Evaluation of image corner detectors for hardware implementation

We analyze a number of corner detection algorithms and identify the advantages and disadvantages of each algorithm to evaluate their suitability for hardware implementation. We implemented three popular corner detectors, Plessy, Wang-Brady, and SUSAN, in software and compared them on the basis of their stability, accuracy, speed and computational requirements. The Plessy algorithm was found to have good stability and accuracy, but suffered from a large computational cost. The SUSAN method required the least computational resources and would therefore be suitable for implementation on a simple FPGA platform. However, it did not perform well on real world images. The Wang-Brady method was found to have better stability than SUSAN but worse than the Plessy algorithm while having a lower computational cost than Plessy and a higher cost than that for SUSAN. Despite the higher computational requirements, we conclude that the Plessy algorithm, because of its significantly better performance, is the most appropriate algorithm for hardware implementation.