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: open Supervisor: Sebastian Zambanini Problem Statement Detecting and classifying objects in remote sensing images is a challenging task, since the objects’ optical signature is less discriminative  due to the small resolution. Common object detection methods in this area aim to find object locations by classifying local structures, while neglecting the larger surroundings of the … Continue reading Exploiting Context in Remote Sensing Object Detection →

Status: taken Supervisor: Sebastian Zambanini Problem Statement Shadows cast in remote sensing images complicate their analysis due to the low contrast in the poorly illuminated image regions. Shadow removal/compensation methods aim at automatically reducing shadow effects, either by a physical or learning-based  model. Goal The goal of this work is to explore and examine methods … Continue reading Shadow Removal in Satellite Images →

Status: taken Supervisor: Sebastian Zambanini Problem Statement Photo triage is the process of grouping similar photos of a large collection and finally selecting the most preferred one in each group. Especially the selection of “the” best photo of a group is challenging as the relevant image features are not obvious to identify and heavily scene-dependent. … Continue reading Automatic Triage of Photo Collections →

Status: open Supervisor: Sebastian Zambanini Problem Statement In the DeVisOr project, historical aerial images are registered to modern satellite images for the purpose of geo-referencing. However, feature based registrations only deliver coarse registration that needs to be refined for improved accuracy. Goal The goal of this work is to investigate the application of fine registration … Continue reading Fine Registration of Historical Aerial Images and Present-Day Satellite Images →

Status: available Supervisor: Sebastian Zambanini Problem Statement Learning local image descriptors by means of deep convolutional neural nets [1,2] has recently shown to produce stronger features than traditional hand-crafted ones such as SIFT [3]. However, these nets have been trained and evaluated on general scenarios of (wide-basline) object matching. For the DeVisOr project, matching historical … Continue reading Image Descriptor Learning for Matching Historical Aerial Images with Present-Day Satellite Images →