GUD-Canny: a real-time GPU-based unsupervised and distributed Canny edge detector.

The Canny algorithm is one of the most commonly used edge detectors due to its superior performance, especially in noisy environments. Its main limitation is that it is time consuming due to its multistage nature and the use of complex computational operations, primarily hysteresis thresholding. For this reason, many efficient implementations of the Canny edge detector have been developed on different accelerating platforms, such as ASICs, FPGAs and GPUs. The two main limitations of the GPU implementations developed to date are the bottleneck caused by the hysteresis process, and the use of fixed hysteresis thresholds. To overcome these issues, a novel GPU-based unsupervised and distributed Canny edge detector is proposed in this paper. Experimental evaluation showed that our Canny edge detector fully satisfies real time requirements, as it only requires 0.35 ms on average to detect edges on 512 × 512 images, and that it is faster than existing GPU and FPGA implementations. [ABSTRACT FROM AUTHOR]