For bin picking robots in real logistics installations, the certainty of picking the correct product out of a mixed-product bin is essential. This paper proposes an approach for the robot to efficiently decide whether it can robustly distinguish the product to pick from the others in the bin. If not, the pick has to be routed not to the robot workstation but to a manual picking station. For this, we introduce a modified version of the confusion matrix, which we call the relative confusion matrix. We show how this matrix can be used to make the required decision, taking into account that all other products in the warehouse can be logically ruled out as they are not contained in the bin. Considering only this subset of products would require a re-computation of the standard confusion matrix. With the relative confusion matrix, no such re-computation is needed, which makes our approach more efficient. We show the usefulness of our approach in extensive experiments with a real bin picking robot, on simulated data, and on a publicly available image dataset.

The present paper deals with the application of atomic force microscopy (AFM) as a tool for morphological characterization of histological brain tumor samples. Data mining techniques will be applied for automatic identification of brain tumor tissues based on AFM images by means of classifying grade II and IV tumors. The rapid advancement of AFM in recent years turned it into a valuable and useful tool to determine the topography of surface nanoscale structures with high precision. Therefore, it is used in a variety of applications in life science, materials science, electrochemistry, polymer science, biophysics, nanotechnology, and biotechnology. Minkowski functionals are used (in particular the Euler- Poincaré characteristic) as a feature descriptor to characterize global geometric structures in images related to the topology of the AFM image. In order to improve classification accuracy on the one hand, but to infer interpretable information from AFM images for domain experts on the other hand, feature analysis and reduction will be applied. From a data mining point of view, Genetic Programming will be introduced as a sophisticated method for both feature analysis and reduction as well as for producing highly accurate and interpretable models. Support Vector Machines will be used for comparison reasons when talking about reachable model accuracy.