Sequence Image Matching Using Adaptive SIFT under Complex Environmental Conditions

摘要

The scale invariant feature transform (SIFT) is one of effective methods for sequence image matching, but undercomplex environmental conditions such as illumination and blur, the matching rate is low, and the matching processbecomes difficult. It is mainly because of the fixed threshold which results in the particular scenes are not considered. Anew method with adaptive threshold is proposed for sequence image matching in this paper. Firstly, the statisticalfeatures of the sequence images are analyzed, then the comprehensive indicators of each statistical feature are calculatedby the principal component analysis method, and finally, based on main influence factor and statistics features, theadaptive threshold prediction model is established. To test the efficiency of the proposed method, it is used for sequenceimage matching. The experimental results show that the adaptive threshold prediction model can be applied to manycases and improves the matching performance for sequence images under complex environmental conditions, especiallyfor the sequence images under poor illumination.