A new self-adapting architecture for feature detection

Abstract

In this paper, we present a FPGA based flexible self-adapting architecture for two features detectors, the Canny edge detector and the Harris corner detector, with reduced latency and memory requirements, and supporting variable resolution images. The new architecture uses neighbourhood extractors that can self-adapt its parameters on-the-fly and algorithm simplifications to reduce mathematical complexity, memory requirements and latency without losing reliability.