Dr.-Ing.

Gruna, Robin

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  • Publication
    Detecting Tar Contaminated Samples in Road-rubble using Hyperspectral Imaging and Texture Analysis
    ( 2023)
    Bäcker, Paul
    ;
    Maier, Georg
    ;
    Gruna, Robin
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    Längle, Thomas
    ;
    Beyerer, Jürgen
    Polycyclic aromatic hydrocarbons (PAH) containing tar-mixtures pose a challenge for recycling road rubble, as the tar containing elements have to be extracted and decontaminated for recycling. In this preliminary study, tar, bitumen and minerals are discriminated using a combination of color (RGB) and Hyperspectral Short Wave Infrared (SWIR) cameras. Further, the use of an autoencoder for detecting minerals embedded inside tar- and bitumen mixtures is proposed. Features are extracted from the spectra of the SWIR camera and the texture of the RGB images. For classification, linear discriminant analysis combined with a k-nearest neighbor classification is used. First results show a reliable detection of minerals and positive signs for separability of tar and bitumen. This work is a foundation for developing a sensor-based sorting system for physical separation of tar contaminated samples in road rubble.
  • Publication
    Machine learning-based multiobject tracking for sensor-based sorting
    ( 2022)
    Maier, Georg
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    Reith-Braun, Marcel
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    Bauer, Albert
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    Gruna, Robin
    ;
    Pfaff, Florian
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    Kruggel-Emden, Harald
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    Längle, Thomas
    ;
    Hanebeck, Uwe
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    Beyerer, Jürgen
  • Publication
    Automatic visual inspection based on trajectory data
    ( 2019)
    Maier, Georg
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    Mürdter, N.
    ;
    Gruna, Robin
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    Längle, Thomas
    ;
    Beyerer, Jürgen
    Automatic inspection tasks have successfully been implemented in several industrial fields and are of growing importance. Visual inspection using optical sensors is wide spread due to the vast variety of different sensors, observable features and comparatively low prices. It seems obvious that corresponding systems are blind towards mechanical features and inspection of those typically requires highly specialized, inflexible and costly systems. Recently, we have shown in the context of sensor-based sorting that tracking objects over a time period allows deriving motion-based features which potentially enable discrimination of optically identical objects, although an optical sensor is used. In this paper, we take one step back from the specific application and study the classification of test objects based on their trajectories. The objects are observed while receiving a certain impulse. We further refrain from manually designing features but use raw coordinates as extracted from a series of images. The success of the method is demonstrated by discriminating spheres made of similar plastic types while bouncing off a plane.
  • Publication
    Application of area-scan sensors in sensor-based sorting
    ( 2018)
    Maier, Georg
    ;
    Pfaff, F.
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    Pieper, C.
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    Gruna, Robin
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    Noack, B.
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    Kruggel-Emden, H.
    ;
    Längle, Thomas
    ;
    Hanebeck, U.D.
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    Wirtz, S.
    ;
    Scherer, V.
    ;
    Beyerer, Jürgen
    In the field of machine vision, sensor-based sorting is an important real-time application that enables the separation of a material feed into different classes. While state-of-the-art systems utilize scanning sensors such as line-scan cameras, advances in sensor technology have made application of area scanning sensors feasible. Provided a sufficiently high frame rate, objects can be observed at multiple points in time. By applying multiobject tracking, information about the objects contained in the material stream can be fused over time. Based on this information, our approach further allows predicting the position of each object for future points in time. While conventional systems typically apply a global, rather simple motion model, our approach includes an individual motion model for each object, which in turn allows estimating the point in time as well as the position when reaching the separation stage. In this contribution, we present results from our collaborative research project and summarize the present advances by discussing the potential of the application of area-scan sensors for sensor-based sorting. Among others, we introduce our simulation-driven approach and present results for physical separation efficiency for simulation-generated data, demonstrate the potential of using motion-based features for material classification and discuss real-time related challenges.
  • Publication
    Improving material characterization in sensor-based sorting by utilizing motion information
    ( 2017)
    Maier, Georg
    ;
    Pfaff, F.
    ;
    Becker, F.
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    Pieper, C.
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    Gruna, Robin
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    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Längle, Thomas
    ;
    Hanebeck, U.D.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Beyerer, Jürgen
    Sensor-based sorting provides state-of-the-art solutions for sorting of cohesive, granular materials. Systems are tailored to a task at hand, for instance by means of sensors and implementation of data analysis. Conventional systems utilize scanning sensors which do not allow for extraction of motion related information of objects contained in a material feed. Recently, usage of area-scan cameras to overcome this disadvantage has been proposed. Multitarget tracking can then be used in order to accurately estimate the point in time and position at which any object will reach the separation stage. In this paper, utilizing motion information of objects which can be retrieved from multitarget tracking for the purpose of classification is proposed. Results show that corresponding features can significantly increase classification performance and eventually decrease the detection error of a sorting system.
