--- _id: '12816' abstract: - lang: eng text: Medical images need annotations with high-level semantic descriptors, so that domain experts can search for the desired dataset among an enormous volume of visual media within a Medical Data Integration Center. This article introduces a processing pipeline for storing and annotating DICOM and PNG imaging data by applying Elasticsearch, S3 and Deep Learning technologies. The proposed method processes both DICOM and PNG images to generate annotations. These image annotations are indexed in Elasticsearch with the corresponding raw data paths, where they can be retrieved and analyzed. author: - first_name: Ka Yung full_name: Cheng, Ka Yung last_name: Cheng - first_name: Santiago full_name: Pazmino, Santiago last_name: Pazmino - first_name: Bjoern full_name: Bergh, Bjoern last_name: Bergh - first_name: Markus full_name: Lange-Hegermann, Markus id: '71761' last_name: Lange-Hegermann - first_name: Bjorn full_name: Schreiweis, Bjorn last_name: Schreiweis citation: ama: Cheng KY, Pazmino S, Bergh B, Lange-Hegermann M, Schreiweis B. An Image Retrieval Pipeline in a Medical Data Integration Center. Vol 310. IOS Press, Incorporated; 2024:1388-1389. doi:10.3233/SHTI231208 apa: Cheng, K. Y., Pazmino, S., Bergh, B., Lange-Hegermann, M., & Schreiweis, B. (2024). An Image Retrieval Pipeline in a Medical Data Integration Center. In 19th World Congress on Medical and Health Informatics (MEDINFO) (Vol. 310, pp. 1388–1389). IOS Press, Incorporated. https://doi.org/10.3233/SHTI231208 bjps: Cheng KY et al. (2024) An Image Retrieval Pipeline in a Medical Data Integration Center. IOS Press, Incorporated. chicago: Cheng, Ka Yung, Santiago Pazmino, Bjoern Bergh, Markus Lange-Hegermann, and Bjorn Schreiweis. An Image Retrieval Pipeline in a Medical Data Integration Center. 19th World Congress on Medical and Health Informatics (MEDINFO). Vol. 310. Studies in Health Technology and Informatics. IOS Press, Incorporated, 2024. https://doi.org/10.3233/SHTI231208. chicago-de: Cheng, Ka Yung, Santiago Pazmino, Bjoern Bergh, Markus Lange-Hegermann und Bjorn Schreiweis. 2024. An Image Retrieval Pipeline in a Medical Data Integration Center. 19th World Congress on Medical and Health Informatics (MEDINFO). Bd. 310. Studies in Health Technology and Informatics. IOS Press, Incorporated. doi:10.3233/SHTI231208, . din1505-2-1: 'Cheng, Ka Yung ; Pazmino, Santiago ; Bergh, Bjoern ; Lange-Hegermann, Markus ; Schreiweis, Bjorn: An Image Retrieval Pipeline in a Medical Data Integration Center., Studies in Health Technology and Informatics. Bd. 310 : IOS Press, Incorporated, 2024' havard: K.Y. Cheng, S. Pazmino, B. Bergh, M. Lange-Hegermann, B. Schreiweis, An Image Retrieval Pipeline in a Medical Data Integration Center., IOS Press, Incorporated, 2024. ieee: 'K. Y. Cheng, S. Pazmino, B. Bergh, M. Lange-Hegermann, and B. Schreiweis, An Image Retrieval Pipeline in a Medical Data Integration Center., vol. 310. IOS Press, Incorporated, 2024, pp. 1388–1389. doi: 10.3233/SHTI231208.' mla: Cheng, Ka Yung, et al. “An Image Retrieval Pipeline in a Medical Data Integration Center.” 19th World Congress on Medical and Health Informatics (MEDINFO), vol. 310, IOS Press, Incorporated, 2024, pp. 1388–89, https://doi.org/10.3233/SHTI231208. short: K.Y. Cheng, S. Pazmino, B. Bergh, M. Lange-Hegermann, B. Schreiweis, An Image Retrieval Pipeline in a Medical Data Integration Center., IOS Press, Incorporated, 2024. ufg: 'Cheng, Ka Yung u. a.: An Image Retrieval Pipeline in a Medical Data Integration Center., Bd. 310, o. O. 2024 (Studies in Health Technology and Informatics).' van: Cheng KY, Pazmino S, Bergh B, Lange-Hegermann M, Schreiweis B. An Image Retrieval Pipeline in a Medical Data Integration Center. 19th World Congress on Medical and Health Informatics (MEDINFO). IOS Press, Incorporated; 2024. (Studies in Health Technology and Informatics; vol. 310). conference: end_date: 2023-08-12 location: Sydney, AUSTRALIA name: 19th World Congress on Medical and Health Informatics (MEDINFO) start_date: 2023-08-08 date_created: 2025-04-17T08:25:27Z date_updated: 2025-06-25T13:05:17Z department: - _id: DEP5023 doi: 10.3233/SHTI231208 external_id: pmid: - '38269660' intvolume: ' 310' keyword: - Medical image retrieval - data lake - DICOM - deep learning - elasticsearch language: - iso: eng page: 1388-1389 pmid: '1' publication: 19th World Congress on Medical and Health Informatics (MEDINFO) publication_identifier: eisbn: - 978-1-64368-457-4 eissn: - 