Segmentation of silt particles from exposure with background by use of second derivative

Abstract

Recently a lot of research is being done on cohesive sediment. It plays a major role in the shoaling of harbours and waterways, and in some serious environmental problems. To predict cohesive sediment transport, information is needed about the distributions of size and settling velocities. Many methods exist to determine sizes of suspended particles, but most are not applicable to cohesive sediment floes, because of their fragility. If not at sampling, the floes break at the subsequent analysis by for example the Coulter Counter or the pipet method. In case of analysis by the Owen tube another problem occurs next to the floc break up at sampling: the long duration of the analysis leads to additional flocculation and causes the measured distribution to be even more unrealistic. To solve these problems, exposures are made by underwater cameras, which give instantaneous information about the undisturbed samples. From one exposure the floc sizes can be determined, and from two successive exposure with known time between them, the settling velocities can be determined. So far, the analysis of exposures of floes was mainly done by hand. Image processing by computer provides a way to do this automatically. It saves time, and consequently more flocs can be analyzed, leading to more representative distributions. The subject of this report is the development and testing of an image processing program to distinguish the objects with use of second derivative method. This method is developed and is compared with other methods. The program is applied to digitized exposures, as can be made by a framegrabber. The framegrabber converts a recording on tape or from a ccd camera into a matrix of digits, the value of each digit representing the brightness of the corresponding pixel. From this grey value image, the image processing program has to distinguish the relevant objects, in other words, make a binary image, consisting of object pixels and non object pixels. This is quite complicated, due to inevitable interferences on the exposures like background features and shadow effects. After producing the binary image, the next program determines particle sizes and calculates the mean size and spread, the measure of the broadness of the distribution. CONCLUSIONS - Second derivative method: the deviation in the calculated mean size and the spread with use of second derivative method seen in absolute values is below 0.25%. - Minmax method: In the situation of uniform illumination, the deviations in the calculated mean and the sigma are both round 0.2% if the brightness of the objects is 255 and 0.5% if it is 127. In the case of strong non-uniform illumination it can go up to 4%. - Uniform method: In the situation of uniform illumination, the deviations in the calculated mean and the sigma are round 1 and 7% respectively. This can go up to 1.5-2% and 7.5% respectively for non-uniform illumination.