High-speed image feature detection using FPGA implementation of fast algorithm

Many of contemporary computer and machine vision applications require finding of corresponding points across multiple images. To that goal, among many features, the most commonly used are corner points. Corners are formed by two or more edges, and mark the boundaries of objects or boundaries between distinctive object parts. This makes corners the feature points that used in a wide range of tasks. Therefore, numerous corner detectors with different properties have been developed. In this paper, we present a complete FPGA architecture implementing corer detection. This architecture is based on the FAST algorithm. The proposed solution is capable of processing the incoming image data with the speed of hundreds of frames per second for a 512 x 512, 8-bit gray-scale image. The speed is comparable to the results achieved by top-of-the-shelf general purpose processors. However, the use of inexpensive FPGA allows to cut costs, power consumption and to reduce the footprint of a complete system solution. The paper includes also a brief description of the implemented algorithm, resource usage summary, resulting images, as well as block diagrams of the described architecture.