Application of Improved Illumination Invariance Algorithm in Building Detection

Abstract

The problem of edge-based classification of natural video sequences containing buildings and captured under changing lighting conditions is addressed. 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 efficiency of the described techniques is implemented and tested with sequences showing changes in the lighting conditions.