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\begin{center}
{\bf Early Processing in Sketch Understanding}\\[8mm]Tevfik Metin Sezgin and Randall Davis
\end{center}

\vspace{0.4cm}

{\bf Abstract:} 

Freehand sketching is both a natural and crucial part
of design, yet is almost totally unsupported by current design automation
software. We are combining the flexibility and ease of use of paper and
pencil with the processing power of a computer to produce a design
environment that feels as natural as paper, yet is far smarter. One of the
most basic steps in accomplishing this goal is converting the original pen
strokes into the intended geometric objects, the early processing part of
the task. In order to reduce the ambiguity in sketches, some of which is 
inherent, a robust system is essential.



{\bf Motivation:} Current CAD systems provide extensive support for
producing precise and detailed shop drawings of devices and are of
considerable utility. But in early stages of a design process, when the
intent is to brainstorm and test out different ideas, a less precise
model, or perhaps a rough sketch, is what most designers need, hence the
extensive use of pencil and paper. But while this feels natural and easy
to use, it has the obvious drawback that the result is at best an image,
rather than a good model of the device. 



{\bf Previous Work:} Mark Gross has worked on recognition and interpretation
of diagrams in design \cite{MG}. His Electronic Coctail Napkin 
architecture provides a computational environment for working with design 
diagrams. In their joint work Mark Gross and Ellen Do stressed the 
importance of employing a natural input scheme during design \cite{MG1}.

Earlier work in our group \cite{MM,LW}produced a
basic sketch recognition system for the mechanical devices domain. This
system recognizes some basic geometric objects and domain specific shapes,
but focused primarily on recognizing mechanical components rather than
careful processing of the basic input data. The present system is far more
robust, in part because it views the sketch as a drawing being created
while the computer watches, and hence can make use of both traditional
geometric information (e.g., direction change) and information about pen
velocity.


{\bf Approach:} We use a digitizing tablet to capture pen’s position in
time, which permits examining pen speed, acceleration and direction of
movement. Using these properties as distinguishing features we extract
potential vertices of each stroke using two different recognition schemes
that assign a certainty to the vertices. Each recognition method suggests
a candidate fit approximating the input stroke. These fits are fed into a
blackboard system that generates more candidate fits using the two input
fits and that returns the best fit. 

\bigskip
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\caption{A stroke that contains both cuves and straight lines
(shown in black).  The output generated by the recognizer system consists
of B\'{e}zier curves and straight lines (shown in red, green, and blue). }
\end{figure}

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\rotatebox[origin=c]{0}{\includegraphics[scale=.6]{engine3_before.eps}}
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\caption{A sample snapshot of a rough sketch for an engine (left), and
the output generated by the recognizer. }

\end{figure}

{\bf Difficulty:} One of the major problems is the noise in the input.
Even strokes that appear straight contain significant amount of noise, a
consequence of freehand drawing. A variety of well-known filtering
algorithms fail to produce any significant improvement because the number
of data points available is generally small compared to what is needed. A
second, related difficulty is that although commercially available
digitizing tablets can support sampling rates above 100 Hz, most
applications do not require such high sampling rates, so the actual report
rate remains around 40-50 Hz. 


{\bf Impact:} Our system gives the user the freedom and convenience of
using a digitizing tablet like a paper, and puts the processing power of a
computer on the user's service. It frees the user from the trouble of
selecting among different tools for drawing different shapes. In addition
it saves time and effort by eliminating the need to export sketches on
paper to electronic format. 



{\bf Future Work:} Our current system is to some extent tuned to the input
capture device in use and while changes to the constants can be done
manually, a self-configuring system would be far better. Also alternate
methods of filtering the noise in the data may improve the performance. 

Beyond these short-term improvements, we aim to integrate this low
level recognition system with higher level sketch understanding and
interpretation modules. Our sytem serves as the link between the raw
input data and their low level geometric representations. The next step
involves mapping these geometric objects to actual domain specific 
entities. This will certainly enable more useful analysis and aid 
the early design process.


{\bf Research Support:} This work is supported in part by the Ford/MIT
Collaboration under the Virtual Engineering project, in part by the
Lockheed Martin Space Operations Company under CSOC Contract NAS9-98100,
and in party by the MIT Oxygen Collaboration.

% \bibliographystyle{plain}
{\bf References:} 
\begin{thebibliographyb}{10}

\bibitem[1]{MG}
M.D. Gross.
\newblock Recognizing and Interpreting Diagrams in Design.
\newblock {\em 2nd Annual International Conference on Image
  Processing}, pp. 308-311, 1995

\bibitem[2]{MG1}
M.D. Gross and E. Do.
\newblock Ambiguous Intentions: a Paper-like Interface for Creative
Design.
\newblock {\em Ninth Annual Symposium for User Interface Software and
  Technology}, pp. 183-192. Marc Brown and Ramana Rao (eds.), Seattle,
1996.


\bibitem[3]{MM}
M. D. Muzumdar
\newblock ICEMENDR: Intelligent Capture Environment for Mechanical Engineering Design.
\newblock Master's Thesis, MIT, 1999

\bibitem[4]{LW}
\newblock A Foundation for Intelligent Multimodal Drawing and Sketching Programs. 
\newblock Master's Thesis, MIT, 1999


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