Feature matching for illumination variation images

Zhenfeng Shao, Min Chen, Chong Liu

Abstract: Illumination variability is one of the most important issues affecting imagery matching performances and still remains a critical problem in the literature, although different levels of improvement have been reported in recent years. This study proposes an illumination robust image matching method. There are three steps in the proposed method: first, local regions are extracted and matched from the input images by using a multiresolution region detector and an illumination robust shape descriptor; second, an algorithm is proposed to estimate the overlapping areas of images and enhance them based on the region matches; finally, general feature detectors and descriptors are combined to process the previous results for illumination robust matching. Experimental results demonstrate that the proposed matching method provides significant improvement in robustness for illumination change images compared with traditional methods.