conclusion.tex

\section{Conclusion and perspectives}

\label{sec:conclusion}

This paper introduced a new straight edge detector based on a local analysis of
the image gradient and on the use of blurred segments to embed an
estimation of the detected edge thickness.
It relies on directional scans of the input image around maximal values of the
gradient magnitude, and on
%that have previously been presented in \cite{KerautretEven09}.
%Despite of good performances achieved, the former approach suffers of two
%major drawbacks: the inaccurate estimation of the blurred segment width
%and orientation, and the lack of guarantee that it is completely detected.
%These limitations were solved through the integration of two new concepts:
%adaptive directional scans that continuously adjust the scan strip
%to the detected blurred segment direction;
%The main limitations of the former approach were solved through
the integration of two new concepts:
adaptive directional scans that continuously adjust the scan strip
to the detected edge direction;
the control of the assigned width based on the observation of the
blurred segment growth.
Expected gains in accuracy and execution time were confirmed by
held experiments.

A residual weakness of the approach is the sensitivity to the initial
conditions.
In supervised context, the user can select a favourable area where
the awaited edge is dominant.
This task is made quite easier, thanks to the stabilization produced by
the duplication of the initial detection.
But in unsupervised context, gradient perturbations in the early stage of
the edge expansion, mostly due to the presence of close edges, can deeply
affect the result.
In future works, we intend to provide solutions to this drawback
by scoring the detection result on the basis of a characterization of the
local context.
%
%Then experimental validation of the consistency of the estimated width and
%orientation values on real situations are planned in different application
%fields.
%In particular, straight edges are rich visual features for 3D scene
%reconstruction from 2D images.
%The preimage of the detected blurred segments,
%i.e. the space of geometric entities which numerization matches this
%blurred segment, may be used to compute some confidence level in the 3D
%interpretations delivered, as a promising extension of former works
%on discrete epipolar geometry \cite{NatsumiAl08}.

%\section*{Acknowledgements}