Combining a fuzzy rule-based classifier and illumination Invariance for improved building detection

The problem of edge-based classification of natural video sequences containing buildings and captured under changing lighting conditions is addressed in this letter. The introduced approach is derived from two empiric observations: In static regions the likelihood of finding features that match the patterns of "buildings" is high because buildings are rigid static objects; and misclassification can be reduced by filtering out image regions changing or deforming in time. These regions may contain objects semantically different to buildings but with a highly similar edge distribution, e.g., high frequency of vertical and horizontal edges. Using these observations a strategy is devised in which a fuzzy rule-based classification technique is combined with a method for changing region detection in outdoor scenes. The proposed approach has been implemented and tested with sequences showing changes in the lighting conditions. Selected results from the experimental evaluation are reported.