A Fast and Robust Binary Feature Descriptor Based on Tuning Sampling Pattern

Based on two sampling patterns’ characteristics of BRISK and FREAK, a new sampling pattern which results in a better descriptor is proposed. Both sampling-point density and size of overlapping receptive fields on sampling pattern have a significantly effect on the performance of descriptor through analyzing both sampling patterns of BRISK and FREAK. Optima of both factors are got from respective tests of describing local area to be more discriminative and enable the sampling pattern to produce a descriptor with better performance. Through a comparing performance test with state-of-the-art descriptors on benchmark datasets, the proposed descriptor in combination with the SURF detector has been proved to be a high-performance one to capture the most discriminative information in the detected salient region.