Towards binary robust fast features using the comparison of pixel blocks
Mariusz Oszust · Measurement Science and Technology · 2016
Abstract Binary descriptors have become popular in many vision-based applications, as a fast and efficient replacement of floating point, heavy counterparts. They achieve a short computation time and low memory footprint due to many simplifications. Consequently, their robustness against a variety of image transformations is lowered, since they rely on pairwise pixel intensity comparisons. This observation has led to the emergence of techniques performing tests on intensities of predefined pixel regions. These approaches, despite a visible improvement in the quality of the obtained results, suffer from a long computation time, and their patch partitioning strategies produce long binary strings requiring the use of salient bit detection techniques. In this paper, a novel binary descriptor is proposed to address these shortcomings. The approach selects image patches around a keypoint, divides them into a small number of pixel blocks and performs binary tests on gradients which are determined for the blocks. The size of each patch depends on the keypoint’s scale. The robustness and distinctiveness of the descriptor are evaluated according to five demanding image benchmarks. The experimental results show that the proposed approach is faster to compute, produces a short binary string and offers a better performance than state-of-the-art binary and floating point descriptors.