Supervisors: Florian Kleber, Marco Peer Status: open The analysis of artistic styles is a well-established challenge in the Cultural Heritage field. Traditional artworks, such as statues and paintings, have been thoroughly studied, leading to standardized methods for identifying an artist’s style. However, historical violins present a unique case. While their main stylistic features are known, … Continue reading Stylistic Classification and Retrieval of Historical Violins and Violin Makers →

Supervisor: Martin Kampel Status: open Motivation The Coin Collection of the Kunsthistorisches Museum Wien (KHM) is one of the five largest and most important coin collections in the world. With some 600,000 objects from three millennia, it contains not only coins, but also paper money, medallions, orders, etc. For research the collection is currently organized … Continue reading Database for numismatic collection of KHM →

The CVL offers bachelor/master theses or student projects in the domain of deep-learning-based document analysis for papyri. Supervisor: Marco Peer Status: taken First Picture: Vesuvius Challenge Second Picture: Peer and Sablatnig, HIP’23 Third picture modified from FAU Erlangen Motivation Greek papyri, ancient documents made from a type of paper, offer valuable insights into the past. … Continue reading Deep Learning for Papyri →

Internship/Master Thesis Status: available Supervisors: Martin Kampel Problem Statement Care work in long-term care (LTC) is considered as a genuine human-centred activity, requiring empathy, emotional investment, physical encounters and intimate, trust-based relations between various care-givers and care-recipients. AI technologies are introduced in this professional field to assist care workers in their daily activities and provide an … Continue reading Bias and Explainability in Long Term Care (LTC) →

Master Practical Training Project Status: not available Supervisors: Irene Ballester, Martin Kampel Problem Statement This project aims to address the challenges associated with the expensive and time-consuming annotation of 3D data by exploring a self-supervised approach for the extraction of 4D spatio-temporal features from dynamic point cloud data. Specifically, the project investigates the prediction of the … Continue reading Self-Supervised 4D Point Cloud Feature Learning for Activity Recognition →

Master Practical Training Project/ Master’s thesis Status: available  taken (but if you would like to work on a similar topic, contact Irene Ballester) Supervisors: Irene Ballester, Martin Kampel Problem Statement Human Activity Recognition (HAR) in computer vision, a pivotal area for healthcare, security, and robotics, often relies on privacy-invading RGB cameras. To enhance HAR accuracy while … Continue reading Human Activity Recognition from Real-World Depth Images →

Status: available Supervisors: Martin Kampel   Abbildungen von Kunstgegenständen finden wir in Internetdatenbanken von Auktionshäusern, Kunstsammlern, Museen, aber auch in Sozialen Medien wie Facebook oder Instagram. Diese Abbildungen können professionell erstellt worden sein, oder durch eine Handyaufnahme eines Betrachters. Es kann sich um Kopien von Abbildungen handeln, oder von einer Darstellung originaler Kunst. Gemälde, chinesische … Continue reading A(RT)I – Finding and Recognizing Artwork →

Status: taken Supervisors: Marco Peer The digitization and preservation of historical documents rely on accurate transcription of handwritten text. However, historical documents often present unique challenges due to variations in writing styles and deteriorated conditions. This thesis should explore the concepts of writer identification and writer-specific style extraction within Handwritten Text Recognition (HTR) systems, focusing … Continue reading Writer Adaption for Handwritten Text Recognition of Historical Documents →

Status: taken Supervisors: Marco Peer, Florian Kleber Deep-learning-based methods for writer retrieval make use of sampling local characteristics of handwriting, for example using patches extracted at SIFT keypoint locations(see Figure 1), to learn discriminative features. To compute a global page descriptor of those local embeddings, state-of-the-art methods rely on fixed aggregation functions, e.g. sum/average pooling … Continue reading Learning Aggregation Functions for Writer Retrieval →