A hybrid edge detection method, using a combination of gradients and zero crossings to accurately locate the edges in an image, is proposed. By convolving the image with the gradient and second-order derivative operators deduced from a cubic facet model, computer experiments are carried out for extracting edge information from real images. Sharp image edges are obtained from a variety of sample images, without need of computation of the cubic facet coefficients. Under noisy conditions, impact of noise on the detected edge map represents isolated points or small clusters, which can be easily reduced by a postcleaning process. The hybrid edge detection technique, combined with a postprocessing method, results in comparable or better noise reduction than Canny. Compared with the Canny approach, the proposed algorithm has higher efficiency while providing a larger figure of merit.