1879-8365 isbn: - 978-1-64368-456-7 issn: - 0926-9630 publication_status: published publisher: IOS Press, Incorporated series_title: Studies in Health Technology and Informatics status: public title: An Image Retrieval Pipeline in a Medical Data Integration Center. type: conference_speech user_id: '83781' volume: 310 year: '2024' ... --- _id: '12904' abstract: - lang: eng text: 'It is crucial to identify defective machine components in production to ensure quality. Some components generate heat when defective, so automating the inspection process with a thermal imaging camera can provide qualitative measurements. This work aims to use computer vision methods to locate these components in thermal images. Since there is currently no comparison of object detection and semantic segmentation algorithms for this use case, this study compares different architectures with the goal of localising these components for further defect inspection. Moreover, as there are currently no datasets for this use case, this study contributes a novel annotated dataset of thermal images of combine harvester components. The different algorithms are evaluated based on the quality of their predictions and their suitability for further defect inspection. As semantic segmentation and object detection cannot be directly compared with each other, custom weighted metrics are used. The architectures evaluated include RetinaNet, YOLOV8 Detector, DeepLabV3+, and SegFormer. Based on the experimental results, semantic segmentation outperforms object detection regarding the use case, and the SegFormer architecture achieves the best results with a weighted MeanIOU of 0.853. ' author: - first_name: Hanna full_name: Senke, Hanna id: '79810' last_name: Senke - first_name: Dennis full_name: Sprute, Dennis last_name: Sprute - first_name: Ulrich full_name: Büker, Ulrich id: '81453' last_name: Büker orcid: 0000-0002-4403-3889 - first_name: Holger full_name: Flatt, Holger id: '58494' last_name: Flatt citation: ama: Senke H, Sprute D, Büker U, Flatt H. Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images. (Längle T, Heizmann M, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik , Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung , eds.). KIT Scientific Publishing; 2024:71-82. doi:10.58895/ksp/1000174496-7 apa: Senke, H., Sprute, D., Büker, U., & Flatt, H. (2024). Deep learning-based localisation of combine harvester components in thermal images. In T. Längle, M. Heizmann, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik , & Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Eds.), Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024 (pp. 71–82). KIT Scientific Publishing. https://doi.org/10.58895/ksp/1000174496-7 bjps: 'Senke H et al. (2024) Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images, Längle T et al. (eds). Karlsruhe: KIT Scientific Publishing.' chicago: 'Senke, Hanna, Dennis Sprute, Ulrich Büker, and Holger Flatt. Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images. Edited by Thomas Längle, Michael Heizmann, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik , and Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung . Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024. Karlsruhe: KIT Scientific Publishing, 2024. https://doi.org/10.58895/ksp/1000174496-7.' chicago-de: 'Senke, Hanna, Dennis Sprute, Ulrich Büker und Holger Flatt. 2024. Deep learning-based localisation of combine harvester components in thermal images. Hg. von Thomas Längle, Michael Heizmann, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik , und Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung . Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024. Karlsruhe: KIT Scientific Publishing. doi:10.58895/ksp/1000174496-7, .' din1505-2-1: 'Senke, Hanna ; Sprute, Dennis ; Büker, Ulrich ; Flatt, Holger ; Längle, T. ; Heizmann, M. ; Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik ; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Hrsg.): Deep learning-based localisation of combine harvester components in thermal images. Karlsruhe : KIT Scientific Publishing, 2024' havard: H. Senke, D. Sprute, U. Büker, H. Flatt, Deep learning-based localisation of combine harvester components in thermal images, KIT Scientific Publishing, Karlsruhe, 2024. ieee: 'H. Senke, D. Sprute, U. Büker, and H. Flatt, Deep learning-based localisation of combine harvester components in thermal images. Karlsruhe: KIT Scientific Publishing, 2024, pp. 71–82. doi: 10.58895/ksp/1000174496-7.' mla: Senke, Hanna, et al. “Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images.” Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024, edited by Thomas Längle et al., KIT Scientific Publishing, 2024, pp. 71–82, https://doi.org/10.58895/ksp/1000174496-7. short: H. Senke, D. Sprute, U. Büker, H. Flatt, Deep Learning-Based Localisation of Combine Harvester Components in Thermal Images, KIT Scientific Publishing, Karlsruhe, 2024. ufg: 'Senke, Hanna u. a.: Deep learning-based localisation of combine harvester components in thermal images, hg. von Längle, Thomas u. a., Karlsruhe 2024.' van: 'Senke H, Sprute D, Büker U, Flatt H. Deep learning-based localisation of combine harvester components in thermal images. Längle T, Heizmann M, Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik , Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung , editors. Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024. Karlsruhe: KIT Scientific Publishing; 2024.' conference: end_date: 2024-11-22 location: Karlsruhe name: Forum Bildverarbeitung 2024 start_date: 2024-11-21 corporate_editor: - 'Karlsruher Institut für Technologie. Institut für Industrielle Informationstechnik ' - 'Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung ' date_created: 2025-05-08T14:01:20Z date_updated: 2025-05-12T07:33:48Z department: - _id: DEP5023 doi: 10.58895/ksp/1000174496-7 editor: - first_name: Thomas full_name: Längle, Thomas last_name: Längle - first_name: Michael full_name: Heizmann, Michael last_name: Heizmann keyword: - industrial quality assurance - deep learning architectures - object localisation - Thermal images language: - iso: eng page: 71-82 place: Karlsruhe publication: Forum Bildverarbeitung 2024 = Image Pocessing Forum 2024 publication_identifier: isbn: - 978-3-7315-1386-5 publication_status: published publisher: KIT Scientific Publishing quality_controlled: '1' status: public title: Deep learning-based localisation of combine harvester components in thermal images type: conference_editor_article user_id: '83781' year: '2024' ... --- _id: '7734' abstract: - lang: eng text: ' Der Konferenzbeitrag zeigt den Forschungs- und Technikstand bezüglich des Griff-in-die-Kiste auf. Basierend auf einer Literaturrecherche werden Beispiele für regelbasierte und lernende Verfahren vorgestellt. Anschließend erfolgt eine systematische Gegenüberstellung der Verfahren. Hierfür werden die Anforderungen, die ein Griff-in-die-Kiste-System zu erfüllen hat, dargelegt. Die Kriterien resultieren aus einer Expertenbefragung des produktionstechnischen Umfelds der Weidmüller Gruppe. Neben den Anforderungen werden die Gewichtungen zur Bildung einer Rangfolge ermittelt. Die erarbeiteten Anforderungen dienen anschließend zur Bewertung der regelbasierten und lernenden Verfahren. Die Analyse mündet in einer methodischen Lücke zwischen beiden Paradigmen und stellt die Ausgangsbasis für die weitere Arbeit zur Entwicklung des industriellen Griff-in-die-Kiste dar. Abschließend werden erste Arbeitsergebnisse zur Objekterkennung von Reihenklemmen veröffentlicht. In einer Untersuchung werden die Zuverlässigkeit, die Robustheit sowie die Einrichtdauer einer Objekterkennung mithilfe von Deep Learning ermittelt. Das angestrebte Forschungsergebnis stellt einen Entwicklungsschritt von automatisierten Systemen, die in einem definierten Wirkbereich eigenständig arbeiten, zu autonomen Systemen, die selbstständig auf zeitvariante Größen reagieren, dar.' author: - first_name: Tobias full_name: Stuke, Tobias id: '79141' last_name: Stuke - first_name: Thomas full_name: Bartsch, Thomas id: '43513' last_name: Bartsch - first_name: Thomas full_name: Rauschenbach, Thomas last_name: Rauschenbach citation: ama: Stuke T, Bartsch T, Rauschenbach T. Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie. 1st ed. (Härle C, Jäkel J, Sand G, Hochschule für Technik, Wirtschaft und Kultur Leipzig, eds.). Open Access; 2022:145-154. doi:https://doi.org/10.33968/2022.14 apa: 'Stuke, T., Bartsch, T., & Rauschenbach, T. (2022). Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie. In C. Härle, J. Jäkel, G. Sand, & Hochschule für Technik, Wirtschaft und Kultur Leipzig (Eds.), Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel (1st ed., pp. 145–154). Open Access. https://doi.org/10.33968/2022.14' bjps: 'Stuke T, Bartsch T and Rauschenbach T (2022) Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie, 1st ed., Härle C et al. (eds). Pforzheim: Open Access.' chicago: 'Stuke, Tobias, Thomas Bartsch, and Thomas Rauschenbach. Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie. Edited by Christian Härle, Jens Jäkel, Guido Sand, and Hochschule für Technik, Wirtschaft und Kultur Leipzig. Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel. 1st ed. Pforzheim: Open Access, 2022. https://doi.org/10.33968/2022.14.' chicago-de: 'Stuke, Tobias, Thomas Bartsch und Thomas Rauschenbach. 2022. Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie. Hg. von Christian Härle, Jens Jäkel, Guido Sand, und Hochschule für Technik, Wirtschaft und Kultur Leipzig. Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel. 1. Aufl. Pforzheim: Open Access. doi:https://doi.org/10.33968/2022.14, .' din1505-2-1: 'Stuke, Tobias ; Bartsch, Thomas ; Rauschenbach, Thomas ; Härle, C. ; Jäkel, J. ; Sand, G. ; Hochschule für Technik, Wirtschaft und Kultur Leipzig (Hrsg.): Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie. 1. Aufl. Pforzheim : Open Access, 2022' havard: T. Stuke, T. Bartsch, T. Rauschenbach, Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie, 1st ed., Open Access, Pforzheim, 2022. ieee: 'T. Stuke, T. Bartsch, and T. Rauschenbach, Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie, 1st ed. Pforzheim: Open Access, 2022, pp. 145–154. doi: https://doi.org/10.33968/2022.14.' mla: 'Stuke, Tobias, et al. “Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie.” Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel, edited by Christian Härle et al., 1st ed., Open Access, 2022, pp. 145–54, https://doi.org/10.33968/2022.14.' short: T. Stuke, T. Bartsch, T. Rauschenbach, Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie, 1st ed., Open Access, Pforzheim, 2022. ufg: 'Stuke, Tobias/Bartsch, Thomas/Rauschenbach, Thomas: Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie, hg. von Härle, Christian u. a., Pforzheim 12022.' van: 'Stuke T, Bartsch T, Rauschenbach T. Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie. 1st ed. Härle C, Jäkel J, Sand G, Hochschule für Technik, Wirtschaft und Kultur Leipzig, editors. Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel. Pforzheim: Open Access; 2022.' conference: end_date: 2022-03-11 location: Pforzheim name: 18. Konferenz für Angewandte Auto­mati­sierungs­technik in Lehre und Entwicklung an Hochschulen (AALE) start_date: 2022-03-09 corporate_editor: - Hochschule für Technik, Wirtschaft und Kultur Leipzig date_created: 2022-04-22T11:44:38Z date_updated: 2024-08-08T13:55:46Z department: - _id: DEP7015 doi: https://doi.org/10.33968/2022.14 edition: '1' editor: - first_name: Christian full_name: Härle, Christian last_name: Härle - first_name: Jens full_name: Jäkel, Jens last_name: Jäkel - first_name: Guido full_name: Sand, Guido last_name: Sand keyword: - Griff-in-die-Kiste - Bildverarbeitung - Robotik - Deep Learning - lernende Verfahren - regelbasierte Verfahren language: - iso: ger page: 145 – 154 place: Pforzheim publication: 'Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel' publication_identifier: unknown: - 978-3-910103-00-9 publication_status: published publisher: Open Access status: public title: Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie type: conference_editor_article user_id: '83781' year: '2022' ... --- _id: '8888' abstract: - lang: ger text: "Diese Arbeit handelt von der Frage, wie Tonaufnahmen-basierte Lernprozesse im Learning Management System der Hochschule für Musik Detmold, Moodle, erweitert werden können. Dazu werden LMS zunächst definiert und anschließend in die Bildungslandschaft eingeordnet. Daraufhin wird der Status Quo betrachtet mit der Feststellung, dass ein Bedarf an Werkzeugen besteht. Dieser Bedarf wurde durch die Programmierung zweier Anwendungen adressiert, die eine Integration im LMS ermöglichen und damit zu einer erhöhten Nutzbarkeit von Tonaufnahmen und musikalischen Inhalten führen sollen. Zum einen ist das eine Implementation des DTW Algorithmus, mittels welchem sich Synchronisationsdaten zwischen zwei verschiedenen Musikdarstellungen desselben Stückes berechnen lassen. Damit ließe sich bspw. ein Interface erstellen, auf dem die Anzeige der Musikwiedergabe mit der Anzeige einer Notenpartitur synchronisiert wird. Die zweite Anwendung fällt in den Bereich des maschinellen Lernens – es wurde ein automatischer Instrumentenklassifizierer geschrieben. Dieser eignet sich zur Erstellung von automatischen Taggings, zwecks Organisation von Daten und Gehörübungen. Die Nutzung einer CNN-Architektur hat sich