All posts by Daniel Helm

Daniel Helm

Publications Helm D., Jogl F., and Kampel M., “HISTORIAN: A Large-Scale Historical Film Dataset with Cinematographic Annotation”, 2022 IEEE International Conference on Image Processing (ICIP) – (accepted) Helm D., Kleber F., and Kampel M. (2022). Graph-based Shot Type Classification in Large Historical Film Archives. In Proceedings of the 17th International Joint Conference on Computer Vision, … Continue reading Daniel Helm

GESICHTSERKENNUNG IN DER FOTOSAMMLUNG DES THEATERMUSEUMS WIEN

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

Bee polls recognition and quantity estimation

Status: finishedSupervisor: Martin Kampel   Problem Statement In the project MIC-Cam a system for the automated detection of mites was developed. It monitors the bees by camera when departing and landing at the flight board of the bee hive to recognise attached mites in real time without restricting the bees. We apply innovative methods of digital … Continue reading Bee polls recognition and quantity estimation

Relation Detection: Image Similarity Measurement in Large Historical Archives

Status: available Supervisor: Daniel Helm, Martin Kampel Start: as soon as possible Problem Statement Automatic scene analysis is a crucial task for historians or film archivists in order to preserve and interpret the memories of human cultural history. Film archives include thousands of hours of digitized analog footage from the last 100 years up to … Continue reading Relation Detection: Image Similarity Measurement in Large Historical Archives

Historical Image Restoration: Deep learning-based versus traditional-based deformation strategies

Status: available Supervisor: Daniel Helm, Martin Kampel Start: as soon as possible Problem Statement Automatic video analysis in large historical film collections is a challenging task due to different aspects. One major challenge is related to the quality of historical films, e.g. over-, underexposures and blur. Furthermore, they include scratches, cracks or press cuts and … Continue reading Historical Image Restoration: Deep learning-based versus traditional-based deformation strategies

Camera Pose Estimation for Video Shot Analysis

Status: available Supervisor: Daniel Helm, Martin Kampel Start: as soon as possible Problem Statement Automatic video content analysis is needed in many different domains such as historical film preservation or film archiving. Due to the enormous quantity of available visual media data, automatic approaches shall be used in order to find specific content in videos … Continue reading Camera Pose Estimation for Video Shot Analysis

Camera Pose Estimation for Video Shot Analysis

Status: available Supervisor: Daniel Helm, Martin Kampel Start: as soon as possible Problem Statement Automatic video content analysis is needed in many different domains such as historical film preservation or film archiving. Due to the enormous quantity of available visual media data, automatic approaches shall be used in order to find specific content in videos … Continue reading Camera Pose Estimation for Video Shot Analysis

Shot Type Classification: A fundamental base for automatic video analysis

Status: availableSupervisor: Daniel Helm, Martin Kampel Start: as soon as possible Problem Statement Automatic video content analysis is needed in many different domains such as historical film preservation or film archiving. Due to the enormous quantity of available visual media data, automatic approaches shall be used in order to find specific content in videos or … Continue reading Shot Type Classification: A fundamental base for automatic video analysis