Smartspectrometer—embedded optical spectroscopy for applications in agriculture and industry

The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its interface is presented. The advantages of the SmartSpectrometer are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field

Predictive tracking with improved motion models for optical belt sorting

Optical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type

Feature-specific illumination patterns for automated visual inspection

The choice of an appropriate illumination design is one of the most important steps in creating successful machine vision systems for automated inspection tasks. In a popular technique, multiple inspection images are captured under angular-varying illumination directions over the hemisphere, which yields a set of images referred to as illumination series. However, most existing approaches are restricted in that they use rather simple and generic illumination patterns on the hemisphere. Furthermore, the spectrum of the illumination is assumed to be fixed and is not considered. In this paper, we present an illumination technique which reduces the effort of capturing a series of inspection images for individual reflectance features by using linear combinations of basis light patterns that vary in their directional and spectral radiance. The key idea is to encode linear functions for feature extraction as angular- and wavelength-dependent illumination patterns, and thereby to compute linear features from the scene's spectral reflectance field directly in the optical domain. Finally, we evaluate and verify the proposed illumination technique to the problem of optical material type classification of printed circuit boards (PCBs)

Acquisition and evaluation of illumination series for unsupervised defect detection

Analyzing scenes under variable illumination has been an important and widely studied research area in the field of machine vision. In this article, we present an illumination device for capturing image series of small objects under variable illumination directions. Due to using a digital projector as programmable light source and a parabolic reflector to reflect the emitted illumination patterns, the device dispenses with the need of moving parts. Furthermore, we demonstrate the utility of illumination series for unsupervised surface defect detection by applying statistical anomaly detection to the measured reflectance data. To this end, we show how relevant illumination directions can be determined without using labeled information by a clustering-based approach

Ein objektangepasstes Beleuchtungsverfahren für die automatische Sichtprüfung

In fast allen Anwendungsbereichen der automatischen Sichtprüfung hat die Wahl der Beleuchtung für die Bildaufnahme einen entscheidenden Einfluss auf die Zuverlässigkeit der nachfolgenden Bildverarbeitung und -auswertung. Viele Inspektionsaufgaben haben zum Ziel, Abweichungen von einem zuvor definierten Sollzustand zu erkennen und zu detektieren. In diesem Artikel wird ein objektangepasstes Beleuchtungsverfahren vorgestellt, das eine optische Änderungsdetektion realisiert und somit Abweichungen von einem definierten Sollzustand direkt im Inspektionsbild ohne weiter Bildverarbeitungsoperationen sichtbar macht. Ein Vergleich des Verfahrens mit herkömmlicher Differenzbildbildung zur Änderungsdetektion zeigt, dass unter plausiblen Annahmen das vorgestellte objektangepasste Beleuchtungsverfahren zu einem besseren Signal-Rauschabstand führt

Pareto analysis of evolutionary and learning systems

This paper attempts to argue that most adaptive systems, such as evolutionary or learning systems, have inherently multiple objectives to deal with. Very often, there is no single solution that can optimize all the objectives. In this case, the concept of Pareto optimality is key to analyzing these systems. To support this argument, we first present an example that considers the robustness and evolvability trade-off in a redundant genetic representation for simulated evolution. It is well known that robustness is critical for biological evolution, since without a sufficient degree of mutational robustness, it is impossible for evolution to create new functionalities. On the other hand, the genetic representation should also provide the chance to find new phenotypes, i.e., the ability to innovate. This example shows quantitatively that a trade-off between robustness and innovation does exist in the studied redundant representation. Interesting results will also be given to show that new insights into learning problems can be gained when the concept of Pareto optimality is applied to machine learning. In the first example, a Pareto-based multi-objective approach is employed to alleviate catastrophic forgetting in neural network learning. We show that learning new information and memorizing learned knowledge are two conflicting objectives, and a major part of both information can be memorized when the multi-objective learning approach is adopted. In the second example, we demonstrate that a Pareto-based approach can address neural network regularizationmore elegantly. By analyzing the Pareto-optimal solutions, it is possible to identifying interesting solutions on the Pareto front