Detection of scale-invariant key points employing a resistive network

We assessed the feasibility of applying a resistive network (RN) filter to the scale-invariant feature transform (SIFT) algorithm by performing computer simulations for the hardware implementation of the filter. SIFT is an algorithm for computer vision to describe and detect local features that are invariant to scale and rotation of objects. However, it is difficult to perform multiple spatial filterings in SIFT algorithm in real time due to its high computational cost. To solve this problem, we employed an RN which performs spatial filtering instantaneously with extremely low power dissipation. In order to apply an RN filter to the SIFT algorithm instead of Gaussian filter, which is employed in the original SIFT algorithm, we investigated the difference in the spatial properties of the two filters. We simulated the SIFT algorithm employing the RN filter on a computer, and we demonstrated that key points were detected at the same place irrespective of the image size, and that the scale of the key point was detected appropriately.