Fast Detection of Curved Edges at Low SNR
This addresses the need for fast and accurate edge detection in high-noise scenarios, which is crucial for applications in computer vision, though it is an incremental improvement over prior global methods.
The paper tackles the problem of detecting curved edges in noisy images, where existing methods are slow, and presents a novel multiscale method that runs nearly linearly in pixel count, achieving orders of magnitude faster speed while maintaining comparable or better detection quality.
Detecting edges is a fundamental problem in computer vision with many applications, some involving very noisy images. While most edge detection methods are fast, they perform well only on relatively clean images. Indeed, edges in such images can be reliably detected using only local filters. Detecting faint edges under high levels of noise cannot be done locally at the individual pixel level, and requires more sophisticated global processing. Unfortunately, existing methods that achieve this goal are quite slow. In this paper we develop a novel multiscale method to detect curved edges in noisy images. While our algorithm searches for edges over a huge set of candidate curves, it does so in a practical runtime, nearly linear in the total number of image pixels. As we demonstrate experimentally, our algorithm is orders of magnitude faster than previous methods designed to deal with high noise levels. Nevertheless, it obtains comparable, if not better, edge detection quality on a variety of challenging noisy images.