An FPGA implementation for real-time edge detection

Abstract

The Hessian matrix-based edge detection algorithm of Dr. Carsten Steger has the advantages of high accuracy and versatility. However, this algorithm has a complex and time-consuming computation process. Large-scale Gaussian convolution also employs a large number of multipliers when implemented on a field programmable gate array (FPGA). To address these problems, an FPGA implementation for Steger’s edge detection algorithm is proposed. This implementation employs pipeline and parallel architectures at both task and data levels for data stream processing. The original kernels of Gaussian convolution are simplified with box-filter to convert the multiplication operation in the convolution into addition, subtraction, or shift operations with the concept of integral image, thereby minimizing the multiplier resources. The proposed FPGA implementation demonstrates a favorable accuracy and anti-noise capability when dealing with different degrees of blur and noise in an image. Therefore, the FPGA implementation can satisfy real-time edge detection requirements.