FPGA-Based Flexible Hardware Architecture for Image Interest Point Detection

Abstract

An important challenge in computer vision is the implementation of fast and accurate feature detectors, as they are the basis for high-level image processing analysis and understanding. However, image feature detectors cannot be easily applied in embedded scenarios, mainly due to the fact that they are time consuming and require a significant amount of processing power. Although some feature detectors have been implemented in hardware, most implementations target a single detector under very specific constraints. This paper proposes a flexible hardware implementation approach for computing interest point extraction from grey-level images based on two different detectors, Harris and SUSAN, suitable for robotic applications. The design is based on parallel and configurable processing elements for window operators and a buffering strategy to support a coarse-grain pipeline scheme for operator sequencing. When targeted to a Virtex-6 FPGA, a throughput of 49.45 Mpixel/s (processing rate of 161 frames per second of VGA image resolution) is achieved at a clock frequency of 50 MHz.