  • Publication
    Improving multitarget tracking using orientation estimates for sorting bulk materials
    ( 2017)
    Pfaff, F.
    ;
    Kurz, G.
    ;
    Pieper, C.
    ;
    Maier, Georg
    ;
    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Gruna, Robin
    ;
    Hanebeck, U.D.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Optical belt sorters can be used to sort a large variety of bulk materials. By the use of sophisticated algorithms, the performance of the complex machinery can be further improved. Recently, we have proposed an extension to industrial optical belt sorters that involves tracking the individual particles on the belt using an area scan camera. If the estimated behavior of the particles matches the true behavior, the reliability of the separation process can be improved. The approach relies on multitarget tracking using hard association decisions between the tracks and the measurements. In this paper, we propose to include the orientation in the assessment of the compatibility of a track and a measurement. This allows us to achieve more reliable associations, facilitating a higher accuracy of the tracking results.
  • Publication
    Evaluation and comparison of different approaches to multi-product brix calibration in near-infrared spectroscopy
    ( 2017)
    Kopf, M.
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    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Near-infrared (NIR) spectroscopy became a widespread technology for qualitative and quantitative material analysis. New fields of application of this technology, e.g., quantitative food analysis for consumers, increase demand for multiproduct calibration models. Conventional multivariate calibration methods, such as partial least squares regression (PLSR), are reported to show weakness in predictive performance [1]. Preliminary studies in multi-product calibration for quantitative analysis of food with near-infrared spectroscopy showed good results for memory-based learning (MBL) and a classification prediction hierarchy (CPH) [2]. In this study, three varieties of apples, pears and tomatoes with known °brix value are analyzed with NIR spectroscopy in the range from 900 nm to 2400 nm. Predictive performance of a linear PLSR model, two nonlinear models (CPH and MBL) and different preprocessing techniques are tested and evaluated. For error estimation, leave-oneproduct-out and leave-one-out cross-validation are used.
  • Publication
    Fast multitarget tracking via strategy switching for sensor-based sorting
    ( 2016)
    Maier, Georg
    ;
    Pfaff, F.
    ;
    Pieper, C.
    ;
    Gruna, Robin
    ;
    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Längle, Thomas
    ;
    Hanebeck, U.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Beyerer, Jürgen
    State-of-the-art sensor-based sorting systems provide solutions to sort various products according to quality aspects. Such systems face the challenge of an existing delay between perception and separation of the material. To reliably predict an object's position when reaching the separation stage, information regarding its movement needs to be derived. Multitarget tracking offers approaches through which this can be achieved. However, processing time is typically limited since the sorting decision for each object needs to be derived sufficiently early before it reaches the separation stage. In this paper, an approach for multitarget tracking in sensor-based sorting is proposed which supports establishing an upper bound regarding processing time required for solving the measurement to track association problem. To demonstrate the success of the proposed method, experiments are conducted for datasets obtained via simulation of a sorting system. This way, it is possible to not only demonstrate the impact on required runtime but also on the quality of the association.
  • Publication
    Simulation-based evaluation of predictive tracking for sorting bulk materials
    ( 2016)
    Pfaff, F.
    ;
    Pieper, C.
    ;
    Maier, Georg
    ;
    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Gruna, Robin
    ;
    Hanebeck, U.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Multitarget tracking problems arise in many realworld applications. The performance of the utilized algorithm strongly depends both on how the data association problem is handled and on the suitability of the motion models employed. Especially the motion models can be hard to validate. Previously, we have proposed to use multitarget tracking to improve optical belt sorters. In this paper, we evaluate both the suitability of our model and the tracking and then of our entire system incorporating the image processing component via the use of highly realistic numerical simulations. We first assess the model using noise-free measurements generated by the simulation and then evaluate the entire system by using synthetically generated image data.
  • Publication
    Simulation of an inverse schlieren image acquisition system for inspecting transparent objects
    ( 2016)
    Meyer, J.
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    This paper describes a novel approach for inspecting transparent objects. Since the underlying optical setup is based on a schlieren setup, any defect can be detected that leads to the deflection or extinction of incident light rays. By capturing a light field image of a defect-free transparent object and by illuminating objects under test with that very light field, the proposed inspection system can visualize defects by acquiring a single inspection image only. In order to evaluate the presented approach, a physically based rendering framework is used. It is extended by models and implementations of the necessary emitter and sensor plugins. Simulation experiments with virtual scenes that consist of a double-convex lens affected by different types of defects are the basis for a qualitative evaluation of the proposed method. The results show that a single image acquired with the described method is sufficient to test transparent objects for defects that cause the deflection or extinction of rays, e.g., enclosed absorbing or scattering impurities, shape anomalies, differences of the index of refraction and 3D-misalignments.