This paper presents an application of image processing methods in Civil Engineering. The aim is to establish the granulometry of a riprap. A riprap is a set of big stones covering an embankment dam. Some images of the riprap surface are acquired by a video camera. These images are then thresholded to separate stones from the voids between them. Depending on the acquisition conditions (lighting, moving . . .), this step does not give satisfying enough results to enable measures of diameters. As knowing diameters enables to evaluate stone weight and then to establish the granulometry, the process requires improving particle detection. Though the thresholding step gives a good detection of voids between stones, particules remain connected, and then, it is not possible to measure stone diameters. Disconnecting particles is equivalent to edge detection. A first approach by gradient computation has been considered. The results are not very satisfying but they enable us to estimate the sieving diameter, but not the maximal diameter. Then edge detection has been quite completed, using a correlation of two distance maps. The correlation image distinguishes points belonging to stone edges from noisy points. And then, 90% edge points are detected. Particles are disconnected enough for diameter measurement. Two diameter values per stone are measured, corresponding to the maximal diameter and the sieving diameter. They are extracted from the particle projection and enable to statistically estimate the third diameter. Then a granulometry curve can be drawn, and it is compared to experimental results.