Developing 3 dimensional image analysis methods for aggregates

In this book, 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.

Autorentext

He has received his BS from F rat Uni., MS from Pamukkale Uni., and the PhD from Dokuz Eylul Uni.. His current research interests image processing, software development, fuzzy logic, artificial neural networks, and pattern recognition. Now, he is an Assistant Professor at Computer Engineering Department of Adnan Menders Uni., Ayd n, Turkey.