Edge Drawing: A combined real-time edge and segment detector

We present a novel edge segment detection algorithm that runs real-time and produces high quality edge segments, each of which is a linear pixel chain. Unlike traditional edge detectors, which work on the thresholded gradient magnitude cluster to determine edge elements, our method first spots sparse points along rows and columns called anchors, and then joins these anchors via a smart, heuristic edge tracing procedure, hence the name Edge Drawing (ED). ED produces edge maps that always consist of clean, perfectly contiguous, well-localized, one-pixel wide edges. Edge quality metrics are inherently satisfied without a further edge linking procedure. In addition, ED is also capable of outputting the result in vector form as an array of chain-wise edge segments. Experiments on a variety of images show that ED produces high quality edge maps and runs up to 10% faster than the fastest known implementation of the Canny edge detector (OpenCV’s implementation).

► We propose a novel edge and segment detection algorithm which runs in real-time. ► Edge Drawing (ED) algorithm always produces high quality chain-wise edge segments. ► ED uses a high level cognitive reasoning to imitate human’s edge detection strategy. ► We compare our algorithm’s results with the well known Canny algorithm. ► We present an extensive set of experimental results on edge and segment detection.