Robust Binary Feature Using the Intensity Order

Yukyung Choi, Chaehoon Park, Joon-Young Lee, In So Kweon

Abstract: Binary features have received much attention with regard to memory and computational efficiency with the emerging demands in the mobile and embedded vision systems fields. In this context, we present a robust binary feature using the intensity order. By analyzing feature regions, we devise a simple but effective strategy to detect keypoints. We adopt an ordinal description and encode the intensity order into a binary descriptor with proper binarization. As a result, our method obtains high repeatability and shows better performance with regard to feature matching with much less storage usage than other conventional features. We evaluate the performance of the proposed binary feature with various experiments, demonstrate its efficiency in terms of storage and computation time, and show its robustness under various geometric and photometric transformations.