A novel neural network approach for image edge detection

In this work a new method for image edge detection based on multilayer perceptron (MLP) is proposed. The method is based on updating a MLP to learn a set of contours drawn on a 3×3 grid and then take advantage of the network generalization capacity to detect different edge details even for very noisy images. The method is applied first to Gray scale images and can be easily extended to color ones. Simulations on synthetic and real image show much promised results in term of precision and localization. Moreover the method works well even for very low contrast images for which other edge operators fail.