A rotation invariant feature descriptor O-DAISY and its FPGA implementation

State-of-the-art local feature descriptors like SIFT or SURF require a significant amount of computational power which prevents their usage in applications with real time constraints. Despite recent efforts to simplify the calculation of feature descriptors, a faster computation comes often to the disadvantage of weakening the invariance to rotation or scale. Recently, Tola et al. introduced DAISY, a new local feature descriptor for wide-baseline matching across stereo image pairs. It is shown that DAISY outperforms SIFT in terms of matching accuracy while being computed significantly faster. This paper takes on the idea of DAISY by proposing a rotational invariant extension of the descriptor, called O-DAISY, and outlining its implementation on FPGA to achieve real time performance. The results are benchmarked against its original version and against the widely used descriptors BRIEF and SURF on a standardized image set.