Proceedings of the 2020 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

In 2020 fand der jährliche Workshop des Faunhofer IOSB und the Lehrstuhls für interaktive Echtzeitsysteme statt. Vom 27. bis zum 31. Juli trugen die Doktorranden der beiden Institute über den Stand ihrer Forschung vor in Themen wie KI, maschinellen Lernen, computer vision, usage control, Metrologie vor. Die Ergebnisse dieser Vorträge sind in diesem Band als technische Berichte gesammelt

Transformer-based Fine-Grained Fungi Classification in an Open-Set Scenario

Fine-grained fungi classification describes the task of estimating the species of a fungus. The FungiCLEF 2022 challenge started a competition for the best solution to solve this task in an open-set scenario. For our solution, we employ a modern transformer-based classification architecture, use a class-balanced training scheme to handle the class-imbalance and apply heavy data augmentation. We approach the open-set scenario by using the final confidence scores as an indicator for unknown species. With this classification model, we were able to achieve an F1 score of 80.6 and 77.5 on the challenge’s public and private test set, respectively. This resulted in achieving the 7th place in the FungiCLEF 2022 challenge. We provide code at https://github.com/wolfstefan/fungi-classification

Proceedings of the 2019 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

In 2019 fand wieder der jährliche Workshop des Fraunhofer IOSB und des Lehrstuhls für Interaktive Echtzeitsysteme des Karlsruher Insitut für Technologie statt. Die Doktoranden beider Institutionen präsentierten den Fortschritt ihrer Forschung in den Themen Maschinelles Lernen, Machine Vision, Messtechnik, Netzwerksicherheit und Usage Control. Die Ideen dieses Workshops sind in diesem Buch gesammelt in der Form technischer Berichte

Towards many-class classification of materials based on their spectral fingerprints

Hyperspectral sensors are becoming cheaper and more available to the public. It is reasonable to assume that in the near future they will become more and more ubiquitous. This gives rise to many interesting applications, for example identification of pharmaceutical products and classification of food stuffs. Such applications require a precise models of the underlying classes, but hand-crafting these models is not feasible. In this paper, we propose to instead learn the model from the data using machine learning techniques. We investigate the use of two popular methods: support vector machines and random forest classifiers. In contrast to similar approaches, we restrict ourselves to linear support vector machines. Furthermore, we train the classifiers by solving the primal, instead of dual optimization problem. Our experiments on a large dataset show that the support vector machine approach is superior to random forest in classification accuracy as well as training time

Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The Proceeding of the annual joint workshop of the Fraunhofer IOSB and the Vision and Fusion Laboratory (IES) 2018 of the KIT contain technical reports of the PhD-stundents on the status of their research. The discussed topics ranging from computer vision and optical metrology to network security and machine learning. This volume provides a comprehensive and up-to-date overview of the research program of the IES Laboratory and the Fraunhofer IOSB

Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

In August 2022, Fraunhofer IOSB and IES of KIT held a joint workshop in a Schwarzwaldhaus near Triberg. Doctoral students presented research reports and discussed various topics like computer vision, optical metrology, network security, usage control, and machine learning. This book compiles the workshop\u27s results and ideas, offering a comprehensive overview of the research program of IES and Fraunhofer IOSB

Open Set Logo Detection and Retrieval

Current logo retrieval research focuses on closed set scenarios. We argue that the logo domain is too large for this strategy and requires an open set approach. To foster research in this direction, a large-scale logo dataset, called Logos in the Wild, is collected and released to the public. A typical open set logo retrieval application is, for example, assessing the effectiveness of advertisement in sports event broadcasts. Given a query sample in shape of a logo image, the task is to find all further occurrences of this logo in a set of images or videos. Currently, common logo retrieval approaches are unsuitable for this task because of their closed world assumption. Thus, an open set logo retrieval method is proposed in this work which allows searching for previously unseen logos by a single query sample. A two stage concept with separate logo detection and comparison is proposed where both modules are based on task specific CNNs. If trained with the Logos in the Wild data, significant performance improvements are observed, especially compared with state-of-the-art closed set approaches.Comment: accepted at VISAPP 201

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports

Efficient Semantic Representation of Network Access Control Configuration for Ontology-based Security Analysis

Assessing countermeasures and the sufficiency of security-relevant configurations within networked system architectures is a very complex task. Even the configuration of single network access control (NAC) instances can be too complex to analyse manually. Therefore, model-based approaches have manifested themselves as a solution for computer-aided configuration analysis. Unfortunately, current approaches suffer from various issues like coping with configuration-language heterogeneity or the analysis of multiple NAC instances as one overall system configuration, which is the case for the maturity of analysis goals. In this paper, we show how deriving and modelling NAC configurations’ effects solves the majority of these issues by allowing generic and simplified security analysis and model extension. The paper further presents the underlying modelling strategy to create such configuration effect representations (hereafter referred to as effective configuration) and explains how analyses based on previous approaches can still be performed. Moreover, the linking between rule representations and effective configuration is demonstrated, which enables the tracing of issues, found in the effective configuration, back to specific rules. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserve