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The analysis of microscopy images has always been an important yet time consuming process in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points. more...

This paper describes an imaging system that was developed to aid industrial bin picking tasks. The purpose of this system was to provide accurate 3D models of parts and objects in the bin, so that precise grasping operations could be performed. The technology described here is based on two types of sensors: range mapping scanners and video cameras. The geometry of bin contents was reconstructed from range maps and modeled using superquadric representations, providing location and parts surface information that can be employed to guide the robotic arm. Texture was also provided by the video streams and applied to the recovered models. The system is expected to improve the accuracy and efficiency of bin sorting and represents a step toward full automation. [ABSTRACT FROM AUTHOR] more...