desired result which may
suffice for the machine to finish the computation.
We use a monocular automatic building system developed at USC (called USC-MABS from now on) as the
underlying system. This system is designed to find rectilinear
building structures from a monocular image. It uses perceptual
grouping techniques to find likely candidates for building roofs and
uses shadows to confirm them and to estimate their height. The
system's performance is generally quite good when sufficient parts of
the roof boundaries can be extracted. However, in some cases, such as
when the roof is dark, the boundary of the roof with the shadow is not
detected and the system fails to confirm the presence of such
buildings due to lack of sufficient evidence. In such cases, a very
simple guidance from the operator, just indicating that in fact a dark
building is present in the vicinity suffices for the automated system
to find one on its own!
Our methodology does allow for more detailed interaction with the
system, in stages, and as necessary. In the worst case, the system
reduces to the user having to provide all the information as is the
case for most manual systems. However, we find this capability is
seldom needed in our system.
The design goals for our system can be summarized as follows:
"Easy" information for the user would be qualitative information without the need of precision, like
answering the question "Is in the indicated area a building visible but not detected?". The last
requirement could also be stated as: the precision required by the user should be minimized.
- the complexity of the interaction-process should be minimized and in worst case not exceed
the complexity required by a manual system.
- the type of information called up in each step should be easy for the user to determine.
(*)This work was supported in part by the
Advanced Research Projects Agency of
the Department of Defense and was monitored by the Army Topographic
Engineering Center under Contract No. DACA76-93-C-0014.
(**) Stephan Heuel was a visiting researcher
at USC from the Institute of
Photogrammetry in Bonn