Circumferential Binary Feature Extraction and Matching Search Algorithms

Binary features allow for the effective comparison, fast calculation, and compact storage in image matching and localization. Binary feature extraction algorithms, however, tend to have poor mirror invariance, and search algorithms that match binary features have a lower inlier ratio. To address these issues, we employ a scale space pyramid to simulate human eye imaging and then detect FAST (FAST feature detector) points at each level in the pyramid and calculate the FAST point feature's orientation with an image intensity centroid. We propose circumferential binary string mirror invariance rules and a circumferential binary feature (CBF) extraction algorithm to enhance the mirror invariance of binary features, and a fast calculate bitmap (FCBM) algorithm and bitmap local sensitive hash (BMLSH) to improve the inlier ratio of matching binary features. Experiments show that the CBF performs well in mirror invariance and has stronger adaptability and that BMLSH searches inliers more efficiently.