Proceedings of the Third International Conference on Computer Vision Theory and Applications , Funchal, Portugal, 22 - 25 January 2008, pp.214-221
The transition to symbolic information from images involves in general the loss or misclassification of information. One way to deal with this missing or wrong information is to get feedback from concrete hypotheses
derived at a symbolic level to the sub-symbolic (signal) stage to amplify weak information or correct misclassifications. This paper proposes such a feedback mechanism between the symbolic level and the signal level,
which we call signal symbol loop. We apply this framework for the detection of low contrast edges making
use of predictions based on Rigid Body Motion. Once the Rigid Body Motion is known, the location and
the properties of edges at a later frame can be predicted. We use these predictions as feedback to the signal
level at a later frame to improve the detection of low contrast edges. We demonstrate our mechanism on a real
example, and evaluate the results using an artificial scene, where the ground truth data is available.