Center-oriented Selection
### Weighting Selection

In the situation when the version component of the pursuit system keeps track
of the target in the image, the disparity information relevant to the
vergence error will be observed around the estimated position of the target.
Using the confidence
values as a weighting factor for the individual disparity estimates and
combining this with a Gaussian envelope, $G(x,y;\sigma)$ in order to suppress
the peripheral disparities, center-oriented disparity is estimated using the
following:
\begin{eqnarray}
D_c=\frac{\displaystyle{\sum_{x,y} C(x,y) G(x-x_0,y-y_0;\sigma) D(x,y)}}{\displaystyle{\sum_{x,y} C(x,y) G(x-x_0,y-y_0;\sigma)}}.
\end{eqnarray}
The Gaussian envelope is placed at the predicted position of the target
$(x_0,y_0)$. The standard deviation $\sigma$ can be adjusted according to the
size of the target and the priority given to the vicinity of the predicted
target position.
### Summary

In this article, we have considered the problem of disparity selection, a
vital component of successful camera vergence in binocular pursuit, in which
also the incorporation of time in disparity estimation plays a central
role in the success.
The
figure
shows the scheme of the algorithm.
Based on the disparity and confidence
maps produced by the phase-based method,
a histogram-based approach and a weighting approach, are introduced to
solve the problem of disparity selection.
Experimental results including the comparison of the approaches will be
presented in the paper along with a description of the algorithms.
\caption{The scheme of the algorithm. The paper describes the part marked by the dashed line.}
\label{fig:frame}
\bibliography{/home/maki/bib/mva94}
**Full paper:**
PostScript 164k

Atsuto Maki
<maki@bion.kth.se>
Tomas Uhlin
<tomas@bion.kth.se>
Jan-Olof Eklundh
<joe@bion.kth.se>