dabei als effektiv erwiesen: Nach insgesamt 39 Lernepochen und knapp 7 Millionen gelernten Parametern konnte eine Genauigkeit von 95% erzielt werden. Als Datensatz diente die frei verfügbare Aufnahmensammlung des britischen Philharmonia Orchesters (vgl. Thorben Dittes). \r\nIm zweiten Kapitel soll ein Abstecken der Zwecke der einzelnen Programme die Designentscheidungen informieren, welche daraufhin erläutert werden. Im dritten Teil wird anschließend mit ScoreTube eine DTW Implementation von Berndt et al. zum Vergleich herangezogen, um die vorliegende Arbeit in den aktuellen Diskurs einzuordnen. Der Beitrag endet mit einer Evaluation der Ergebnisse und einem Ausblick auf potenzielle zukünftige Arbeiten." author: - first_name: Dennis full_name: Treiber, Dennis id: '72911' last_name: Treiber citation: ama: 'Treiber D. Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Technische Hochschule Ostwestfalen-Lippe; 2022.' apa: 'Treiber, D. (2022). Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Technische Hochschule Ostwestfalen-Lippe.' bjps: 'Treiber D (2022) Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Detmold: Technische Hochschule Ostwestfalen-Lippe.' chicago: 'Treiber, Dennis. Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Detmold: Technische Hochschule Ostwestfalen-Lippe, 2022.' chicago-de: 'Treiber, Dennis. 2022. Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Detmold: Technische Hochschule Ostwestfalen-Lippe.' din1505-2-1: 'Treiber, Dennis: Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Detmold : Technische Hochschule Ostwestfalen-Lippe, 2022' havard: 'D. Treiber, Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen., Technische Hochschule Ostwestfalen-Lippe, Detmold, 2022.' ieee: 'D. Treiber, Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Detmold: Technische Hochschule Ostwestfalen-Lippe, 2022.' mla: 'Treiber, Dennis. Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Technische Hochschule Ostwestfalen-Lippe, 2022.' short: 'D. Treiber, Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen., Technische Hochschule Ostwestfalen-Lippe, Detmold, 2022.' ufg: 'Treiber, Dennis: Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen., Detmold 2022.' van: 'Treiber D. Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen. Detmold: Technische Hochschule Ostwestfalen-Lippe; 2022. 53 p.' date_created: 2022-09-07T09:31:21Z date_updated: 2023-03-15T13:50:16Z ddc: - '004' defense_date: 2022-08-31 department: - _id: DEP2001 file: - access_level: open_access content_type: application/pdf creator: 5r2-ybz date_created: 2022-09-07T09:25:33Z date_updated: 2022-09-07T09:25:33Z file_id: '8889' file_name: BA - Verwendung von Tonaufnahmen im LMS - Dennis Treiber.pdf file_size: 1302756 relation: main_file title: Die Verwendung von Tonaufnahmen im LMS file_date_updated: 2022-09-07T09:25:33Z has_accepted_license: '1' jel: - C61 keyword: - learning management system - dynamic time warping - deep learning - convolutional neural network language: - iso: ger oa: '1' page: '53' place: Detmold publication_status: published publisher: Technische Hochschule Ostwestfalen-Lippe status: public supervisor: - first_name: Aristotelis full_name: Hadjakos, Aristotelis id: '58704' last_name: Hadjakos - first_name: Guido full_name: Falkemeier, Guido id: '29084' last_name: Falkemeier title: 'Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen.' type: bachelor_thesis user_id: '15514' year: 2022 ... --- _id: '4097' abstract: - lang: eng text: The capabilities of object detection are well known, but many projects don’t use them, despite potential benefit. Even though the use of object detection algorithms is facilitated through frameworks and publications, a big issue is the creation of the necessary training data. To tackle this issue, this work shows the design and evaluation of a prototype, which allows users to create synthetic datasets for object detection in images. The prototype is evaluated using YOLOv3 as the underlying detector and shows that the generated datasets are equally good in quality as manually created data. This encourages a wide adoption of object detection algorithms in different areas, since image creation and labeling is often the most time consuming step. author: - first_name: Andreas full_name: Besginow, Andreas id: '61743' last_name: Besginow - first_name: Sebastian full_name: Büttner, Sebastian id: '61868' last_name: Büttner - first_name: Carsten full_name: Röcker, Carsten id: '61525' last_name: Röcker citation: ama: 'Besginow A, Büttner S, Röcker C. Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd International Conference on Human-Computer Interaction. Vol 12203. Lecture Notes in Computer Science . Springer; 2020:178-192. doi:https://doi.org/10.1007/978-3-030-50344-4_14' apa: Besginow, A., Büttner, S., & Röcker, C. (2020). Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. 22nd International Conference on Human-Computer Interaction, 12203, 178–192. https://doi.org/10.1007/978-3-030-50344-4_14 bjps: 'Besginow A, Büttner S and Röcker C (2020) Making Object Detection Available to Everyone - A Hardware Prototype for Semi-Automatic Synthetic Data Generation. 22nd International Conference on Human-Computer Interaction, vol. 12203. Berlin: Springer, pp. 178–192.' chicago: 'Besginow, Andreas, Sebastian Büttner, and Carsten Röcker. “Making Object Detection Available to Everyone - A Hardware Prototype for Semi-Automatic Synthetic Data Generation.” In 22nd International Conference on Human-Computer Interaction, 12203:178–92. Lecture Notes in Computer Science . Berlin: Springer, 2020. https://doi.org/10.1007/978-3-030-50344-4_14.' chicago-de: 'Besginow, Andreas, Sebastian Büttner und Carsten Röcker. 2020. Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd International Conference on Human-Computer Interaction, 12203:178–192. Lecture Notes in Computer Science . Berlin: Springer. doi:https://doi.org/10.1007/978-3-030-50344-4_14, .' din1505-2-1: 'Besginow, Andreas ; Büttner, Sebastian ; Röcker, Carsten: Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd International Conference on Human-Computer Interaction, Lecture Notes in Computer Science . Bd. 12203. Berlin : Springer, 2020, S. 178–192' havard: 'A. Besginow, S. Büttner, C. Röcker, Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation, in: 22nd International Conference on Human-Computer Interaction, Springer, Berlin, 2020: pp. 178–192.' ieee: 'A. Besginow, S. Büttner, and C. Röcker, “Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation,” in 22nd International Conference on Human-Computer Interaction, Copenhagen, Denmark, 2020, vol. 12203, pp. 178–192. doi: https://doi.org/10.1007/978-3-030-50344-4_14.' mla: Besginow, Andreas, et al. “Making Object Detection Available to Everyone - A Hardware Prototype for Semi-Automatic Synthetic Data Generation.” 22nd International Conference on Human-Computer Interaction, vol. 12203, Springer, 2020, pp. 178–92, https://doi.org/10.1007/978-3-030-50344-4_14. short: 'A. Besginow, S. Büttner, C. Röcker, in: 22nd International Conference on Human-Computer Interaction, Springer, Berlin, 2020, pp. 178–192.' ufg: 'Besginow, Andreas/Büttner, Sebastian/Röcker, Carsten: Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation, in: o. Hg.: 22nd International Conference on Human-Computer Interaction, Bd. 12203, Berlin 2020 (Lecture Notes in Computer Science ),  S. 178–192.' van: 'Besginow A, Büttner S, Röcker C. Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd International Conference on Human-Computer Interaction. Berlin: Springer; 2020. p. 178–92. (Lecture Notes in Computer Science ; vol. 12203).' conference: end_date: 2020-07-24 location: Copenhagen, Denmark name: 22nd International Conference on Human-Computer Interaction start_date: 2020-07-19 date_created: 2020-11-26T14:10:04Z date_updated: 2025-06-26T13:28:35Z department: - _id: DEP5023 doi: https://doi.org/10.1007/978-3-030-50344-4_14 intvolume: ' 12203' keyword: - Object detection - Synthetic datasets - Machine learning - Deep learning language: - iso: eng main_file_link: - open_access: '1' url: https://link.springer.com/chapter/10.1007/978-3-030-50344-4_14 oa: '1' page: 178-192 place: Berlin publication: 22nd International Conference on Human-Computer Interaction publication_identifier: eisbn: - 978-3-030-50344-4 isbn: - 978-3-030-50343-7 publication_status: published publisher: Springer series_title: 'Lecture Notes in Computer Science ' status: public title: Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation type: conference user_id: '83781' volume: 12203 year: '2020' ... --- _id: '12807' abstract: - lang: eng text: Writing chorales in the style of Bach has been a music theory exercise for generations of music students. As such it is not surprising that automatic Bach chorale harmonization has been a topic in music technology for decades. We suggest several improvements to current neural network solutions based on musicological insights into human choral composition practices. Evaluations with expert listeners show that the generated chorales closely resemble Bach's harmonization style. author: - first_name: Alexander full_name: Leemhuis, Alexander last_name: Leemhuis - first_name: Simon full_name: Waloschek, Simon last_name: Waloschek - first_name: Aristotelis full_name: Hadjakos, Aristotelis id: '58704' last_name: Hadjakos citation: ama: Leemhuis A, Waloschek S, Hadjakos A. Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization. Vol 1168. (Cellier P, Driessens K, eds.). Springer International Publishing; 2020:462-469. doi:10.1007/978-3-030-43887-6_39 apa: 'Leemhuis, A., Waloschek, S., & Hadjakos, A. (2020). Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization. In P. Cellier & K. Driessens (Eds.), Machine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019 (Vol. 1168, pp. 462–469). Springer International Publishing. https://doi.org/10.1007/978-3-030-43887-6_39' bjps: 'Leemhuis A, Waloschek S and Hadjakos A (2020) Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization, Cellier P and Driessens K (eds). Cham: Springer International Publishing.' chicago: 'Leemhuis, Alexander, Simon Waloschek, and Aristotelis Hadjakos. Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization. Edited by Peggy Cellier and Kurt Driessens. Machine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019. Vol. 1168. Communications in Computer and Information Science . Cham: Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-43887-6_39.' chicago-de: 'Leemhuis, Alexander, Simon Waloschek und Aristotelis Hadjakos. 2020. Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization. Hg. von Peggy Cellier und Kurt Driessens. Machine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019. Bd. 1168. Communications in Computer and Information Science . Cham: Springer International Publishing. doi:10.1007/978-3-030-43887-6_39, .' din1505-2-1: 'Leemhuis, Alexander ; Waloschek, Simon ; Hadjakos, Aristotelis ; Cellier, P. ; Driessens, K. (Hrsg.): Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization, Communications in Computer and Information Science . Bd. 1168. Cham : Springer International Publishing, 2020' havard: A. Leemhuis, S. Waloschek, A. Hadjakos, Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization, Springer International Publishing, Cham, 2020. ieee: 'A. Leemhuis, S. Waloschek, and A. Hadjakos, Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization, vol. 1168. Cham: Springer International Publishing, 2020, pp. 462–469. doi: 10.1007/978-3-030-43887-6_39.' mla: 'Leemhuis, Alexander, et al. “Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization.” Machine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019, edited by Peggy Cellier and Kurt Driessens, vol. 1168, Springer International Publishing, 2020, pp. 462–69, https://doi.org/10.1007/978-3-030-43887-6_39.' short: A. Leemhuis, S. Waloschek, A. Hadjakos, Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization, Springer International Publishing, Cham, 2020. ufg: 'Leemhuis, Alexander/Waloschek, Simon/Hadjakos, Aristotelis: Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization, Bd. 1168, hg. von Cellier, Peggy/Driessens, Kurt, Cham 2020 (Communications in Computer and Information Science ).' van: 'Leemhuis A, Waloschek S, Hadjakos A. Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization. Cellier P, Driessens K, editors. Machine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019. Cham: Springer International Publishing; 2020. (Communications in Computer and Information Science ; vol. 1168).' conference: end_date: 2019-09-20 location: Würzburg name: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) start_date: 2019-09-16 date_created: 2025-04-16T07:52:39Z date_updated: 2025-06-26T13:36:14Z department: - _id: DEP2000 doi: 10.1007/978-3-030-43887-6_39 editor: - first_name: Peggy full_name: Cellier, Peggy last_name: Cellier - first_name: Kurt full_name: Driessens, Kurt last_name: Driessens intvolume: ' 1168' keyword: - Bach chorale harmonization - Deep learning - Beam search language: - iso: eng page: 462–469 place: Cham publication: 'Machine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019' publication_identifier: eisbn: - 978-3-030-43887-6 eissn: - 1865-0937 isbn: - 978-3-030-43886-9 issn: - 1865-0929 publication_status: published publisher: Springer International Publishing series_title: 'Communications in Computer and Information Science ' status: public