Anmeldung via TISS. Erstbesprechung: 2. März 09:00 mit der VO Übungsablauf TeilnehmerInnen lösen individuelle Aufgaben. Es gibt keinen regelmäßigen Übungstermin, der LVA-Leiter steht aber für individuelle Terminvereinbarungen zur Verfügung (Beantwortung von Fragen, allgemeine Beratung, Besprechung des Programm-Codes usw.). Es gibt jedoch einen Termin zur Präsentation der Aufgabenstellung, einen Termin zur Zwischenpräsentation und einen für die … Continue reading Scene Understanding and Surveillance (UE) →
Vorlesung: Mittwoch, 1. 3.2023, 09.00-10.00 Uhr c.t. (Vormals: Video Analysis) Ort: Seminarraum FAV 01 A (Seminarraum 183/2) Beginn: 1.3.2023 (Vorbesprechung der VO und LU) Weitere Informationen: TISS und TUWEL In dieser LVA wird die Analyse von Bildfolgen (Videos, Tiefen- und Thermaldaten) vertiefend behandelt: dazu gehören die Themengebiete Sensorik, Bewegungserkennung, Objektverfolgung, Szenen-Rekonstruktion und Performance Evaluation. Fragestellungen … Continue reading Scene Understanding and Surveillance →
The enhancement of document images describes the task of improving the visual quality of document images. Thus, degradations (like background clutter or uneven illumination) can be removed. Enhancement can be used as a preprocessing step for further document image analysis methods – including text recognition. Goal The aim of this internship is to select an … Continue reading Document image enhancement →
An often used preprocessing step in text recognition systems is text line detection. The aim of this internship is to implement an existing method for text line detection. Goal Implement an existing method, which is introduced in [1]. Compare the results of your implementation with results gained in the original work. Workflow Literature review Data … Continue reading Text Line Detection →
Offline Handwritten Text Recognition (HTR) describes the task of transcribing handwritten text into digital texts. Compared to Optical Character Recognition (OCR), HTR is much more challenging and still an open problem.Recently, a transformer based framework named TrOCR was suggested in [1]. Goal The aim of this internship is to fine-tune existing HTR models in [1] … Continue reading Handwritten Text Recognition →
Supervisor: Fabian Hollaus The quality of photographs of single document pages or book pages is often limited by distortions. Dewarping techniques can be used in order to remove such distortions, i.e. rectify the document images. Goal The aim of this work is to implement a document image dewarping algorithm. The method should make use of … Continue reading Document Dewarping →
Status: available Supervisors: Michael Reiter, Matthias Wödlinger Why? The target dataset is the enormous digitised collection of historical printed publications at Austrian National Library. In particular, Austrian Books Online (digitised over the last years in a large-scale cooperation with Google) and Austrian Newspapers Online from 16th to 19th century, which rank among the most important … Continue reading Document image classification of historic book pages →
Problem Statement Status: vergebenSupervisor: Martin Kampel Das Theatermuseum Wien beherbergt ca. eine halbe Million Fotos. Davon wurden rund 80.000 digitalisiert und sind vor 1940 entstanden. Von vielen Dargestellten wissen wir die Namen. Aber von ebenso vielen haben wir keine Informationen. Vor allem Statist*innen, Tänzer*innen und Nebenschauspieler*innen sind heute oft schwer zu eruieren.Die Aufgabe besteht nun … Continue reading GESICHTSERKENNUNG IN DER FOTOSAMMLUNG DES THEATERMUSEUMS WIEN →
Supervisors: Dominik Schörkhuber, Margrit Gelautz Distributed Deep Learning In this practicum, we aim to evaluate the feasibility of distributed deep learning for Computer Vision applications on the Vienna Scientific Cluster (VSC) (https://www.it.tuwien.ac.at/services/forschung/high-performance-computing/vsc-vienna-scientific-cluster). We are currently developing deep learning algorithms with steep requirements when it comes to computational resources for an ongoing research project in the … Continue reading Distributed Deep Learning →
Supervisor: Florian Kleber Start: as soon as possible Problem Statement Document Image Analysis (DIA) deals with the analysis and recognition of document images. Tasks range from skew estimation, layout analysis to Handwritten Text Recognition (HTR). Tables in documents contain structured information which can allow a deeper insight into specific data. Table Structure Recognition (TSR) deals … Continue reading Table Structure Recognition →
