Recognition and classification of geometric shapes using neural networks

The research presented in this paper refers to classification of geometric shapes (cubes, pyramids and cylinders) using multilayer neural network. The input data of the algorithm are the images of shapes placed in different positions and distances from the camera. The classification is based on feature vectors that are obtained using methods of digital image processing. Feature vectors are inputs of neural network. Supervised training of neural network is performed. Reduction algorithm was used in aim of dimension reduction of feature vectors, so the classification results can be displayed graphically. Recognition and classification of geometric shapes may be of interest for realization of many robotic tasks, especially those related to catching of objects with robotic arm or movement of a robot with a set of obstacles.