Status: available 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 →
Course description The lecture will cover advanced computer vision methods in depth: • Texture, Scenes, und Context • Local- and Multiscale Representations • Interest Points, Corners • Scene Emergent Features • Scene Recognition, Bag of Words, SIFT • Clustering, Pyramid Matching, Support Vector Machine • Deep Learning, CNNs • Perceptron, Linear Basis Function Models, RBF … Continue reading Grundlagen der Computer Vision →
Status: open Supervisor: Robert Sablatnig Carpacity is a young company from Vienna that has recently finished a research project with the Institute of Spatial Planning at TU Wien. They use traffic sensors and LED walls to change how road traffic is analysed and stimulated. Its mission is to accelerate the decarbonisation of how people move … Continue reading Car Occupants Counting from Near-Infrared Photos →
Status: taken Supervisor: Sebastian Zambanini Problem Statement The INDIGO project aims to document and analyze the graffiti along Vienna’s Donaukanal. However, indexing graffiti images by written text is a time-consuming task. Hence, a system which provides automatic guesses for written text would be highly beneficial. Goal The goal of this work is to explore and … Continue reading Text Recognition in Graffiti Images →
Status: taken Supervisor: Sebastian Zambanini Problem Statement The INDIGO project aims to document and analyze the graffiti along Vienna’s Donaukanal. One of the main problems faced is monitoring new graffiti. Instead of solely relying on Instagram and human memory, an automatic change detection between images from different time stamps can support the monitoring process. Goal … Continue reading Change Detection in Graffiti Images →
Status: taken Supervisor: Margrit Gelautz The Computer Vision Lab at TU Wien announces a research position for a Diploma/Master Student to work on the “SyntheticCabin” project starting in March 2023 (or later). We are looking for a highly motivated diploma/master student of Informatics (or related studies) with a background in computer vision and machine … Continue reading Diploma Thesis – Assisted/Automated Driving →
Supervisor: Michael Reiter, Florian Kleber Start: as soon as possible Problem Statement A herbarium is a collection of preserved plant specimens with information from the collector and additional data. The Herbarium of the Natural History Museum in Vienna (W) was established in 1807 and is now ranked amongst the top five botanical collections in the … Continue reading Automated Analysis of NHM Herbarium Collection →
The following topics are part of our collaboration with Cogvis Gmbh, For processing deep learning networks, Cogvis as well as CVL have powerful hardware available which can be used. All Master students get an internal company supervisor who is responsible for the topic and the guidance of the students. A renumeration and/working place is possible. … Continue reading Selected Topics in Active Assisted Living in collaboration with a company →
Problem Statement Object detection is a very important and still unsolved problem in object recognition. For example, the problem becomes challenging in aerial imaging and remote sensing as the scenes and the data differ significantly from the case usually considered in computer vision. The aim of this thesis is to study vehicle detection for a … Continue reading Vehicle Detection in a Sequence of SAT Images →
Supervisor: Michael Reiter Background Flow Cytometry is a laser-based technique to measure antigen expression levels of blood cells. It is used in research as well as in daily clinical routines for immunophenotyping and for monitoring residual numbers of cancer cells during chemotherapy. One patient’s sample contains approximately 50-300k cells (also called events) with up to … Continue reading Evaluating different transformer architectures for cell classification in Flow Cytometry Data →
