Improving the robustness in feature detection by local contrast enhancement

Abstract

This paper presents a new feature detector, with improved local contrast performance. The proposed method is based on an improved non-linear version of the classic Difference of Gaussians, which exhibits increased sensitivity to low contrast. Additionally, it does not employ computationally expensive or memory demanding routines. In order to evaluate the degree of illumination invariance that the proposed, as well as, other existing detectors exhibit, a new benchmark image database has been created. It features a greater variety of imaging conditions, compared to existing databases, containing real scenes under various degrees and combinations of uniform and non-uniform illumination. Experimental results show that the proposed detector extracts greater number of features, with high level of repeatability, compared to other existing ones. These results are evident for both uniform and non-uniform illumination, evincing a favorable usage of the proposed feature detector by robotic platforms working in outdoor working environments.