Developing 3 dimensional image analysis methods for determining characteristic and classification of aggregates

In this thesis, an interdisciplinary study is presented. In this study, a method for three-dimensional (3D) shape characterization and classification is proposed for the six different aggregates. In the first phase, a new 3D laser based imaging system is designed to capture images of aggregates. The imaging system has been optimized to minimize the errors during image capturing. In the second phase, novel 3D shape characterization parameters of the aggregates are extracted. Geometrical parameters of the aggregates are calculated in 3D spatial domain. The last phase, the aggregates are classified by using different classifier models (ANN, FLDA and KNN) with the help of these parameters. Among the classifier types, multi-layer perceptron neural network model that has two hidden layers gives the best performance that is 99.20 percent. The performance of the proposed system is evaluated using manual measurement method and two-dimensional image processing method. Results are analyzed and compared with other studies given in the literature.