Intensity edge detection with stack filters

Recently, a morphological method was proposed for edge detection in which intensity edges were obtained by thresholding the difference between the image and a dilated version of the image. While this technique is promising, it is quite sensitive to noise. To improve noise immunity and robustness, we propose using stack filters to estimate the dilated and eroded versions of the image, and then threshold the difference between these two images. Comparisons between this stack filter based technique and some standard edge detectors are provided. For instance, we find that this approach yields results comparable to those obtained with the Canny operator for images with additive Gaussian noise, but works much better when the noise is impulsive. Extensive simulations with many different images and different types of noise were performed. Pratt's figure of merit was used as an objective measure of performance on synthetic images. Many natural scenes were also used to test the performance of this technique. The results indicate that this approach is robust with respect to changes in both the image and the noise. In other words, filters obtained by training on one image and one type of noise work well even when both the image and noise statistics vary.