title: Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization type: conference_editor_article user_id: '83781' volume: 1168 year: '2020' ... --- _id: '4102' abstract: - lang: eng text: Complexity is a fundamental part of product design and manufacturing today, owing to increased demands for customization and advances in digital design techniques. Assembling and repairing such an enormous variety of components means that workers are cognitively challenged, take longer to search for the relevant information and are prone to making mistakes. Although in recent years deep learning approaches to object recognition have seen rapid advances, the combined potential of deep learning and augmented reality in the industrial domain remains relatively under explored. In this paper we introduce AR-ProMO, a combined hardware/software solution that provides a generalizable assistance system for identifying mistakes during product assembly and repair. author: - first_name: Hitesh full_name: Dhiman, Hitesh id: '71767' last_name: Dhiman - first_name: Sebastian full_name: Büttner, Sebastian id: '61868' last_name: Büttner - first_name: Carsten full_name: Röcker, Carsten id: '61525' last_name: Röcker - first_name: Raphael full_name: Reisch, Raphael last_name: Reisch citation: ama: 'Dhiman H, Büttner S, Röcker C, Reisch R. Handling Work Complexity with AR/Deep Learning. In: Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia. ACM; 2019:518–522. doi:10.1145/3369457.3370919' apa: 'Dhiman, H., Büttner, S., Röcker, C., & Reisch, R. (2019). Handling Work Complexity with AR/Deep Learning. In Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia (pp. 518–522). Perth/Fremantle, WA, Australia: ACM. https://doi.org/10.1145/3369457.3370919' bjps: 'Dhiman H et al. (2019) Handling Work Complexity with AR/Deep Learning. Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia. ACM, pp. 518–522.' chicago: 'Dhiman, Hitesh, Sebastian Büttner, Carsten Röcker, and Raphael Reisch. “Handling Work Complexity with AR/Deep Learning.” In Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia, 518–522. ACM, 2019. https://doi.org/10.1145/3369457.3370919.' chicago-de: 'Dhiman, Hitesh, Sebastian Büttner, Carsten Röcker und Raphael Reisch. 2019. Handling Work Complexity with AR/Deep Learning. In: Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia, 518–522. ACM. doi:10.1145/3369457.3370919, .' din1505-2-1: 'Dhiman, Hitesh ; Büttner, Sebastian ; Röcker, Carsten ; Reisch, Raphael: Handling Work Complexity with AR/Deep Learning. In: Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia : ACM, 2019, S. 518–522' havard: 'H. Dhiman, S. Büttner, C. Röcker, R. Reisch, Handling Work Complexity with AR/Deep Learning, in: Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia, ACM, 2019: pp. 518–522.' ieee: 'H. Dhiman, S. Büttner, C. Röcker, and R. Reisch, “Handling Work Complexity with AR/Deep Learning,” in Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia, Perth/Fremantle, WA, Australia, 2019, pp. 518–522.' mla: 'Dhiman, Hitesh, et al. “Handling Work Complexity with AR/Deep Learning.” Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia, ACM, 2019, pp. 518–522, doi:10.1145/3369457.3370919.' short: 'H. Dhiman, S. Büttner, C. Röcker, R. Reisch, in: Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia, ACM, 2019, pp. 518–522.' ufg: 'Dhiman, Hitesh et. al. (2019): Handling Work Complexity with AR/Deep Learning, in: Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia, S. 518–522.' van: 'Dhiman H, Büttner S, Röcker C, Reisch R. Handling Work Complexity with AR/Deep Learning. In: Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI’19) : 2nd Dec-5th Dec 2019, Perth/Fremantle, WA, Australia. ACM; 2019. p. 518–522.' conference: end_date: 2019-12-05 location: Perth/Fremantle, WA, Australia name: '31st Australian Conference on Human-Computer-Interaction (OzCHI''19) ' start_date: 201912-02 date_created: 2020-11-27T10:22:40Z date_updated: 2023-03-15T13:49:50Z department: - _id: DEP5023 doi: 10.1145/3369457.3370919 keyword: - Augmented Reality - Deep Learning language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1145/3369457.3370919 oa: '1' page: ' 518–522' publication: 'Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI''19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia' publication_identifier: isbn: - 978-1-4503-7696-9 publication_status: published publisher: ACM status: public title: Handling Work Complexity with AR/Deep Learning type: conference user_id: '15514' year: 2019 ...